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Feld L, Schell-Majoor L, Hellmers S, Koschate J, Hein A, Zieschang T, Kollmeier B. Comparison of professional and everyday wearable technology at different body positions in terms of recording gait perturbations. PLOS DIGITAL HEALTH 2024; 3:e0000553. [PMID: 39213262 PMCID: PMC11364241 DOI: 10.1371/journal.pdig.0000553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 06/18/2024] [Indexed: 09/04/2024]
Abstract
Falls are a significant health problem in older people, so preventing them is essential. Since falls are often a consequence of improper reaction to gait disturbances, such as slips and trips, their detection is gaining attention in research. However there are no studies to date that investigated perturbation detection, using everyday wearable devices like hearing aids or smartphones at different body positions. Sixty-six study participants were perturbed on a split-belt treadmill while recording data with hearing aids, smartphones, and professional inertial measurement units (IMUs) at various positions (left/right ear, jacket pocket, shoulder bag, pants pocket, left/right foot, left/right wrist, lumbar, sternum). The data were visually inspected and median maximum cross-correlations were calculated for whole trials and different perturbation conditions. The results show that the hearing aids and IMUs perform equally in measuring acceleration data (correlation coefficient of 0.93 for the left hearing aid and 0.99 for the right hearing aid), which emphasizes the potential of utilizing sensors in hearing aids for head acceleration measurements. Additionally, the data implicate that measurement with a single hearing aid is sufficient and a second hearing aid provides no added value. Furthermore, the acceleration patterns were similar for the ear position, the jacket pocket position, and the lumbar (correlation coefficient of about 0.8) or sternal position (correlation coefficient of about 0.9). The correlations were found to be more or less independent of the type of perturbation. Data obtained from everyday wearable devices appears to represent the movements of the human body during perturbations similar to that of professional devices. The results suggest that IMUs in hearing aids and smartphones, placed at the trunk, could be well suited for an automatic detection of gait perturbations.
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Affiliation(s)
- Lea Feld
- Department of Medical Physics and Acoustics, Medical Physics and Cluster of Excellence Hearing4all, Carl von Ossietzky University, Oldenburg, Germany
- Department for Health Services Research, Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Oldenburg, Germany
- Department for Health Services Research, Geriatric Medicine, Carl von Ossietzky University, Oldenburg, Germany
| | - Lena Schell-Majoor
- Department of Medical Physics and Acoustics, Medical Physics and Cluster of Excellence Hearing4all, Carl von Ossietzky University, Oldenburg, Germany
| | - Sandra Hellmers
- Department for Health Services Research, Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Oldenburg, Germany
| | - Jessica Koschate
- Department for Health Services Research, Geriatric Medicine, Carl von Ossietzky University, Oldenburg, Germany
| | - Andreas Hein
- Department for Health Services Research, Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Oldenburg, Germany
| | - Tania Zieschang
- Department for Health Services Research, Geriatric Medicine, Carl von Ossietzky University, Oldenburg, Germany
| | - Birger Kollmeier
- Department of Medical Physics and Acoustics, Medical Physics and Cluster of Excellence Hearing4all, Carl von Ossietzky University, Oldenburg, Germany
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2
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Torres-Guzman RA, Paulson MR, Avila FR, Maita K, Garcia JP, Forte AJ, Maniaci MJ. Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:1323. [PMID: 36772364 PMCID: PMC9920087 DOI: 10.3390/s23031323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims to discuss the extent of smartphone (SP) usage in fall detection and prevention across a range of care settings. A computerized search was conducted on six electronic databases to investigate the use of remote sensing technology, wireless technology, and other related MeSH terms for detecting and preventing falls. After applying inclusion and exclusion criteria, 44 studies were included. Most of the studies targeted detecting falls, two focused on detecting and preventing falls, and one only looked at preventing falls. Accelerometers were employed in all the experiments for the detection and/or prevention of falls. The most frequent course of action following a fall event was an alarm to the guardian. Numerous studies investigated in this research used accelerometer data analysis, machine learning, and data from previous falls to devise a boundary and increase detection accuracy. SP was found to have potential as a fall detection system but is not widely implemented. Technology-based applications are being developed to protect at-risk individuals from falls, with the objective of providing more effective and efficient interventions than traditional means. Successful healthcare technology implementation requires cooperation between engineers, clinicians, and administrators.
