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Altunkaya S. Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis. Med Biol Eng Comput 2024:10.1007/s11517-024-03180-2. [PMID: 39126561 DOI: 10.1007/s11517-024-03180-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024]
Abstract
There is no effective fall risk screening tool for the elderly that can be integrated into clinical practice. Developing a system that can be easily used in primary care services is a current need. Current studies focus on the use of multiple sensors or activities to achieve higher accuracy. However, multiple sensors and activities reduce the availability of these systems. This study aims to develop a system to perform fall prediction for the elderly by using signals recorded from a single sensor during a short-term activity. A total of 168 features in the time and frequency domains were created using acceleration signals obtained from 71 elderly people. The features were weighted based on the ReliefF algorithm, and the artificial neural networks model was developed using the most important features. The best classification result was obtained using the 17 most important features of those weighted for K = 20 nearest neighbors. The highest accuracy was 82.2% (82.9% Sensitivity, 81.6% Specificity). The partially high accuracy obtained in our study shows that falling can be detected early with a sensor and a simple activity by determining the right features and can be easily applied in the assessment of the elderly during routine follow-ups.
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Affiliation(s)
- Sabri Altunkaya
- Department of Electrical and Electronics Engineering, Necmettin Erbakan University, Konya, Türkiye.
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Piergiovanni S, Terrier P. Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people. Sci Rep 2024; 14:15784. [PMID: 38982219 PMCID: PMC11233570 DOI: 10.1038/s41598-024-65662-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
This study investigates the effects of metronome walking on gait dynamics in older adults, focusing on long-range correlation structures and long-range attractor divergence (assessed by maximum Lyapunov exponents). Sixty older adults participated in indoor walking tests with and without metronome cues. Gait parameters were recorded using two triaxial accelerometers attached to the lumbar region and to the foot. We analyzed logarithmic divergence of lumbar acceleration using Rosenstein's algorithm and scaling exponents for stride intervals from foot accelerometers using detrended fluctuation analysis (DFA). Results indicated a concomitant reduction in long-term divergence exponents and scaling exponents during metronome walking, while short-term divergence remained largely unchanged. Furthermore, long-term divergence exponents and scaling exponents were significantly correlated. Reliability analysis revealed moderate intrasession consistency for long-term divergence exponents, but poor reliability for scaling exponents. Our results suggest that long-term divergence exponents could effectively replace scaling exponents for unsupervised gait quality assessment in older adults. This approach may improve the assessment of attentional involvement in gait control and enhance fall risk assessment.
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Affiliation(s)
- Sophia Piergiovanni
- Haute-Ecole Arc Santé, HES-SO University of Applied Sciences and Arts Western Switzerland, Espace de l'Europe 11, 2000, Neuchâtel, Switzerland
| | - Philippe Terrier
- Haute-Ecole Arc Santé, HES-SO University of Applied Sciences and Arts Western Switzerland, Espace de l'Europe 11, 2000, Neuchâtel, Switzerland.
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Nishiyama D, Arita S, Fukui D, Yamanaka M, Yamada H. Accurate fall risk classification in elderly using one gait cycle data and machine learning. Clin Biomech (Bristol, Avon) 2024; 115:106262. [PMID: 38744224 DOI: 10.1016/j.clinbiomech.2024.106262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. This challenge stems from individual variability and step-to-step fluctuations, making accurate classification difficult. METHODS We recruited 44 participants, equally divided into high and low fall-risk groups. A smartphone secured on their second sacral spinous process recorded data during indoor walking. Features were extracted at each gait cycle from a 6-dimensional time series (tri-axial angular velocity and tri-axial acceleration) and classified using the gradient boosting decision tree algorithm. FINDINGS Mean accuracy across five-fold cross-validation was 0.936. "Age" was the most influential individual feature, while features related to acceleration in the gait direction held the highest total relative importance when aggregated by axis (0.5365). INTERPRETATION Combining acceleration, angular velocity data, and the gradient boosting decision tree algorithm enabled accurate fall risk classification in the elderly, previously challenging due to lack of discernible features. We reveal the first-ever identification of three-dimensional pelvic motion characteristics during single gait cycles in the high-risk group. This novel method, requiring only one gait cycle, is valuable for individuals with physical limitations hindering repetitive or long-distance walking or for use in spaces with limited walking areas. Additionally, utilizing readily available smartphones instead of dedicated equipment has potential to improve gait analysis accessibility.
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Affiliation(s)
- Daisuke Nishiyama
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan.
| | - Satoshi Arita
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan
| | - Daisuke Fukui
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan
| | - Manabu Yamanaka
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan
| | - Hiroshi Yamada
- Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan
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Yenişehir S. Artificial intelligence based on falling in older people: A bibliometric analysis. Aging Med (Milton) 2024; 7:162-170. [PMID: 38725694 PMCID: PMC11077341 DOI: 10.1002/agm2.12302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 02/27/2024] [Accepted: 03/28/2024] [Indexed: 05/12/2024] Open
Abstract
Objectives This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective. Methods The Web of Science database was searched for titles of English-language articles containing the words "artificial intelligence," "deep learning," "machine learning," "natural language processing,", "neural artificial network," "fall," "geriatric," "elderly," "aging," "older," and "old age." An R-based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis. Results Thirty-seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was "Engineering Electrical Electronic" with seven articles. The most productive country was the United States, followed by China. The most common words were "injuries," "people," and "risk factors." Conclusion Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed.
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Affiliation(s)
- Semiha Yenişehir
- Faculty of Health Sciences, Department of Physiotherapy and RehabilitationMuş Alparslan UniversityMuşTurkey
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Hellec J, Colson SS, Jaafar A, Guérin O, Chorin F. A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups. SENSORS (BASEL, SWITZERLAND) 2024; 24:1427. [PMID: 38474963 DOI: 10.3390/s24051427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/17/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024]
Abstract
Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.
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Affiliation(s)
- Justine Hellec
- Université Côte d'Azur, LAMHESS, France
- Ellcie Healthy, 06600 Antibes, France
| | | | | | - Olivier Guérin
- Université Côte d'Azur, CHU, France
- Université Côte d'Azur, CNRS, INSERM, IRCAN, France
| | - Frédéric Chorin
- Université Côte d'Azur, LAMHESS, France
- Université Côte d'Azur, CHU, France
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Olsen S, Rashid U, Barbado D, Suresh P, Alder G, Khan Niazi I, Taylor D. The validity of smartphone-based spatiotemporal gait measurements during walking with and without head turns: Comparison with the GAITRite® system. J Biomech 2024; 162:111899. [PMID: 38128468 DOI: 10.1016/j.jbiomech.2023.111899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 11/26/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023]
Abstract
Smartphone accelerometry has potential to provide clinicians with specialized gait analysis not available in most clinical settings. The Gait&Balance Application (G&B App) uses smartphone accelerometry to assess spatiotemporal gait parameters under two conditions: walking looking straight ahead and walking with horizontal head turns. This study investigated the validity of G&B App gait parameters compared with the GAITRite® pressure-sensitive walkway. Healthy young and older adults (age range 21-85 years) attended a single session where a smartphone was secured over the lumbosacral junction. Data were collected concurrently with the app and GAITRite® systems as participants completed the two walking conditions. Spatiotemporal gait parameters for 54 participants were determined from both systems and agreement evaluated with partial Pearson's correlation coefficients and limits of agreement. The results demonstrated moderate to excellent validity for G&B App measures of step time (rp 0.97, 95 % CI [0.96, 0.98]), walking speed (rp 0.83 [0.78, 0.87]), and step length (rp 0.74, [0.66, 0.80]) when walking looking straight ahead, and results were comparable with head turns. The validity of walking speed and step length measures was influenced by sex and height. G&B App measures of step length variability, step time variability, step length asymmetry, and step time asymmetry had poor validity. The G&B App has potential to provide valid measures of unilateral and bilateral step time, unilateral and bilateral step length, and walking speed, under two walking conditions in healthy young and older adults. Further research should validate this tool in clinical conditions and optimise the algorithm for demographic characteristics.
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Affiliation(s)
- Sharon Olsen
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand.
| | - Usman Rashid
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand; Centre for Chiropractic Research, New Zealand College of Chiropractic, PO Box 113-044, Newmarket, Auckland 1149, New Zealand
| | - David Barbado
- Department of Sport Science, Sports Research Centre, Miguel Hernandez University of Elche, Avda. de la Universidad s/n, Elche 03202, Spain; Institute for Health and Biomedical Research (ISABIAL Foundation), Avda. Pintor Baeza, 12 HGUA, Alicante 03550, Spain
| | - Priyadharshini Suresh
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Gemma Alder
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Imran Khan Niazi
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand; Centre for Chiropractic Research, New Zealand College of Chiropractic, PO Box 113-044, Newmarket, Auckland 1149, New Zealand; Centre for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
| | - Denise Taylor
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
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Li KJ, Wong NLY, Law MC, Lam FMH, Wong HC, Chan TO, Wong KN, Zheng YP, Huang QY, Wong AYL, Kwok TCY, Ma CZH. Reliability, Validity, and Identification Ability of a Commercialized Waist-Attached Inertial Measurement Unit (IMU) Sensor-Based System in Fall Risk Assessment of Older People. BIOSENSORS 2023; 13:998. [PMID: 38131758 PMCID: PMC10742152 DOI: 10.3390/bios13120998] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/31/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023]
Abstract
Falls are a prevalent cause of injury among older people. While some wearable inertial measurement unit (IMU) sensor-based systems have been widely investigated for fall risk assessment, their reliability, validity, and identification ability in community-dwelling older people remain unclear. Therefore, this study evaluated the performance of a commercially available IMU sensor-based fall risk assessment system among 20 community-dwelling older recurrent fallers (with a history of ≥2 falls in the past 12 months) and 20 community-dwelling older non-fallers (no history of falls in the past 12 months), together with applying the clinical scale of the Mini-Balance Evaluation Systems Test (Mini-BESTest). The results show that the IMU sensor-based system exhibited a significant moderate to excellent test-retest reliability (ICC = 0.838, p < 0.001), an acceptable level of internal consistency reliability (Spearman's rho = 0.471, p = 0.002), an acceptable convergent validity (Cronbach's α = 0.712), and an area under the curve (AUC) value of 0.590 for the IMU sensor-based receiver-operating characteristic (ROC) curve. The findings suggest that while the evaluated IMU sensor-based system exhibited good reliability and acceptable validity, it might not be able to fully identify the recurrent fallers and non-fallers in a community-dwelling older population. Further system optimization is still needed.
