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Liu W, Bai J. Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection. J Neuroeng Rehabil 2024; 21:111. [PMID: 38926890 PMCID: PMC11202321 DOI: 10.1186/s12984-024-01409-7] [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: 11/09/2023] [Accepted: 06/20/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE To avoid deviation caused by the traditional scale method, the present study explored the accuracy, advantages, and disadvantages of different objective detection methods in evaluating lower extremity motor function in elderly individuals. METHODS Studies on lower extremity motor function assessment in elderly individuals published in the PubMed, Web of Science, Cochrane Library and EMBASE databases in the past five years were searched. The methodological quality of the included trials was assessed using RevMan 5.4.1 and Stata, followed by statistical analyses. RESULTS In total, 19 randomized controlled trials with a total of 2626 participants, were included. The results of the meta-analysis showed that inertial measurement units (IMUs), motion sensors, 3D motion capture systems, and observational gait analysis had statistical significance in evaluating the changes in step velocity and step length of lower extremity movement in elderly individuals (P < 0.00001), which can be used as a standardized basis for the assessment of motor function in elderly individuals. Subgroup analysis showed that there was significant heterogeneity in the assessment of step velocity [SMD=-0.98, 95%CI(-1.23, -0.72), I2 = 91.3%, P < 0.00001] and step length [SMD=-1.40, 95%CI(-1.77, -1.02), I2 = 86.4%, P < 0.00001] in elderly individuals. However, the sensors (I2 = 9%, I2 = 0%) and 3D motion capture systems (I2 = 0%) showed low heterogeneity in terms of step velocity and step length. The sensitivity analysis and publication bias test demonstrated that the results were stable and reliable. CONCLUSION observational gait analysis, motion sensors, 3D motion capture systems, and IMUs, as evaluation means, play a certain role in evaluating the characteristic parameters of step velocity and step length in lower extremity motor function of elderly individuals, which has good accuracy and clinical value in preventing motor injury. However, the high heterogeneity of observational gait analysis and IMUs suggested that different evaluation methods use different calculation formulas and indicators, resulting in the failure to obtain standardized indicators in clinical applications. Thus, multimodal quantitative evaluation should be integrated.
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Affiliation(s)
- Wen Liu
- Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Spine and Spinal Cord Surgery, Beijing Boai Hospital, China Rehabilitation Research Centre, Beijing, China
| | - Jinzhu Bai
- Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China.
- Department of Spine and Spinal Cord Surgery, Beijing Boai Hospital, China Rehabilitation Research Centre, Beijing, China.
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.
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Caramaschi S, Olsson CM, Orchard E, Molloy J, Salvi D. Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT. SENSORS (BASEL, SWITZERLAND) 2024; 24:2632. [PMID: 38676249 PMCID: PMC11054500 DOI: 10.3390/s24082632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/18/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024]
Abstract
As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.
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Affiliation(s)
- Sara Caramaschi
- Department of Computer Science and Media Technology, Internet of Things and People Research Center, Malmö University, 21119 Malmö, Sweden; (C.M.O.); (D.S.)
| | - Carl Magnus Olsson
- Department of Computer Science and Media Technology, Internet of Things and People Research Center, Malmö University, 21119 Malmö, Sweden; (C.M.O.); (D.S.)
| | - Elizabeth Orchard
- Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7JX, UK; (E.O.); (J.M.)
| | - Jackson Molloy
- Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7JX, UK; (E.O.); (J.M.)
| | - Dario Salvi
- Department of Computer Science and Media Technology, Internet of Things and People Research Center, Malmö University, 21119 Malmö, Sweden; (C.M.O.); (D.S.)