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Affiliation(s)
| | - Margaret R. Paulson
- Division of Hospital Internal Medicine, Mayo Clinic Health Systems, 1221 Whipple St., Eau Claire, WI 54703, USA
| | - Francisco R. Avila
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | - Karla Maita
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | - John P. Garcia
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | - Antonio J. Forte
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
| | - Michael J. Maniaci
- Division of Hospital Internal Medicine, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA
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Frechette M, Fanning J, Hsieh K, Rice L, Sosnoff J. The Usability of a Smartphone-Based Fall Risk Assessment App for Adult Wheelchair Users: Observational Study. JMIR Form Res 2022; 6:e32453. [PMID: 36112405 PMCID: PMC9526126 DOI: 10.2196/32453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 04/22/2022] [Accepted: 06/27/2022] [Indexed: 12/04/2022] Open
Abstract
Background Individuals who use wheelchairs and scooters rarely undergo fall risk screening. Mobile health technology is a possible avenue to provide fall risk assessment. The promise of this approach is dependent upon its usability. Objective We aimed to determine the usability of a fall risk mobile health app and identify key technology development insights for aging adults who use wheeled devices. Methods Two rounds (with 5 participants in each round) of usability testing utilizing an iterative design-evaluation process were performed. Participants completed use of the custom-designed fall risk app, Steady-Wheels. To quantify fall risk, the app led participants through 12 demographic questions and 3 progressively more challenging seated balance tasks. Once completed, participants shared insights on the app’s usability through semistructured interviews and completion of the Systematic Usability Scale. Testing sessions were recorded and transcribed. Codes were identified within the transcriptions to create themes. Average Systematic Usability Scale scores were calculated for each round. Results The first round of testing yielded 2 main themes: ease of use and flexibility of design. Systematic Usability Scale scores ranged from 72.5 to 97.5 with a mean score of 84.5 (SD 11.4). After modifications were made, the second round of testing yielded 2 new themes: app layout and clarity of instruction. Systematic Usability Scale scores improved in the second iteration and ranged from 87.5 to 97.5 with a mean score of 91.9 (SD 4.3). Conclusions The mobile health app, Steady-Wheels, has excellent usability and the potential to provide adult wheeled device users with an easy-to-use, remote fall risk assessment tool. Characteristics that promoted usability were guided navigation, large text and radio buttons, clear and brief instructions accompanied by representative illustrations, and simple error recovery. Intuitive fall risk reporting was achieved through the presentation of a single number located on a color-coordinated continuum that delineated low, medium, and high risk.
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Affiliation(s)
- Mikaela Frechette
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Siebel Center for Design, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Jason Fanning
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC, United States
| | - Katherine Hsieh
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Laura Rice
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Illinois Multiple Sclerosis Research Collaborative, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Center on Health, Aging, and Disability, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jacob Sosnoff
- Illinois Multiple Sclerosis Research Collaborative, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, KS, United States
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4
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Human fall detection and activity monitoring: a comparative analysis of vision-based methods for classification and detection techniques. Soft comput 2022. [DOI: 10.1007/s00500-021-06717-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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5
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Mertens M, Debard G, Davis J, Devriendt E, Milisen K, Tournoy J, Croonenborghs T, Vanrumste B. Motion Sensor-Based Detection of Outlier Days Supporting Continuous Health Assessment for Single Older Adults. SENSORS (BASEL, SWITZERLAND) 2021; 21:6080. [PMID: 34577295 PMCID: PMC8472855 DOI: 10.3390/s21186080] [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: 06/29/2021] [Revised: 09/03/2021] [Accepted: 09/04/2021] [Indexed: 12/19/2022]
Abstract
The aging population has resulted in interest in remote monitoring of elderly individuals' health and well being. This paper describes a simple unsupervised monitoring system that can automatically detect if an elderly individual's pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual's typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual's observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject's health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver.