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Affiliation(s)
- Ke-Jing Li
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
| | - Nicky Lok-Yi Wong
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
| | - Man-Ching Law
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China;
- Jockey Club Smart Ageing Hub, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Freddy Man-Hin Lam
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China;
| | - Hoi-Ching Wong
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
- Jockey Club Smart Ageing Hub, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Tsz-On Chan
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
- Jockey Club Smart Ageing Hub, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Kit-Naam Wong
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
- Jockey Club Smart Ageing Hub, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yong-Ping Zheng
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China;
- Jockey Club Smart Ageing Hub, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Qi-Yao Huang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong SAR, China;
| | - Arnold Yu-Lok Wong
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China;
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China;
| | - Timothy Chi-Yui Kwok
- Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China;
| | - Christina Zong-Hao Ma
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China; (K.-J.L.); (N.L.-Y.W.); (M.-C.L.); (H.-C.W.); (T.-O.C.); (K.-N.W.); (Y.-P.Z.)
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China;
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Álvarez MN, Rodríguez-Sánchez C, Huertas-Hoyas E, García-Villamil-Neira G, Espinoza-Cerda MT, Pérez-Delgado L, Reina-Robles E, Martin IB, Del-Ama AJ, Ruiz-Ruiz L, Jiménez-Ruiz AR. Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study. BMC Geriatr 2023; 23:737. [PMID: 37957597 PMCID: PMC10644581 DOI: 10.1186/s12877-023-04379-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/04/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND There are a lot of tools to use for fall assessment, but there is not yet one that predicts the risk of falls in the elderly. This study aims to evaluate the use of the G-STRIDE prototype in the analysis of fall risk, defining the cut-off points to predict the risk of falling and developing a predictive model that allows discriminating between subjects with and without fall risks and those at risk of future falls. METHODS An observational, multicenter case-control study was conducted with older people coming from two different public hospitals and three different nursing homes. We gathered clinical variables ( Short Physical Performance Battery (SPPB), Standardized Frailty Criteria, Speed 4 m walk, Falls Efficacy Scale-International (FES-I), Time-Up Go Test, and Global Deterioration Scale (GDS)) and measured gait kinematics using an inertial measure unit (IMU). We performed a logistic regression model using a training set of observations (70% of the participants) to predict the probability of falls. RESULTS A total of 163 participants were included, 86 people with gait and balance disorders or falls and 77 without falls; 67,8% were females, with a mean age of 82,63 ± 6,01 years. G-STRIDE made it possible to measure gait parameters under normal living conditions. There are 46 cut-off values of conventional clinical parameters and those estimated with the G-STRIDE solution. A logistic regression mixed model, with four conventional and 2 kinematic variables allows us to identify people at risk of falls showing good predictive value with AUC of 77,6% (sensitivity 0,773 y specificity 0,780). In addition, we could predict the fallers in the test group (30% observations not in the model) with similar performance to conventional methods. CONCLUSIONS The G-STRIDE IMU device allows to predict the risk of falls using a mixed model with an accuracy of 0,776 with similar performance to conventional model. This approach allows better precision, low cost and less infrastructures for an early intervention and prevention of future falls.
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Affiliation(s)
- Marta Neira Álvarez
- Department of Geriatrics, Foundation for Research and Biomedical Innovation of the Infanta Sofía University Hospital and Henares University Hospital, (FIIB HUIS HHEN), European University, 28702, Madrid, Spain
| | | | - Elisabet Huertas-Hoyas
- Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Rey Juan Carlos University, 28922, Madrid, Spain.
| | | | - Maria Teresa Espinoza-Cerda
- Department of Geriatrics, Foundation for Research and Biomedical Innovation of the Getafe University Hospital, 28905, Madrid, Spain
| | - Laura Pérez-Delgado
- Department of Geriatrics, Gastón Baquero Residential Centre, 28108, Alcobendas Madrid, Spain
| | - Elena Reina-Robles
- Department of Geriatrics, Torrelaguna Residential Centre, 28180, Torrelaguna, Spain
| | | | - Antonio J Del-Ama
- School of Experimental Sciences and Technology, Rey Juan Carlos University, 28933, Madrid, Spain
| | - Luisa Ruiz-Ruiz
- Centre for Automation and Robotics, UPM-CSIC, 28500, Madrid, Spain
- Universidad Alcalá (UAH), Madrid, Spain
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Noamani A, Riahi N, Vette AH, Rouhani H. Clinical Static Balance Assessment: A Narrative Review of Traditional and IMU-Based Posturography in Older Adults and Individuals with Incomplete Spinal Cord Injury. SENSORS (BASEL, SWITZERLAND) 2023; 23:8881. [PMID: 37960580 PMCID: PMC10650039 DOI: 10.3390/s23218881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual's quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms of the balance deficiencies, and developing targeted interventions to improve standing balance and prevent falls. This review paper first explores the methods to quantify standing balance. Then, it reviews traditional posturography and recent advancements in using wearable inertial measurement units (IMUs) to assess static balance in two populations: older adults and those with incomplete spinal cord injury (iSCI). The inclusion of these two groups is supported by their large representation among individuals with balance impairments. Also, each group exhibits distinct aspects in balance assessment due to diverse underlying causes associated with aging and neurological impairment. Given the high vulnerability of both demographics to balance impairments and falls, the significance of targeted interventions to improve standing balance and mitigate fall risk becomes apparent. Overall, this review highlights the importance of static balance assessment and the potential of emerging methods and technologies to improve our understanding of postural control in different populations.
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Affiliation(s)
- Alireza Noamani
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
| | - Negar Riahi
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
| | - Albert H. Vette
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
- Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB T5G 0B7, Canada
| | - Hossein Rouhani
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
- Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB T5G 0B7, Canada
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10
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Bohlke K, Redfern MS, Rosso AL, Sejdic E. Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review. Aging Clin Exp Res 2023; 35:1991-2007. [PMID: 37526887 PMCID: PMC10881067 DOI: 10.1007/s40520-023-02503-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/11/2023] [Indexed: 08/02/2023]
Abstract
Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.
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Affiliation(s)
- Kayla Bohlke
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Mark S Redfern
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Andrea L Rosso
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Ervin Sejdic
- The Edward S. Rogers Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, M5S, Canada.
- North York General Hospital, 4001 Leslie St., Toronto, ON, M2K, Canada.
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11
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Zhao Y, Yu L, Fan X, Pang MYC, Tsui KL, Wang H. Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:8008. [PMID: 37766060 PMCID: PMC10535689 DOI: 10.3390/s23188008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.
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Affiliation(s)
- Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China;
| | - Lisha Yu
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518000, China;
| | - Marco Y. C. Pang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Kwok-Leung Tsui
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA;
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
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12
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Lee CH, Mendoza T, Huang CH, Sun TL. Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data. Healthcare (Basel) 2023; 11:1938. [PMID: 37444772 PMCID: PMC10341555 DOI: 10.3390/healthcare11131938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
Fall-risk assessment studies generally focus on identifying characteristics that affect postural balance in a specific group of subjects. However, falls affect a multitude of individuals. Among the groups with the most recurrent fallers are the community-dwelling elderly and stroke survivors. Thus, this study focuses on identifying a set of features that can explain fall risk for these two groups of subjects. Sixty-five community dwelling elderly (forty-nine female, sixteen male) and thirty-five stroke-survivors (twenty-two male, thirteen male) participated in our study. With the use of an inertial sensor, some features are extracted from the acceleration data of a Timed Up and Go (TUG) test performed by both groups of individuals. A short-form berg balance scale (SFBBS) score and the TUG test score were used for labeling the data. With the use of a 100-fold cross-validation approach, Relief-F and Extra Trees Classifier algorithms were used to extract sets of the top 5, 10, 15, 20, 25, and 30 features. Random Forest classifiers were trained for each set of features. The best models were selected, and the repeated features for each group of subjects were analyzed and discussed. The results show that only the stand duration was an important feature for the prediction of fall risk across all clinical tests and both groups of individuals.