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Shah VV, Carlson-Kuhta P, Mancini M, Sowalsky K, Horak FB. Digital gait measures, but not the 400-meter walk time, detect abnormal gait characteristics in people with Prediabetes. Gait Posture 2024; 109:84-88. [PMID: 38286063 DOI: 10.1016/j.gaitpost.2024.01.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 01/04/2024] [Accepted: 01/23/2024] [Indexed: 01/31/2024]
Abstract
BACKGROUND AND AIM Abnormal gait characteristics have been observed in people with diabetic neuropathy, but it is unclear if subtle changes in gait occur in prediabetic people with impaired fasting glucose (IFG). The aims of this study were: (1) to investigate if digital gait measures discriminate people with prediabetes from healthy control participants (HC) and (2) to investigate the relationship between gait measures and clinical scores (concurrent validity). METHODS 108 people with prediabetes (71.20 ± 5.11 years) and 63 HC subjects (70.40 ± 6.25 years) wore 6 inertial sensors (Opals by APDM, Clario) while performing the 400-meter fast walk test. Fifty-five measures across 5 domains of gait (Lower Body, Upper Body, Turning, and Variability) were averaged. Analysis of Covariance was used to investigate the group differences, with body mass index as a covariate. Pearson's correlation coefficient assessed the association between the gait measures and the Short Physical Performance Battery (SPPB) score. RESULTS Nine gait measures were significantly different (p < 10-4) between IFG and HC groups. Step duration, cadence, and turn velocity were the most discriminative measures. In contrast, traditional stop-watch time was not significantly different between groups (p = 0.13), after controlling for BMI. Cadence (r = -0.37, p < 0.001), step duration (r = -0.39, p < 0.001), and turn velocity (r = 0.47, p < 0.001) showed a significant correlation with the SPPB score. CONCLUSION Body-worn inertial sensors detected gait impairments in people with prediabetes that related to clinical balance test performance, even when the traditional stop-watch time was not prolonged for the 400-meter walk test.
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Affiliation(s)
- Vrutangkumar V Shah
- APDM Wearable Technologies, a Clario company, Portland, OR, USA; Department of Neurology, Oregon Health & Science University, Portland, OR, USA.
| | | | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | | | - Fay B Horak
- APDM Wearable Technologies, a Clario company, Portland, OR, USA; Department of Neurology, Oregon Health & Science University, Portland, OR, USA
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Wang Y, Fehr KH, Adamczyk PG. Impact-Aware Foot Motion Reconstruction and Ramp/Stair Detection Using One Foot-Mounted Inertial Measurement Unit. SENSORS (BASEL, SWITZERLAND) 2024; 24:1480. [PMID: 38475012 DOI: 10.3390/s24051480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
Motion reconstruction using wearable sensors enables broad opportunities for gait analysis outside laboratory environments. Inertial Measurement Unit (IMU)-based foot trajectory reconstruction is an essential component of estimating the foot motion and user position required for any related biomechanics metrics. However, limitations remain in the reconstruction quality due to well-known sensor noise and drift issues, and in some cases, limited sensor bandwidth and range. In this work, to reduce drift in the height direction and handle the impulsive velocity error at heel strike, we enhanced the integration reconstruction with a novel kinematic model that partitions integration velocity errors into estimates of acceleration bias and heel strike vertical velocity error. Using this model, we achieve reduced height drift in reconstruction and simultaneously accomplish reliable terrain determination among level ground, ramps, and stairs. The reconstruction performance of the proposed method is compared against the widely used Error State Kalman Filter-based Pedestrian Dead Reckoning and integration-based foot-IMU motion reconstruction method with 15 trials from six subjects, including one prosthesis user. The mean height errors per stride are 0.03±0.08 cm on level ground, 0.95±0.37 cm on ramps, and 1.27±1.22 cm on stairs. The proposed method can determine the terrain types accurately by thresholding on the model output and demonstrates great reconstruction improvement in level-ground walking and moderate improvement on ramps and stairs.
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Affiliation(s)
- Yisen Wang
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Katherine H Fehr
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Peter G Adamczyk
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
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Kvist A, Tinmark F, Bezuidenhout L, Reimeringer M, Conradsson DM, Franzén E. Validation of algorithms for calculating spatiotemporal gait parameters during continuous turning using lumbar and foot mounted inertial measurement units. J Biomech 2024; 162:111907. [PMID: 38134464 DOI: 10.1016/j.jbiomech.2023.111907] [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: 07/12/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
Spatiotemporal gait parameters such as step time and walking speed can be used to quantify gait performance and determine physical function. Inertial measurement units (IMUs) allow for the measurement of spatiotemporal gait parameters in unconstrained environments but must be validated against a gold standard. While many IMU systems and algorithms have been validated during treadmill walking and overground walking in a straight line, fewer studies have validated algorithms during more complex walking conditions such as continuous turning in different directions. This study explored the concurrent validity in a population of healthy adults (range 26-52 years) of three different algorithms using lumbar and foot mounted IMUs to calculate spatiotemporal gait parameters: two methods utilizing an inverted pendulum model, and one method based on strapdown integration. IMU data was compared to a Vicon twelve-camera optoelectronic system, using data collected from 9 participants performing straight walking and continuous walking trials at different speeds, resulting in 162 walking trials in total. Intraclass correlation coefficients (ICCA,1) for absolute agreement were calculated between the algorithm outputs and Vicon output. Temporal parameters were comparable in all methods and ranged from moderate to excellent, except double support time which was poor. Strapdown integration performed better for estimating spatial parameters than pendulum models during straight walking, but worse during turning. Selecting the most appropriate model should take into consideration both speed and walking condition.