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Affiliation(s)
- Marc Mertens
- Mobilab & Care, Thomas More University of Applied Sciences Kempen, Kleinhoefstraat 4, 2440 Geel, Belgium;
- Department of Computer Science, KU Leuven, 3001 Heverlee, Belgium; (J.D.); (T.C.)
| | - Glen Debard
- Mobilab & Care, Thomas More University of Applied Sciences Kempen, Kleinhoefstraat 4, 2440 Geel, Belgium;
| | - Jesse Davis
- Department of Computer Science, KU Leuven, 3001 Heverlee, Belgium; (J.D.); (T.C.)
| | - Els Devriendt
- Department of Public Health and Primary Care, Academic Centre for Nursing and Midwifery, KU Leuven, 3000 Leuven, Belgium; (E.D.); (K.M.)
- Department of Geriatric Medicine, University Hospitals Leuven, 3000 Leuven, Belgium;
| | - Koen Milisen
- Department of Public Health and Primary Care, Academic Centre for Nursing and Midwifery, KU Leuven, 3000 Leuven, Belgium; (E.D.); (K.M.)
- Department of Geriatric Medicine, University Hospitals Leuven, 3000 Leuven, Belgium;
| | - Jos Tournoy
- Department of Geriatric Medicine, University Hospitals Leuven, 3000 Leuven, Belgium;
- Department of Public Health and Primary Care, Gerontology and Geriatrics, University of Leuven, 3000 Leuven, Belgium
| | - Tom Croonenborghs
- Department of Computer Science, KU Leuven, 3001 Heverlee, Belgium; (J.D.); (T.C.)
| | - Bart Vanrumste
- eMedia ResearchLab and STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Heverlee, Belgium;
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González-Cañete FJ, Casilari E. A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems. SENSORS 2021; 21:s21062254. [PMID: 33807104 PMCID: PMC8004721 DOI: 10.3390/s21062254] [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] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/18/2021] [Accepted: 03/22/2021] [Indexed: 12/24/2022]
Abstract
Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.
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7
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Hawley-Hague H, Tacconi C, Mellone S, Martinez E, Chiari L, Helbostad J, Todd C. One-to-One and Group-Based Teleconferencing for Falls Rehabilitation: Usability, Acceptability, and Feasibility Study. JMIR Rehabil Assist Technol 2021; 8:e19690. [PMID: 33433398 PMCID: PMC7837999 DOI: 10.2196/19690] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/31/2020] [Accepted: 08/16/2020] [Indexed: 11/24/2022] Open
Abstract
Background Falls have implications for the health of older adults. Strength and balance interventions significantly reduce the risk of falls; however, patients seldom perform the dose of exercise that is required based on evidence. Health professionals play an important role in supporting older adults as they perform and progress in their exercises. Teleconferencing could enable health professionals to support patients more frequently, which is important in exercise behavior. Objective This study aims to examine the overall concept and acceptability of teleconferencing for the delivery of falls rehabilitation with health care professionals and older adults and to examine the usability, acceptability, and feasibility of teleconferencing delivery with health care professionals and patients. Methods There were 2 stages to the research: patient and public involvement workshops and usability and feasibility testing. A total of 2 workshops were conducted, one with 5 health care professionals and the other with 8 older adults from a community strength and balance exercise group. For usability and feasibility testing, we tested teleconferencing both one-to-one and in small groups on a smartphone with one falls service and their patients for 3 weeks. Semistructured interviews and focus groups were used to explore acceptability, usability, and feasibility. Focus groups were conducted with the service that used teleconferencing with patients and 2 other services that received only a demonstration of how teleconferencing works. Qualitative data were analyzed using the framework approach. Results In the workshops, the health care professionals thought that teleconferencing provided an opportunity to save travel time. Older adults thought that it could enable increased support. Safety is of key importance, and delivery needs to be carefully considered. Both older adults and health care professionals felt that it was important that technology did not eliminate face-to-face contact. There were concerns from older adults about the intrusiveness of technology. For the usability and feasibility testing, 7 patients and 3 health care professionals participated, with interviews conducted with 6 patients and a focus group with the health care team. Two additional teams (8 health professionals) took part in a demonstration and focus group. Barriers and facilitators were identified, with 5 barriers around reliability due to poor connectivity, cost of connectivity, safety concerns linked to positioning of equipment and connectivity, intrusiveness of technology, and resistance to group teleconferencing. Two facilitators focused on the positive benefits of increased support and monitoring and positive solutions for future improvements. Conclusions Teleconferencing as a way of delivering fall prevention interventions can be acceptable to older adults, patients, and health care professionals if it works effectively. Connectivity, where there is no Wi-Fi provision, is one of the largest issues. Therefore, local infrastructure needs to be improved. A larger usability study is required to establish whether better equipment for delivery improves usability.