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Affiliation(s)
- Chia-Hsuan Lee
- Department of Data Science, Soochow University, No. 70, Linxi Road, Shilin District, Taipei 111, Taiwan;
| | - Tomas Mendoza
- Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan 320, Taiwan;
| | - Chien-Hua Huang
- Department of Eldercare, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan;
| | - Tien-Lung Sun
- Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan 320, Taiwan;
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13
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Chen Q, Diao Y, Wang Y, Chen Y, Ning Y, Li G, Zhao G. A Precise Hip Protection System with Multi-scale Fall Warning Algorithm Based on Offset Displacement. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38082866 DOI: 10.1109/embc40787.2023.10339954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Falls occur frequently in daily life and the damage to the body is irreversible. Therefore, it is crucial to implement timely and effective warning and protection systems for falls to minimize the damage caused by falls. Currently, the fall warning algorithm has shortcomings such as low recognition rates for falls and fall-risk movements and insufficient lead-time, the time before the subject impacts the floor, making it difficult for falling protection devices to function effectively. In this study, a multi-scale falls warning algorithm based on offset displacement is built, and a hip protection system is designed. The performance of the algorithm and the system is validated using 150 falling and 500 fall-risk actions from 10 volunteers. The results showed that the recognition accuracy for falling actions is 98.7% and the recognition accuracy for fall-risk actions is 99.4%, with an average lead-time of 402ms. The protection rate for falling movements reached 98.7%. This proposed algorithm and hip protection system have the potential to be applied in elderly communities, hospitals, and homes to reduce the damage caused by falls.Clinical Relevance- This study provides important reference for clinicians in analyzing fall behaviors to patients at risk of falls in clinical settings, offering valuable technical support for ensuring the safety of patients in danger of falling. It also contributes to further promoting the development of falling-prevention medical devices.
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14
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Ortega-Bastidas P, Gómez B, Aqueveque P, Luarte-Martínez S, Cano-de-la-Cuerda R. Instrumented Timed Up and Go Test (iTUG)-More Than Assessing Time to Predict Falls: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3426. [PMID: 37050485 PMCID: PMC10098780 DOI: 10.3390/s23073426] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 06/19/2023]
Abstract
The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test's predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson's disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings.
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Affiliation(s)
- Paulina Ortega-Bastidas
- Health Sciences PhD Programme, International Doctoral School, Universidad Rey Juan Carlos, 28922 Madrid, Spain
- Kinesiology Department, Faculty of Medicine, Universidad de Concepción, Concepción, 151 Janequeo St., Concepcion 4030000, Chile
| | - Britam Gómez
- Biomedical Engineering, Faculty of Engineering, Universidad de Santiago de Chile, Libertador Bernardo O’Higgins Av., Santiago 9170022, Chile
| | - Pablo Aqueveque
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile
| | - Soledad Luarte-Martínez
- Kinesiology Department, Faculty of Medicine, Universidad de Concepción, Concepción, 151 Janequeo St., Concepcion 4030000, Chile
| | - Roberto Cano-de-la-Cuerda
- Physiotherapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Universidad Rey Juan Carlos, 28922 Madrid, Spain
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15
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Dubbeldam R, Lee YY, Pennone J, Mochizuki L, Le Mouel C. Systematic review of candidate prognostic factors for falling in older adults identified from motion analysis of challenging walking tasks. Eur Rev Aging Phys Act 2023; 20:2. [PMID: 36765288 PMCID: PMC9921041 DOI: 10.1186/s11556-023-00312-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
The objective of this systematic review is to identify motion analysis parameters measured during challenging walking tasks which can predict fall risk in the older population. Numerous studies have attempted to predict fall risk from the motion analysis of standing balance or steady walking. However, most falls do not occur during steady gait but occur due to challenging centre of mass displacements or environmental hazards resulting in slipping, tripping or falls on stairs. We conducted a systematic review of motion analysis parameters during stair climbing, perturbed walking and obstacle crossing, predictive of fall risk in healthy older adults. We searched the databases of Pubmed, Scopus and IEEEexplore.A total of 78 articles were included, of which 62 simply compared a group of younger to a group of older adults. Importantly, the differences found between younger and older adults did not match those found between older adults at higher and lower risk of falls. Two prospective and six retrospective fall history studies were included. The other eight studies compared two groups of older adults with higher or lower risk based on mental or physical performance, functional decline, unsteadiness complaints or task performance. A wide range of parameters were reported, including outcomes related to success, timing, foot and step, centre of mass, force plates, dynamic stability, joints and segments. Due to the large variety in parameter assessment methods, a meta-analysis was not possible. Despite the range of parameters assessed, only a few candidate prognostic factors could be identified: older adults with a retrospective fall history demonstrated a significant larger step length variability, larger step time variability, and prolonged anticipatory postural adjustments in obstacle crossing compared to older adults without a fall history. Older adults who fell during a tripping perturbation had a larger angular momentum than those who did not fall. Lastly, in an obstacle course, reduced gait flexibility (i.e., change in stepping pattern relative to unobstructed walking) was a prognostic factor for falling in daily life. We provided recommendations for future fall risk assessment in terms of study design.In conclusion, studies comparing older to younger adults cannot be used to explore relationships between fall risk and motion analysis parameters. Even when comparing two older adult populations, it is necessary to measure fall history to identify fall risk prognostic factors.
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Affiliation(s)
- Rosemary Dubbeldam
- Department of Movement Science, Institute of Sport and Exercise Science, University of Münster, Münster, Germany.
| | - Yu Yuan Lee
- grid.5949.10000 0001 2172 9288Department of Movement Science, Institute of Sport and Exercise Science, University of Münster, Münster, Germany
| | - Juliana Pennone
- grid.11899.380000 0004 1937 0722School of Arts, Sciences, and Humanities, University of São Paulo and School of Medicine, University of São Paulo, Sao Paulo, Brazil
| | - Luis Mochizuki
- grid.11899.380000 0004 1937 0722School of Arts, Sciences, and Humanities, University of São Paulo and School of Medicine, University of São Paulo, Sao Paulo, Brazil
| | - Charlotte Le Mouel
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
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16
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Ehn M, Kristoffersson A. Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness. SENSORS (BASEL, SWITZERLAND) 2023; 23:1904. [PMID: 36850500 PMCID: PMC9958653 DOI: 10.3390/s23041904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.
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17
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Bargiotas I, Wang D, Mantilla J, Quijoux F, Moreau A, Vidal C, Barrois R, Nicolai A, Audiffren J, Labourdette C, Bertin-Hugaul F, Oudre L, Buffat S, Yelnik A, Ricard D, Vayatis N, Vidal PP. Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall. J Neurol 2023; 270:618-631. [PMID: 35817988 PMCID: PMC9886639 DOI: 10.1007/s00415-022-11251-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 02/03/2023]
Abstract
Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.
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Affiliation(s)
- Ioannis Bargiotas
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France. .,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.
| | - Danping Wang
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Juan Mantilla
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Flavien Quijoux
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,ORPEA Group, Puteaux, France
| | - Albane Moreau
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Catherine Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Otorhinolaryngology (ENT), AP-HP, Hôpital Universitaire Pitié Salpêtrière, Paris, 75013, France
| | - Remi Barrois
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Alice Nicolai
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Julien Audiffren
- Department of Neuroscience, University of Fribourg, Fribourg, Switzerland
| | - Christophe Labourdette
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | | | - Laurent Oudre
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Stephane Buffat
- Laboratoire d'accidentologie de biomécanique et du comportement des conducteurs, GIE Psa Renault Groupes, Nanterre, France
| | - Alain Yelnik
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Physical and Rehabilitation Medicine (PRM), AP- HP, GH St Louis, Lariboisière, F. Widal, Paris, 75010, France
| | - Damien Ricard
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Neurology, AP-HP, Hôpital d'Instruction des Armées de Percy, Service de Santé des Armées, Clamart, 92140, France.,École d'application du Val-de-Grâce, Service de Santé des Armée, Paris, France
| | - Nicolas Vayatis
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Pierre-Paul Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
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18
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Vera‐Remartínez EJ, Lázaro‐Monge R, Casado‐Hoces SV, Garcés‐Pina E, Molés‐Julio MP. Validity and reliability of an android device for the assessment of fall risk in older adult inmates. Nurs Open 2022; 10:2904-2911. [PMID: 36464813 PMCID: PMC10077384 DOI: 10.1002/nop2.1532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/31/2022] [Accepted: 11/20/2022] [Indexed: 12/12/2022] Open
Abstract
AIM To validate the Android device, FallSkip, as a tool to assess the fallers in older adult inmates. DESIGN A cross-sectional descriptive and analytical study. METHODS For the validation of the FallSkip, the diagnostic criterion used was the risk of having suffered a fall during the last year. RESULTS The results for the FallSkip tool were as follows: sensitivity 60.7%; specificity 83.0%; positive predictive value 65.4%; negative predictive value 80.0%; accuracy 75.3%. In total, 32.1% of participants were found to be at high risk of falls, 23.5% were at mild risk and 7.4% were found to have no risk. CONCLUSION The FallSkip device is shown to be a very suitable tool for fall risk assessment. The sample studied presented a statistically significant percentage of fall risk, which made it necessary to carry out interventions through physical activities to improve balance and stability.