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Affiliation(s)
- Alexander Kvist
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Fredrik Tinmark
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Physiology, Nutrition and Biomechanics, The Swedish School of Sport and Health Sciences, Sweden
| | - Lucian Bezuidenhout
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Reimeringer
- Karolinska University Hospital, Motion Analysis Laboratory, Stockholm, Sweden
| | - David Moulaee Conradsson
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
| | - Erika Franzén
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden.
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Woelfle T, Bourguignon L, Lorscheider J, Kappos L, Naegelin Y, Jutzeler CR. Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective. J Med Internet Res 2023; 25:e44428. [PMID: 37498655 PMCID: PMC10415952 DOI: 10.2196/44428] [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: 11/18/2022] [Revised: 12/19/2022] [Accepted: 05/04/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains. OBJECTIVE This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades. METHODS We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. RESULTS Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good. CONCLUSIONS Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS. TRIAL REGISTRATION PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.
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Affiliation(s)
- Tim Woelfle
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Lucie Bourguignon
- Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland
| | - Johannes Lorscheider
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Ludwig Kappos
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Yvonne Naegelin
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
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Mekritthikrai N, Yuenyongchaiwat K, Thanawattano C. Concurrent validity and reliability of new application for 6-min walk test in healthy adults. Heliyon 2023; 9:e17854. [PMID: 37539231 PMCID: PMC10395284 DOI: 10.1016/j.heliyon.2023.e17854] [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: 10/16/2022] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 08/05/2023] Open
Abstract
Background Evaluation assessments for physical performance, such as walking tests, are important for measuring a person's well-being. As of current, medical technology is primarily used to administer these assessments. However, medical devices are not easily accessible and are intended for research purposes only, and hence inconvenient for clinical use. Therefore, we aimed to develop a prototype physical performance assessor device with a mobile application and explored concurrent validity and reliability between the standard 6-min walk test (6MWT) and wearable sensor 6MWT using 6-min walk distance in healthy adults. Methods Sixty healthy males and females, above 18 years of age, were required to attach a sensor to their dominant ankle while the standard protocol for 6MWT was performed. After completing the walking test, the distance from the wearable sensor 6MWT with a mobile application and the standard 6MWT were recorded and compared. Results There was no significant difference between the distance between the standard 6MWT (410.12 ± 74.03 m) and the distance obtained with the wearable sensor. Concurrent validity was found to be moderate, and Cronbach's alpha was 0.79, which indicated good internal consistency. Conclusion The innovative prototype wearable walking sensor with a mobile application can effectively evaluate physical performance in healthy individuals.Clinical trial registration number: TCTR20220801002.
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Affiliation(s)
- Nuttawuth Mekritthikrai
- Department of Physiotherapy, Faculty of Allied Health Sciences, Thammasat University, Thailand
| | - Kornanong Yuenyongchaiwat
- Department of Physiotherapy, Faculty of Allied Health Sciences, Thammasat University, Thailand
- Thammasat University Research Unit in Physical Therapy in Cardiovascular and Respiratory Systems, Thailand
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Granat M, Holtermann A, Lyden K. Sensors for Human Physical Behaviour Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:4091. [PMID: 37112432 PMCID: PMC10145139 DOI: 10.3390/s23084091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 06/19/2023]
Abstract
The understanding and measurement of physical behaviours that occur in everyday life are essential not only for determining their relationship with health, but also for interventions, physical activity monitoring/surveillance of the population and specific groups, drug development, and developing public health guidelines and messages [...].
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Affiliation(s)
- Malcolm Granat
- School of Health and Society, University of Salford, Salford M6 6PU, UK
| | - Andreas Holtermann
- National Research Centre for the Working Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark
| | - Kate Lyden
- VivoSense, 27 Dorian, Newport Coast, CA 92657, USA
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