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Affiliation(s)
- Helen Hawley-Hague
- School of Health Sciences, University of Manchester, Manchester, United Kingdom.,Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Carlo Tacconi
- Interdepartmental Center for Industrial Research, Health Sciences and Technologies, University of Bologna, Bologna, Italy.,mHealth Technologies s.r.l., Bologna, Italy
| | - Sabato Mellone
- Interdepartmental Center for Industrial Research, Health Sciences and Technologies, University of Bologna, Bologna, Italy.,mHealth Technologies s.r.l., Bologna, Italy.,Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
| | - Ellen Martinez
- Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Lorenzo Chiari
- Interdepartmental Center for Industrial Research, Health Sciences and Technologies, University of Bologna, Bologna, Italy.,mHealth Technologies s.r.l., Bologna, Italy.,Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
| | - Jorunn Helbostad
- Department of Neuromedicine and Movement Science, The Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Chris Todd
- School of Health Sciences, University of Manchester, Manchester, United Kingdom.,Manchester Academic Health Science Centre, Manchester, United Kingdom.,Manchester University NHS Foundation Trust, Manchester, United Kingdom
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8
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Gawronska A, Pajor A, Zamyslowska-Szmytke E, Rosiak O, Jozefowicz-Korczynska M. Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review. Clin Interv Aging 2020; 15:2397-2406. [PMID: 33376315 PMCID: PMC7764625 DOI: 10.2147/cia.s289861] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 12/09/2020] [Indexed: 11/23/2022] Open
Abstract
Objective The gold standard for objective body posture examination is posturography. Body movements are detected through the use of force platforms that assess static and dynamic balance (conventional posturography). In recent years, new technologies like wearable sensors (mobile posturography) have been applied during complex dynamic activities to diagnose and rehabilitate balance disorders. They are used in healthy people, especially in the aging population, for detecting falls in the older adults, in the rehabilitation of different neurological, osteoarticular, and muscular system diseases, and in vestibular disorders. Mobile devices are portable, lightweight, and less expensive than conventional posturography. The vibrotactile system can consist of an accelerometer (linear acceleration measurement), gyroscopes (angular acceleration measurement), and magnetometers (heading measurement, relative to the Earth’s magnetic field). The sensors may be mounted to the trunk (most often in the lumbar region of the spine, and the pelvis), wrists, arms, sternum, feet, or shins. Some static and dynamic clinical tests have been performed with the use of wearable sensors. Smartphones are widely used as a mobile computing platform and to evaluate the results or monitor the patient during the movement and rehabilitation. There are various mobile applications for smartphone-based balance systems. Future research should focus on validating the sensitivity and reliability of mobile device measurements compared to conventional posturography. Conclusion Smartphone based mobile devices are limited to one sensor lumbar level posturography and offer basic clinical evaluation. Single or multi sensor mobile posturography is available from different manufacturers and offers single to multi-level measurements, providing more data and in some instances even performing sophisticated clinical balance tests.