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19
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Duarte MB, da Costa Moraes AA, Ferreira EV, da Silva Almeida GC, da Rocha Santos EG, Pinto GHL, de Oliveira PR, Amorim CF, Dos Santos Cabral A, Saunier G, Costa E Silva ADA, Belgamo A, Souza GDS, Callegari B. Validity and reliability of a smartphone-based assessment for anticipatory and compensatory postural adjustments during predictable perturbations. Gait Posture 2022; 96:9-17. [PMID: 35533431 DOI: 10.1016/j.gaitpost.2022.05.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 04/26/2022] [Accepted: 05/02/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Postural adjustments involve displacements of the center of mass (COM), controlled by the central nervous system (CNS), to maintain equilibrium whilst standing. Postural adjustments can be anticipatory (APAs) or compensatory (CPAs), and are triggered to counteract predictable perturbations. RESEARCH QUESTION Is the new smartphone application, Momentum, a valid and reliable tool for the assessment of body balance, by measuring APAs and CPAs using accelerometer readings? METHODS 20 young adults were exposed to external predictable perturbations induced at the shoulder level, whilst standing. COM linear acceleration was recorded by Momentum (extracting data from a smartphone's accelerometer) and a 3D motion capture system. RESULTS The key results demonstrated a very high, significant correlation (r ≥ 0.7, p < 0.05) between the two device settings in the APA parameters, which obtained r = 0.65, denoting a high correlation. Considering the reliability, variables that are compensatory in nature are presented on a scale of good to excellent in measurement methods, kinematics, and Momentum. However, the anticipatory variables presented excellent reliability only for the kinematics. SIGNIFICANCE These experiments show that Momentum is a valid method for measuring COM acceleration under predictable perturbations and is reliable for compensatory events.
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Affiliation(s)
- Manuela Brito Duarte
- Laboratório de Estudos da Motricidade Humana, Av. Generalíssimo deodoro 01, Belém 66073-00, PA, Brazil.
| | | | - Eduardo Veloso Ferreira
- Laboratório de Estudos da Motricidade Humana, Av. Generalíssimo deodoro 01, Belém 66073-00, PA, Brazil.
| | | | - Enzo Gabriel da Rocha Santos
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, R. Augusto Corrêa, 01, Belém 66093-020, PA, Brazil
| | - Gustavo Henrique Lima Pinto
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, R. Augusto Corrêa, 01, Belém 66093-020, PA, Brazil
| | - Paulo Rui de Oliveira
- Doctoral and Masters Program in Physical Therapy, UNICID, 448/475 Cesário Galeno St., São Paulo, SP, Brazil.
| | - César Ferreira Amorim
- Doctoral and Masters Program in Physical Therapy, UNICID, 448/475 Cesário Galeno St., São Paulo, SP, Brazil; Département des Sciences de la Santé, Programme de physiothérapie de l'université McGill offert en extension à l'UQAC, Saguenay, Québec, Canada; Physical Therapy and Neuroscience Departments, Wertheims' Colleges of Nursing and Health Sciences and Medicine, Florida International University (FIU), Miami, FL, United States
| | - André Dos Santos Cabral
- Centro de Ciências Biológicas e da Saúde, Universidade do Estado do Pará, Tv. Perebebuí, 2623 - Marco, Belém, PA 66087-662, Brazil.
| | - Ghislain Saunier
- Laboratório de Cognição Motora, Departamento de Anatomia, Universidade Federal do Pará, Rua Augusto Corrêa 01, Belém 66075-110, PA, Brazil.
| | - Anselmo de Athayde Costa E Silva
- Programa de Pós Graduação em Ciências do Movimento, Universidade Federal do Pará, Av. Generalíssimo deodoro 01, Belém 66073-00, PA, Brazil.
| | - Anderson Belgamo
- Departamento de Ciência da Computação, Instituto Federal de São Paulo, Piracicaba, Brazil.
| | - Givago da Silva Souza
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Rua Augusto Corrêa 01, Belém 66075-110, PA, Brazil.
| | - Bianca Callegari
- Laboratório de Estudos da Motricidade Humana, Av. Generalíssimo deodoro 01, Belém 66073-00, PA, Brazil; Programa de Pós Graduação em Ciências do Movimento, Universidade Federal do Pará, Av. Generalíssimo deodoro 01, Belém 66073-00, PA, Brazil.
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20
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Kelly D, Condell J, Gillespie J, Munoz Esquivel K, Barton J, Tedesco S, Nordstrom A, Åkerlund Larsson M, Alamäki A. Improved screening of fall risk using free-living based accelerometer data. J Biomed Inform 2022; 131:104116. [PMID: 35690351 DOI: 10.1016/j.jbi.2022.104116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 04/07/2022] [Accepted: 06/05/2022] [Indexed: 11/19/2022]
Abstract
Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.
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Affiliation(s)
- D Kelly
- Ulster University, Northern Ireland, United Kingdom.
| | - J Condell
- Ulster University, Northern Ireland, United Kingdom
| | - J Gillespie
- Ulster University, Northern Ireland, United Kingdom
| | | | - J Barton
- Tyndall National Institute, University College Cork, Ireland
| | - S Tedesco
- Tyndall National Institute, University College Cork, Ireland
| | | | | | - A Alamäki
- Karelia University of Applied Sciences, Finland
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Dasgupta P, Frisch A, Huber J, Sejdic E, Suffoletto B. Predicting falls within 3 months of emergency department discharge among community-dwelling older adults using self-report tools versus a brief functional assessment. Am J Emerg Med 2022; 53:245-249. [PMID: 35085878 PMCID: PMC9231635 DOI: 10.1016/j.ajem.2021.12.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/23/2021] [Accepted: 12/31/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Identifying older adults with risk for falls prior to discharge home from the Emergency Department (ED) could help direct fall prevention interventions, yet ED-based tools to assist risk stratification are under-developed. The aim of this study was to assess the performance of self-report and functional assessments to predict falls in the 3 months post-ED discharge for older adults. METHODS A prospective cohort of community-dwelling adults age 60 years and older were recruited from one urban ED (N = 134). Participants completed: a single item screen for mobility (SIS-M), the 12-item Stay Independent Questionnaire (SIQ-12), and the Timed Up and Go test (TUG). Falls were defined through self-report of any fall at 1- and 3-months and medical record review for fall-related injury 3-months post-discharge. We developed a hybrid-convolutional recurrent neural network (HCRNN) model of gait and balance characteristics using truncal 3-axis accelerometry collected during the TUG. Internal validation was conducted using bootstrap resampling with 1000 iterations for SIS-M, FRQ, and GUG and leave-one-out for the HCRNN. We compared performance of M-SIS, FRQ, TUG time, and HCRNN by calculating the area under the receiver operating characteristic area under the curves (AUCs). RESULTS 14 (10.4%) of participants met our primary outcome of a fall or fall-related injury within 3-months. The SIS-M had an AUC of 0.42 [95% confidence interval (CI) 0.19-0.65]. The SIQ-12 score had an AUC of 0.64 [95% confidence interval (CI) 0.49-0.80]. The TUG had an AUC of 0.48 (95% CI 0.29-0.68). The HCRNN model using generated accelerometer features collected during the TUG had an AUC of 0.99 (95% CI 0.98-1.00). CONCLUSION We found that self-report and functional assessments lack sufficient accuracy to be used in isolation in the ED. A neural network model using accelerometer features could be a promising modality but research is needed to externally validate these findings.
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Affiliation(s)
- Pritika Dasgupta
- Epidemiology Data Center, Graduate School of Public Health, University of Pittsburgh
| | - Adam Frisch
- Department of Emergency Medicine, School of Medicine, University of Pittsburgh
| | - James Huber
- School of Medicine, West Virginia University
| | - Ervin Sejdic
- Department of Engineering, University of Toronto
| | - Brian Suffoletto
- Department of Emergency Medicine, School of Medicine, Stanford University, USA.
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22
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Ferreira RN, Ribeiro NF, Santos CP. Fall Risk Assessment Using Wearable Sensors: A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:984. [PMID: 35161731 PMCID: PMC8838304 DOI: 10.3390/s22030984] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 05/07/2023]
Abstract
Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.
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Affiliation(s)
- Rafael N. Ferreira
- Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimaraes, Portugal; (R.N.F.); (N.F.R.)
- LABBELS—Associate Laboratory, 4710-057 Braga, Portugal
- LABBELS—Associate Laboratory, 4800-058 Guimaraes, Portugal
| | - Nuno Ferrete Ribeiro
- Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimaraes, Portugal; (R.N.F.); (N.F.R.)
- LABBELS—Associate Laboratory, 4710-057 Braga, Portugal
- LABBELS—Associate Laboratory, 4800-058 Guimaraes, Portugal
| | - Cristina P. Santos
- Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimaraes, Portugal; (R.N.F.); (N.F.R.)
- LABBELS—Associate Laboratory, 4710-057 Braga, Portugal
- LABBELS—Associate Laboratory, 4800-058 Guimaraes, Portugal
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23
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Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor. SENSORS 2022; 22:s22020412. [PMID: 35062375 PMCID: PMC8778464 DOI: 10.3390/s22020412] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/23/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023]
Abstract
Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
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Yhdego H, Li J, Paolini C, Audette M. Wearable Sensor Gait Analysis of Fall Detection using Attention Network. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2021; 2021:3137-3141. [PMID: 37223490 PMCID: PMC10205066 DOI: 10.1109/bibm52615.2021.9669795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding- founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.
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Affiliation(s)
- Haben Yhdego
- Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, USA
| | - Jiang Li
- Electrical and Computer Engineering, Old Dominion University, Norfolk, USA
| | - Christopher Paolini
- Electrical and Computer Engineering, San Diego State University, San Diego, California
| | - Michel Audette
- Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, USA
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Warrington DJ, Shortis EJ, Whittaker PJ. Are wearable devices effective for preventing and detecting falls: an umbrella review (a review of systematic reviews). BMC Public Health 2021; 21:2091. [PMID: 34775947 PMCID: PMC8591794 DOI: 10.1186/s12889-021-12169-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 10/26/2021] [Indexed: 01/08/2023] Open
Abstract
Background Falls are a common and serious health issue facing the global population, causing an estimated 646,000 deaths per year globally. Wearable devices typically combine accelerometers, gyroscopes and even barometers; using the data collected and inputting this into an algorithm that decides whether a fall has occurred. The purpose of this umbrella review was to provide a comprehensive overview of the systematic reviews on the effectiveness of wearable electronic devices for falls detection in adults. Methods MEDLINE, Embase, Cochrane Database of Systematic Reviews (CDSR), and CINAHL, were searched from their inceptions until April 2019 for systematic reviews that assessed the accuracy of wearable technology in the detection of falls. Results Seven systematic reviews were included in this review. Due to heterogeneity between the included systematic reviews in their methods and their reporting of results, a meta-analysis could not be performed. Most devices tested used accelerometers, often in combination with gyroscopes. Three systematic reviews reported an average sensitivity of 93.1% or greater and an average specificity of 86.4% or greater for the detection of falls. Placing sensors on the trunk, foot or leg appears to provide the highest accuracy for falls detection, with multiple sensors increasing the accuracy, specificity, and sensitivity of these devices. Conclusions This review demonstrated that wearable device technology offers a low-cost and accurate way to effectively detect falls and summon for help. There are significant differences in the effectiveness of these devices depending on the type of device and its placement. Further high-quality research is needed to confirm the accuracy of these devices in frail older people in real-world settings.
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Affiliation(s)
- Daniel Joseph Warrington
- Division of Population Health, Health Services Research & Primary Care, Faculty of Biology, Medicine and Health, University of Manchester, Room 2.545, Stopford Building, Oxford Road, Manchester, M13 9PT, UK.
| | - Elizabeth Jane Shortis
- Division of Population Health, Health Services Research & Primary Care, Faculty of Biology, Medicine and Health, University of Manchester, Room 2.545, Stopford Building, Oxford Road, Manchester, M13 9PT, UK
| | - Paula Jane Whittaker
- Division of Population Health, Health Services Research & Primary Care, Faculty of Biology, Medicine and Health, University of Manchester, Room 2.545, Stopford Building, Oxford Road, Manchester, M13 9PT, UK
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26
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Ruiz-Ruiz L, Jimenez AR, Garcia-Villamil G, Seco F. Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters. SENSORS 2021; 21:s21206918. [PMID: 34696131 PMCID: PMC8538337 DOI: 10.3390/s21206918] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/08/2021] [Accepted: 10/14/2021] [Indexed: 12/15/2022]
Abstract
In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related to these pathologies and to make an early diagnosis. Inertial Measurement Units (IMUs) are wearables that, due to their accuracy, portability, and low price, are an excellent option to analyze human gait parameters in health-monitoring applications. Many relevant gait parameters (e.g., step time, walking speed) are used to assess motor, or even cognitive, health problems in the elderly, but we perceived that there is not a full consensus on which parameters are the most significant to estimate the risk of fall and the frailty state. In this work, we analyzed the different IMU-based gait parameters proposed in the literature to assess frailty state (robust, prefrail, or frail) or fall risk. The aim was to collect the most significant gait parameters, measured from inertial sensors, able to discriminate between patient groups and to highlight those parameters that are not relevant or for which there is controversy among the examined works. For this purpose, a literature review of the studies published in recent years was carried out; apart from 10 previous relevant reviews using inertial and other sensing technologies, a total of 22 specific studies giving statistical significance values were analyzed. The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant. Although these results are important, the total number of works found was limited and most of them performed the significance statistics on subsets of all possible gait parameters; this fact highlights the need for new frailty studies using a more complete set of gait parameters.
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27
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Cortés OL, Piñeros H, Aya PA, Sarmiento J, Arévalo I. Systematic review and meta-analysis of clinical trials: In-hospital use of sensors for prevention of falls. Medicine (Baltimore) 2021; 100:e27467. [PMID: 34731123 PMCID: PMC8519232 DOI: 10.1097/md.0000000000027467] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 09/18/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Intra-hospital falls have become an important public health problem globally. The use of movement sensors with alarms has been studied as elements with predictive capacity for falls at hospital level. However, in spite of their use in some hospitals throughout the world, evidence is lacking about their effectiveness in reducing intra-hospital falls. Therefore, this study aims to develop a systematic review and meta-analysis of existing scientific literature exploring the impact of using sensors for fall prevention in hospitalized adults and the elderly population. METHODS We explored literature based on clinical trials in Spanish, English, and Portuguese, assessing the impact of devices used for hospital fall prevention in adult and elderly populations. The search included databases such as IEEE Xplore, the Cochrane Library, Scopus, PubMed, MEDLINE, and Science Direct databases. The critical appraisal was performed independently by two researchers. Methodological quality was assessed based on the ratings of individual biases. We performed the sum of the results, generating an estimation of the grouped effect (Relative Risk, 95% CI) for the outcome first fall for each patient. We assessed heterogeneity and publication bias. The study followed PRISMA guidelines. RESULTS Results were assessed in three randomized controlled clinical trials, including 29,691 patients. A total of 351 (3%) patients fell among 11,769 patients assigned to the intervention group, compared with 426 (2.4%) patients who fell among 17,922 patients assigned to the control group (general estimation RR 1.20, 95% CI 1.04, 1.37, P = .02, I2 = 0%; Moderate GRADE). CONCLUSION Our results show an increase of 19% in falls among elderly patients who are users of sensors located in their bed, bed-chair, or chair among their hospitalizations. Other types of sensors such as wearable sensors can be explored as coadjutants for fall prevention care in hospitals.
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Affiliation(s)
- Olga L. Cortés
- Department of Research and Department of Nursing. Fundación Cardioinfantil - Instituto de Cardiología, Bogotá, Colombia
| | - Hillary Piñeros
- Biomedical Engineering student, Faculty of Biomedical Engineering. Universidad del Rosario - Escuela Colombiana de Ingeniería Julio Garavito, Bogotá, Colombia
| | - Pedro Antonio Aya
- Biomedical Engineer, MSC. Faculty of Biomedical Engineering. Universidad del Rosario - Escuela Colombiana de Ingeniería Julio Garavito, Bogotá, Colombia
| | - Jefferson Sarmiento
- Electronic Engineer, Faculty of Biomedical Engineering. Universidad del Rosario - Escuela Colombiana de Ingeniería Julio Garavito, Bogotá, Colombia
| | - Indira Arévalo
- Nurse, Director of Nursing Department. Clínica Universidad de la Sabana, Chía, Cundinamarca, Colombia
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Pérez-Ros P, Sanchis-Aguado MA, Durá-Gil JV, Martínez-Arnau FM, Belda-Lois JM. FallSkip device is a useful tool for fall risk assessment in sarcopenic older community people. Int J Older People Nurs 2021; 17:e12431. [PMID: 34652070 DOI: 10.1111/opn.12431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 09/23/2021] [Accepted: 09/28/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE Fall prevention is a major health concern for the ageing population. Sarcopenia is considered a risk factor for falls. Some instruments, such as Time Up and Go (TUG), are used for screening risk. The use of sensors has also been shown to be a viable tool that can provide accurate, cost-effective, and easy to manage assessment of fall risk. One novel sensor for assessing fall risk in older people is the Fallskip device. The present study evaluates the performance of the FallSkip device against the TUG method in fall risk screening and assesses its measurement properties in sarcopenic older people. METHODS A cross-sectional study was made in a sample of community-dwelling sarcopenic and non-sarcopenic older people aged 70 years or over. RESULTS The study sample consisted of 34 older people with a mean age of 77.03 (6.58) years, of which 79.4% (n = 27) were females, and 41.2% (n = 14) were sarcopenic. The Pearson correlation coefficient between TUG time and FallSkip time was 0.70 (p < 0.001). The sarcopenic individuals took longer in performing both TUG and FallSkip. They also presented poorer reaction time, gait and sit-to-stand - though no statistically significant differences were observed. The results in terms of feasibility, acceptability, reliability and validity in sarcopenic older people with FallSkip were acceptable. CONCLUSIONS The FallSkip device has suitable metric properties for the assessment of fall risk in sarcopenic community-dwelling older people. FallSkip analyses more parameters than TUG in assessing fall risk and has greater discriminatory power in evaluating the risk of falls.
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Affiliation(s)
- Pilar Pérez-Ros
- Department of Nursing, University of Valencia, Valencia, Spain.,Frailty and Cognitive Impairment Organized Group (FROG), University of Valencia, Valencia, Spain
| | | | - Juan V Durá-Gil
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain
| | - Francisco M Martínez-Arnau
- Frailty and Cognitive Impairment Organized Group (FROG), University of Valencia, Valencia, Spain.,Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Juan M Belda-Lois
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain
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Mendoza T, Lee CH, Huang CH, Sun TL. Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test. SENSORS (BASEL, SWITZERLAND) 2021; 21:5930. [PMID: 34502821 PMCID: PMC8434667 DOI: 10.3390/s21175930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 01/28/2023]
Abstract
Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.
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Affiliation(s)
- Tomas Mendoza
- Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan 320, Taiwan;
| | - Chia-Hsuan Lee
- Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Road, Da’an District, Taipei 106, Taiwan;
| | - Chien-Hua Huang
- Department of Eldercare, Central Taiwan University of Science and Technology, Taipei 106, Taiwan;
| | - Tien-Lung Sun
- Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan 320, Taiwan;
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Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review. SENSORS 2021; 21:s21175863. [PMID: 34502755 PMCID: PMC8434325 DOI: 10.3390/s21175863] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/11/2021] [Accepted: 08/27/2021] [Indexed: 12/30/2022]
Abstract
Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.
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Gaspar AGM, Escada P, Lapão LV. How Can We Develop an Efficient eHealth Service for Provision of Care for Elderly People with Balance Disorders and Risk of Falling? A Mixed Methods Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:7410. [PMID: 34299861 PMCID: PMC8307396 DOI: 10.3390/ijerph18147410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 12/31/2022]
Abstract
This study aimed to identify relevant topics for the development of an efficient eHealth service for elderly people with balance disorders and risk of falling, based on input from physicians providing healthcare to this patient group. In the quantitative part of the study, an open multiple-choice questionnaire was made available on the website of the Portuguese General Medical Council to assess the satisfaction with electronic medical records regarding clinical data available, the time needed to retrieve data and the usefulness of the data. Of the 118 participants, 55% were dissatisfied/very dissatisfied with data availability and 61% with the time spent to access and update data related to the focused patient group. Despite this negative experience, 76% considered future e-Health solutions as pertinent/very pertinent. Subsequently, these findings were further explored with eight semi-structured interviews. The physicians confirmed the reported dissatisfactions and pointed out the lack of comprehensive data and system interoperability as serious problems, causing inefficient health services with an overlap of emergency visits and uncoordinated diagnostics and treatment. In addition, they discussed the importance of camera and audio monitoring to add significant value. Our results indicate considerable potential for e-Health solutions, but substantial improvements are crucial to achieving such future solutions.
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Affiliation(s)
- Andréa Gomes Martins Gaspar
- Instituto de Higiene e Medicina Tropical (IHMT), Universidade NOVA de Lisboa (UNL), 1349-008 Lisbon, Portugal;
- Hospital Beatriz Ângelo, 2674-514 Lisbon, Portugal
| | | | - Luís Velez Lapão
- Instituto de Higiene e Medicina Tropical (IHMT), Universidade NOVA de Lisboa (UNL), 1349-008 Lisbon, Portugal;
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Bezold J, Krell-Roesch J, Eckert T, Jekauc D, Woll A. Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review. Eur Rev Aging Phys Act 2021; 18:15. [PMID: 34243722 PMCID: PMC8272315 DOI: 10.1186/s11556-021-00266-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 06/13/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design. RESULTS Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%). CONCLUSION Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment. TRIAL REGISTRATION This systematic review is registered in PROSPERO ( CRD42020171118 ).
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Affiliation(s)
- Jelena Bezold
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany
| | - Janina Krell-Roesch
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Tobias Eckert
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany
| | - Darko Jekauc
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany
| | - Alexander Woll
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany
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A Pilot Study to Validate a Wearable Inertial Sensor for Gait Assessment in Older Adults with Falls. SENSORS 2021; 21:s21134334. [PMID: 34202786 PMCID: PMC8272102 DOI: 10.3390/s21134334] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/19/2021] [Accepted: 06/22/2021] [Indexed: 12/11/2022]
Abstract
The high prevalence of falls and the enormous impact they have on the elderly population is a cause for concern. We aimed to develop a walking-monitor gait pattern (G-STRIDE) for older adults based on a 6-axis inertial measurement (IMU) with the application of pedestrian dead reckoning algorithms and tested its structural and clinical validity. A cross-sectional case–control study was conducted with 21 participants (11 fallers and 10 non-fallers). We measured gait using an IMU attached to the foot while participants walked around different grounds (indoor flooring, outdoor floor, asphalt, etc.). The G-STRIDE consisted of a portable inertial device that monitored the gait pattern and a mobile app for telematic clinical analysis. G-STRIDE made it possible to measure gait parameters under normal living conditions when walking without assessing the patient in the outpatient clinic. Moreover, we verified concurrent validity with convectional outcome measures using intraclass correlation coefficients (ICCs) and analyzed the differences between participants. G-STRIDE showed high estimation accuracy for the walking speed of the elderly and good concurrent validity compared to conventional measures (ICC = 0.69; p < 0.000). In conclusion, the developed inertial-based G-STRIDE can accurately classify older people with risk to fall with a significance as high as using traditional but more subjective clinical methods (gait speed, Timed Up and Go Test).
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Geronimo A, Martin AE, Simmons Z. Inertial sensing of step kinematics in ambulatory patients with ALS and related motor neuron diseases. J Med Eng Technol 2021; 45:486-493. [PMID: 34016013 DOI: 10.1080/03091902.2021.1922526] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive disorder which impairs gait and elevates the risk for falls. Current methods of assessing gait in these patients are infrequent and subjective. The goal of this study was to evaluate wearable-based methods for assessing gait to facilitating better monitoring of ambulatory health and ultimately lessen fall risk. Thirty ambulatory patients seen in ALS clinic were guided by a physical therapist on a short walk, during which inertial sensors recorded their movement. Two methods, utilising sensors at the waist or foot, were used independently to estimate gait parameters. Decreased stride length, increased stride duration and decreased walking speed were associated with lower functional walking scores, and the presence of a cane or walker. Overall, there was no group-wide mean walking speed differences between methods, though the waist method overestimated stride length and walking speed in those with more significant gait dysfunction compared to the foot method. Reconstruction of movement using the foot-based sensor resulted in route segments that were 94 ± 1% standard error of the mean (SEM) the length of a centre-to-centre hallway reference vector, with an angular error of 0.66 ± 0.28° SEM.
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Affiliation(s)
- Andrew Geronimo
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
| | - Anne E Martin
- Department of Mechanical Engineering, Penn State University, University Park, PA, USA
| | - Zachary Simmons
- Departments of Neurology and Humanities, Penn State College of Medicine, Hershey, PA, USA
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Hsieh CY, Huang HY, Liu KC, Liu CP, Chan CT, Hsu SJP. Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor. SENSORS 2021; 21:s21093302. [PMID: 34068804 PMCID: PMC8126206 DOI: 10.3390/s21093302] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/07/2021] [Accepted: 05/07/2021] [Indexed: 12/19/2022]
Abstract
Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.
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Affiliation(s)
- Chia-Yeh Hsieh
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Hsiang-Yun Huang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Kai-Chun Liu
- Research Center for Information Technology Innovation, Academia Sinica, Taipei 11529, Taiwan;
| | - Chien-Pin Liu
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Chia-Tai Chan
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Steen Jun-Ping Hsu
- Department of Information Management, Minghsin University of Science and Technology, Hsinchu 30401, Taiwan
- Correspondence:
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Gaspar AGM, Lapão LV. eHealth for Addressing Balance Disorders in the Elderly: Systematic Review. J Med Internet Res 2021; 23:e22215. [PMID: 33908890 PMCID: PMC8116987 DOI: 10.2196/22215] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/10/2021] [Accepted: 02/25/2021] [Indexed: 02/06/2023] Open
Abstract
Background The population is aging on a global scale, triggering vulnerability for chronic multimorbidity, balance disorders, and falls. Falls with injuries are the main cause of accidental death in the elderly population, representing a relevant public health problem. Balance disorder is a major risk factor for falling and represents one of the most frequent reasons for health care demand. The use of information and communication technologies to support distance healthcare (eHealth) represents an opportunity to improve the access and quality of health care services for the elderly. In recent years, several studies have addressed the potential of eHealth devices to assess the balance and risk of falling of elderly people. Remote rehabilitation has also been explored. However, the clinical applicability of these digital solutions for elderly people with balance disorders remains to be studied. Objective The aim of this review was to guide the clinical applicability of eHealth devices in providing the screening, assessment, and treatment of elderly people with balance disorders, but without neurological disease. Methods A systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. Data were obtained through searching the PubMed, Google Scholar, Embase, and SciELO databases. Only randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published between January 2015 and December 2019 were included. The quality of the evidence to respond to the research question was assessed using Joanna Briggs Institute (JBI) Critical Appraisal for RCTs and the JBI Critical Appraisal Checklist for QESs. RCTs were assessed using the Cochrane risk of bias tool. We provide a narrative synthesis of the main outcomes from the included studies. Results Among 1030 unduplicated articles retrieved, 21 articles were included in this review. Twelve studies explored different technology devices to obtain data about balance and risk of falling. Nine studies focused on different types of balance exercise training. A wide range of clinical tests, functional scales, classifications of faller participants, sensor-based tasks, intervention protocols, and follow-up times were used. Only one study described the clinical conditions of the participants. Instrumental tests of the inner ear were neither used as the gold-standard test nor performed in pre and postrehabilitation assessments. Conclusions eHealth has potential for providing additional health care to elderly people with balance disorder and risk of falling. In the included literature, the heterogeneity of populations under study, methodologies, eHealth devices, and time of follow-up did not allow for clear comparison to guide proper clinical applicability. This suggests that more rigorous studies are needed.
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Affiliation(s)
- Andréa G Martins Gaspar
- Hospital Beatriz Ângelo, Lisbon, Portugal.,Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Luís Velez Lapão
- Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa, Lisbon, Portugal
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A Solution for the Remote Care of Frail Elderly Individuals via Exergames. SENSORS 2021; 21:s21082719. [PMID: 33921548 PMCID: PMC8069394 DOI: 10.3390/s21082719] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/06/2021] [Accepted: 04/09/2021] [Indexed: 12/14/2022]
Abstract
Internet of Things (IoT) solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. Currently, frail elderly people are among end users who most need and would benefit from IoT solutions from both a social and a healthcare point of view. Indeed, IoT technologies can provide a set of services to monitor the healthcare of the elderly or support them in order to reduce the risk of injuries, and preserve their motor and cognitive abilities. The main feature of IoT solutions for the elderly population is ease of use. Indeed, to fully exploit the potential of an IoT solution, patients should be able to autonomously deal with it. The remote-monitoring validation engineering system (ReMoVES) described here is an IoT solution that caters to the specific needs of frail elderly individuals. Its architecture was designed for use at rehabilitation centers and at patients' homes. The system is user-friendly and comfortably usable by persons who are not familiar with technology. In addition, exergames enhance patient engagement in order to curb therapy abandonment. Along with the technical presentation of the solution, a real-life scenario application is described referring to sit-to-stand activity.
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Yu L, Zhao Y, Wang H, Sun TL, Murphy TE, Tsui KL. Assessing elderly's functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests. BMC Med Inform Decis Mak 2021; 21:108. [PMID: 33766011 PMCID: PMC7995592 DOI: 10.1186/s12911-021-01463-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 03/07/2021] [Indexed: 11/18/2022] Open
Abstract
Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.
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Affiliation(s)
- Lisha Yu
- School of Data Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-Sen University, Guangdong, People's Republic of China.
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Tien-Lung Sun
- Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan
| | - Terrence E Murphy
- Department of Internal Medicine, Yale University School of Medicine, New Haven, USA
| | - Kwok-Leung Tsui
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, USA
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Chen B, Liu P, Xiao F, Liu Z, Wang Y. Review of the Upright Balance Assessment Based on the Force Plate. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18052696. [PMID: 33800119 PMCID: PMC7967421 DOI: 10.3390/ijerph18052696] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 02/26/2021] [Indexed: 12/15/2022]
Abstract
Quantitative assessment is crucial for the evaluation of human postural balance. The force plate system is the key quantitative balance assessment method. The purpose of this study is to review the important concepts in balance assessment and analyze the experimental conditions, parameter variables, and application scope based on force plate technology. As there is a wide range of balance assessment tests and a variety of commercial force plate systems to choose from, there is room for further improvement of the test details and evaluation variables of the balance assessment. The recommendations presented in this article are the foundation and key part of the postural balance assessment; these recommendations focus on the type of force plate, the subject's foot posture, and the choice of assessment variables, which further enriches the content of posturography. In order to promote a more reasonable balance assessment method based on force plates, further methodological research and a stronger consensus are still needed.
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Fall risk assessment in the wild: A critical examination of wearable sensor use in free-living conditions. Gait Posture 2021; 85:178-190. [PMID: 33601319 DOI: 10.1016/j.gaitpost.2020.04.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 03/12/2020] [Accepted: 04/04/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND Despite advances in laboratory-based supervised fall risk assessment methods (FRAs), falls still remain a major public health problem. This can be due to the alteration of behavior in laboratory due to the awareness of being observed (i.e., Hawthorne effect), the multifactorial complex etiology of falls, and our limited understanding of human behaviour in natural environments, or in the' wild'. To address these imitations, a growing body of literature has focused on free-living wearable-sensor-based FRAs. The objective of this narrative literature review is to discuss papers investigating natural data collected by wearable sensors for a duration of at least 24 h to identify fall-prone older adults. METHODS Databases (Scopus, PubMed and Google Scholar) were searched for studies based on a rigorous search strategy. RESULTS Twenty-four journal papers were selected, in which inertial sensors were the only wearable system employed for FRA in the wild. Gait was the most-investigated activity; but sitting, standing, lying, transitions and gait events, such as turns and missteps, were also explored. A multitude of free-living fall predictors (FLFPs), e.g., the quantity of daily steps, were extracted from activity bouts and events. FLFPs were further categorized into discrete domains (e.g., pace, complexity) defined by conceptual or data-driven models. Heterogeneity was found within the reviewed studies, which includes variance in: terminology (e.g., quantity vs macro), hyperparameters to define/estimate FLFPs, models and domains, and data processing approaches (e.g., the cut-off thresholds to define an ambulatory bout). These inconsistencies led to different results for similar FLFPs, limiting the ability to interpret and compare the evidence. CONCLUSION Free-living FRA is a promising avenue for fall prevention. Achieving a harmonized model is necessary to systematically address the inconsistencies in the field and identify FLFPs with the highest predictive values for falls to eventually address intervention programs and fall prevention.
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Vollmer Dahlke D, Lee S, Smith ML, Shubert T, Popovich S, Ory MG. Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study. JMIR Aging 2021; 4:e23381. [PMID: 33502320 PMCID: PMC8081189 DOI: 10.2196/23381] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/18/2020] [Accepted: 11/09/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Wearable technology for fall alerts among older adult care recipients is one of the more frequently studied areas of technology, given the concerning consequences of falls among this population. Falls are quite prevalent in later life. While there is a growing amount of literature on older adults' acceptance of technology, less is known about how caregivers' attitudes toward technology can impact care recipients' use of such technology. OBJECTIVE The objective of our study was to examine associations between caregivers' attitudes toward technology for caregiving and care recipients' use of fall alert wearables. METHODS This study examined data collected with an online survey from 626 caregivers for adults 50 years and older. Adapted from the technology acceptance model, a structural equation model tested the following prespecified hypotheses: (1) higher perceived usefulness of technologies for caregiving would predict higher perceived value of and greater interest in technologies for caregiving; (2) higher perceived value of technologies for caregiving would predict greater interest in technologies for caregiving; and (3) greater interest in technologies for caregiving would predict greater use of fall alert wearables among care recipients. Additionally, we included demographic factors (eg, caregivers' and care recipients' ages) and caregiving context (eg, caregiver type and caregiving situation) as important predictors of care recipients' use of fall alert wearables. RESULTS Of 626 total respondents, 548 (87.5%) with all valid responses were included in this study. Among care recipients, 28% used fall alert wearables. The final model had a good to fair model fit: a confirmatory factor index of 0.93, a standardized root mean square residual of 0.049, and root mean square error of approximation of 0.066. Caregivers' perceived usefulness of technology was positively associated with their attitudes toward using technology in caregiving (b=.70, P<.001) and interest in using technology for caregiving (b=.22, P=.003). Greater perceived value of using technology in caregiving predicted greater interest in using technology for caregiving (b=.65, P<.001). Greater interest in using technology for caregiving was associated with greater likelihood of care recipients using fall alert wearables (b=.27, P<.001). The caregiver type had the strongest inverse relationship with care recipients' use of fall alert wearables (unpaid vs paid caregiver) (b=-.33, P<.001). CONCLUSIONS This study underscores the importance of caregivers' attitudes in care recipients' technology use for falls management. Raising awareness and improving perception about technologies for caregiving may help caregivers and care recipients adopt and better utilize technologies that can promote independence and enhance safety.
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Affiliation(s)
- Deborah Vollmer Dahlke
- Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States.,DVD Associates, LLC, Austin, TX, United States
| | - Shinduk Lee
- Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States
| | - Matthew Lee Smith
- Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States
| | | | - Stephen Popovich
- Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States.,Clairvoyant Networks, Austin, TX, United States
| | - Marcia G Ory
- Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States
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Karmali F, Goodworth AD, Valko Y, Leeder T, Peterka RJ, Merfeld DM. The role of vestibular cues in postural sway. J Neurophysiol 2021; 125:672-686. [PMID: 33502934 DOI: 10.1152/jn.00168.2020] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Controlling posture requires continuous sensory feedback about body motion and orientation, including from the vestibular organs. Little is known about the role of tilt vs. translation vs. rotation vestibular cues. We examined whether intersubject differences in vestibular function were correlated with intersubject differences in postural control. Vestibular function was assayed using vestibular direction-recognition perceptual thresholds, which determine the smallest motion that can be reliably perceived by a subject seated on a motorized platform in the dark. In study A, we measured thresholds for lateral translation, vertical translation, yaw rotation, and head-centered roll tilts. In study B, we measured thresholds for roll, pitch, and left anterior-right posterior and right anterior-left posterior tilts. Center-of-pressure (CoP) sway was measured in sensory organization tests (study A) and Romberg tests (study B). We found a strong positive relationship between CoP sway and lateral translation thresholds but not CoP sway and other thresholds. This finding suggests that the vestibular encoding of lateral translation may contribute substantially to balance control. Since thresholds assay sensory noise, our results support the hypothesis that vestibular noise contributes to spontaneous postural sway. Specifically, we found that lateral translation thresholds explained more of the variation in postural sway in postural test conditions with altered proprioceptive cues (vs. a solid surface), consistent with postural sway being more dependent on vestibular noise when the vestibular contribution to balance is higher. These results have potential implications for vestibular implants, balance prostheses, and physical therapy exercises.NEW & NOTEWORTHY Vestibular feedback is important for postural control, but little is known about the role of tilt cues vs. translation cues vs. rotation cues. We studied healthy human subjects with no known vestibular pathology or symptoms. Our findings showed that vestibular encoding of lateral translation correlated with medial-lateral postural sway, consistent with lateral translation cues contributing to balance control. This adds support to the hypothesis that vestibular noise contributes to spontaneous postural sway.
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Affiliation(s)
- Faisal Karmali
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts.,Jenks Vestibular Physiology Laboratory, Massachusetts Eye and Ear, Boston, Massachusetts
| | - Adam D Goodworth
- Kinesiology Department, Westmont College, Santa Barbara, California
| | - Yulia Valko
- Departments of Ophthalmology and Neurology, University Hospital Zurich, University of Zurich, Switzerland
| | - Tania Leeder
- Jenks Vestibular Physiology Laboratory, Massachusetts Eye and Ear, Boston, Massachusetts
| | - Robert J Peterka
- Department of Neurology, Oregon Health and Science University, Portland, Oregon.,National Center for Rehabilitative Auditory Research, Veterans Affairs Portland Health Care System, Portland, Oregon
| | - Daniel M Merfeld
- Department of Otolaryngology - Head and Neck Surgery, The Ohio State University, Columbus, Ohio
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Hu Y, Bishnoi A, Kaur R, Sowers R, Hernandez ME. Exploration of Machine Learning to Identify Community Dwelling Older Adults with Balance Dysfunction Using Short Duration Accelerometer Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:812-815. [PMID: 33018109 DOI: 10.1109/embc44109.2020.9175871] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The incidence of fall-related injuries in older adults is high. Given the significant and adverse outcomes that arise from injurious falls in older adults, it is of the utmost importance to identify older adults at greater risk for falls as early as possible. Given that balance dysfunction provides a significant risk factor for falls, an automated and objective identification of balance dysfunction in community dwelling older adults using wearable sensor data when walking may be beneficial. In this study, we examine the feasibility of using wearable sensors, when walking, to identify older adults who have trouble with balance at an early stage using state-of-the-art machine learning techniques. We recruited 21 community dwelling older women. The experimental paradigm consisted of two tasks: Normal walking with a self-selected comfortable speed on an instrumented treadmill and a test of reflexive postural response, using the motor control test (MCT). Based on the MCT, identification of older women with low or high balance function was performed. Using short duration accelerometer data from sensors placed on the knee and hip while walking, supervised machine learning was carried out to classify subjects with low and high balance function. Using a Gradient Boosting Machine (GBM) algorithm, we classified balance function in older adults using 60 seconds of accelerometer data with an average cross validation accuracy of 91.5% and area under the receiver operating characteristic curve (AUC) of 0.97. Early diagnosis of balance dysfunction in community dwelling older adults through the use of user friendly and inexpensive wearable sensors may help in reducing future fall risk in older adults through earlier interventions and treatments, and thereby significantly reduce associated healthcare costs.
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Patel M, Pavic A, Goodwin VA. Wearable inertial sensors to measure gait and posture characteristic differences in older adult fallers and non-fallers: A scoping review. Gait Posture 2020; 76:110-121. [PMID: 31756666 DOI: 10.1016/j.gaitpost.2019.10.039] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 09/27/2019] [Accepted: 10/27/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND Wearable inertial sensors have grown in popularity as a means of objectively assessing fall risk. This review aimed to identify gait and posture differences among older adult fallers and non-fallers which can be measured with the use of wearable inertial sensors. In addition to describing the number of sensors used to obtain measures, the concurrent anatomical locations, how these measures compare to current forms of clinical fall risk assessment tests and the setting of tests. METHODS Following the development of a rigorous search strategy, MEDLINE, Web of Science, Cochrane, EMBASE, PEDro, and CINAHL were systematically searched for studies involving the use of wearable inertial sensors, to determine gait and postural based differences among fallers or those at high fall risk compared with non-fallers and low fall risk adults aged 60 years and older. RESULTS Thirty five papers met the inclusion criteria. One hundred and forty nine gait and posture characteristic differences were identified using wearable inertial sensors. There were sensor derived measures which significantly and strongly correlated with traditional clinical tests. The use of a single wearable inertial sensor located at the lower posterior trunk, was most the most effective location and enough to ascertain multiple pertinent fall risk factors. CONCLUSION This review identified the capabilities of identifying fall risk factors among older adults with the use of wearable inertial sensors. The lightweight portable nature makes inertial sensors an effective tool to be implemented into clinical fall risk assessment and continuous unsupervised home monitoring, in addition to, outdoor testing.
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Affiliation(s)
- Mubarak Patel
- Vibration Engineering Section, College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK.
| | - Aleksandar Pavic
- Vibration Engineering Section, College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK
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Porumb M, Stranges S, Pescapè A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep 2020; 10:170. [PMID: 31932608 PMCID: PMC6957484 DOI: 10.1038/s41598-019-56927-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 01/21/2023] Open
Abstract
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.
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Affiliation(s)
- Mihaela Porumb
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Antonio Pescapè
- Department of Electrical Engineering, University of Napoli "Federico II", Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
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Tunca C, Salur G, Ersoy C. Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters. IEEE J Biomed Health Inform 2019; 24:1994-2005. [PMID: 31831454 DOI: 10.1109/jbhi.2019.2958879] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to facilitate continuous fall risk assessment in non-hospital settings. However, a gait analysis system is not sufficient to detect the characteristics leading to increased fall risk, and powerful inference models are required to detect the underlying factors specific to fall risk. Machine learning models and especially the recently proposed deep learning methods offer the needed predictive power. Deep neural networks have the potential to produce models that can operate directly on the raw data, thus alleviating the need for feature engineering. However, the domain knowledge inherent in the well-established spatio-temporal gait parameters are still valuable to help a model achieve high inference accuracies. In this study, we explore deep learning methods, specifically long short-term memory (LSTM) neural networks, for the problem of fall risk assessment. We utilize sequences of spatio-temporal gait parameters extracted by an inertial sensor-based gait analysis system as input features. To quantify the performance of the proposed approach, we compare it with more traditional machine learning methods. The proposed LSTM model, trained with a gait dataset collected from 60 neurological disorder patients, achieves a superior classification accuracy of 92.1% on a separate test dataset collected from 16 patients. This study serves as one of the first attempts to employ deep learning approaches in this domain and the results demonstrate their potential.
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Bet P, Castro PC, Ponti MA. Fall detection and fall risk assessment in older person using wearable sensors: A systematic review. Int J Med Inform 2019; 130:103946. [PMID: 31450081 DOI: 10.1016/j.ijmedinf.2019.08.006] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/15/2019] [Accepted: 08/07/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening. OBJECTIVE To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies. METHODS A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application. RESULTS We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application. CONCLUSION This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.
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Affiliation(s)
- Patricia Bet
- DGero - Universidade Federal de São Carlos, São Carlos, SP, Brazil.
| | - Paula C Castro
- DGero - Universidade Federal de São Carlos, São Carlos, SP, Brazil
| | - Moacir A Ponti
- ICMC - Universidade de São Paulo, São Carlos, 13566-590, SP, Brazil
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Aprigliano F, Micera S, Monaco V. Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3713. [PMID: 31461908 PMCID: PMC6749342 DOI: 10.3390/s19173713] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 08/22/2019] [Accepted: 08/26/2019] [Indexed: 02/02/2023]
Abstract
This study aimed to investigate the performance of an updated version of our pre-impact detection algorithm parsing out the output of a set of Inertial Measurement Units (IMUs) placed on lower limbs and designed to recognize signs of lack of balance due to tripping. Eight young subjects were asked to manage tripping events while walking on a treadmill. An adaptive threshold-based algorithm, relying on a pool of adaptive oscillators, was tuned to identify abrupt kinematics modifications during tripping. Inputs of the algorithm were the elevation angles of lower limb segments, as estimated by IMUs located on thighs, shanks and feet. The results showed that the proposed algorithm can identify a lack of balance in about 0.37 ± 0.11 s after the onset of the perturbation, with a low percentage of false alarms (<10%), by using only data related to the perturbed shank. The proposed algorithm can hence be considered a multi-purpose tool to identify different perturbations (i.e., slippage and tripping). In this respect, it can be implemented for different wearable applications (e.g., smart garments or wearable robots) and adopted during daily life activities to enable on-demand injury prevention systems prior to fall impacts.
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Affiliation(s)
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, Ecole Polytechnique Federale de Lausanne, 1015 Lausanne, Switzerland
| | - Vito Monaco
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy.
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Hao M, Chen K, Fu C. Smoother-Based 3-D Foot Trajectory Estimation Using Inertial Sensors. IEEE Trans Biomed Eng 2019; 66:3534-3542. [PMID: 30932822 DOI: 10.1109/tbme.2019.2907322] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Measuring three-dimensional (3-D) foot trajectories with foot-worn inertial measurement units (IMUs) is essential for a variety of applications, such as gait analysis and fall risk assessment. IMU-based foot trajectory is usually reconstructed by double integrating the global coordinate acceleration, in which drifts of signals are accumulated and lead to unbounded error increase. To reduce drift errors, a smoother-based method is proposed in this paper. METHODS The smoother-based method not only corrects initial values of integrations, but also smooths integrating processes through a backward update. Both the orientation estimation and the velocity estimation are improved in this concept, which contribute to the improvement of the trajectory estimation. RESULTS The final results are compared with an optical motion capture system as reference. Accuracy is evaluated with ground level walking of nine adult participants, 2302 strides in total. Errors are reduced by 62% on stride length and 44% on stride width of the estimation without our method, with final errors [Formula: see text] and [Formula: see text]. CONCLUSION/SIGNIFICANCE Results prove that our method can improve the accuracy of 3-D foot trajectory estimation. Furthermore, this smoother-based method can reduce drift-related errors when estimating trajectories, which will allow it to expand its applications into other IMU-based measurements.
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