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Affiliation(s)
- Anna Gawronska
- Balance Disorders Unit, Department of Otolaryngology, Medical University of Lodz, The Norbert Barlicki Memorial Teaching Hospital, Lodz, Poland
| | - Anna Pajor
- Department of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, The Norbert Barlicki Memorial Teaching Hospital, Lodz, Poland
| | - Ewa Zamyslowska-Szmytke
- Balance Disorders Unit, Department of Audiology and Phoniatrics, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Oskar Rosiak
- Balance Disorders Unit, Department of Otolaryngology, Medical University of Lodz, The Norbert Barlicki Memorial Teaching Hospital, Lodz, Poland
| | - Magdalena Jozefowicz-Korczynska
- Balance Disorders Unit, Department of Otolaryngology, Medical University of Lodz, The Norbert Barlicki Memorial Teaching Hospital, Lodz, Poland
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9
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Ponciano V, Pires IM, Ribeiro FR, Spinsante S. Sensors are Capable to Help in the Measurement of the Results of the Timed-Up and Go Test? A Systematic Review. J Med Syst 2020; 44:199. [PMID: 33070247 DOI: 10.1007/s10916-020-01666-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 10/12/2020] [Indexed: 11/24/2022]
Abstract
The analysis of movements used in physiotherapy areas related to the elderly is becoming increasingly important due to factors such as the increase in the average life expectancy and the rate of elderly people over the whole population. In this systematic review, we try to determine how the inertial sensors embedded in mobile devices are exploited for the measurement of the different parameters involved in the Timed-Up and Go test. The results show the mobile devices equipped with onboard motion sensors can be exploited for these types of studies: the most commonly used sensors are the magnetometer, accelerometer and gyroscope available in consumer off-the-shelf smartphones. Other features typically used to evaluate the Timed-Up and Go test are the time duration, the angular velocity and the number of steps, allowing for the recognition of some diseases as well as the measurement of the subject's performance during the test execution.
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Affiliation(s)
- Vasco Ponciano
- R&D Unit in Digital Services, Applications and Content, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal. .,Altranportugal, Lisbon, Portugal.
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal.,Computer Science Department, Polytechnic Institute of Viseu, Viseu, Portugal.,UICISA:E Research Centre, School of Health, Polytechnic Institute of Viseu, Viseu, Portugal
| | - Fernando Reinaldo Ribeiro
- R&D Unit in Digital Services, Applications and Content, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal
| | - Susanna Spinsante
- Department of Information Engineering, Marche Polytechnic University, Ancona, Italy
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10
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Mrozek D, Koczur A, Małysiak-Mrozek B. Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.05.070] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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11
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Bergquist R, Nerz C, Taraldsen K, Mellone S, Ihlen EA, Vereijken B, Helbostad JL, Becker C, Mikolaizak AS. Predicting Advanced Balance Ability and Mobility with an Instrumented Timed Up and Go Test. SENSORS 2020; 20:s20174987. [PMID: 32899143 PMCID: PMC7506906 DOI: 10.3390/s20174987] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 01/14/2023]
Abstract
Extensive test batteries are often needed to obtain a comprehensive picture of a person’s functional status. Many test batteries are not suitable for active and healthy adults due to ceiling effects, or require a lot of space, time, and training. The Community Balance and Mobility Scale (CBMS) is considered a gold standard for this population, but the test is complex, as well as time- and resource intensive. There is a strong need for a faster, yet sensitive and robust test of physical function in seniors. We sought to investigate whether an instrumented Timed Up and Go (iTUG) could predict the CBMS score in 60 outpatients and healthy community-dwelling seniors, where features of the iTUG were predictive, and how the prediction of CBMS with the iTUG compared to standard clinical tests. A partial least squares regression analysis was used to identify latent components explaining variation in CBMS total score. The model with iTUG features was able to predict the CBMS total score with an accuracy of 85.2% (84.9–85.5%), while standard clinical tests predicted 82.5% (82.2–82.8%) of the score. These findings suggest that a fast and easily administered iTUG could be used to predict CBMS score, providing a valuable tool for research and clinical care.
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Affiliation(s)
- Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway; (K.T.); (E.A.F.I.); (B.V.); (J.L.H.)
- Correspondence:
| | - Corinna Nerz
- Department for Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (C.N.); (C.B.); (A.S.M.)
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway; (K.T.); (E.A.F.I.); (B.V.); (J.L.H.)
| | - Sabato Mellone
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, 40136 Bologna, Italy;
| | - Espen A.F. Ihlen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway; (K.T.); (E.A.F.I.); (B.V.); (J.L.H.)
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway; (K.T.); (E.A.F.I.); (B.V.); (J.L.H.)
| | - Jorunn L. Helbostad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway; (K.T.); (E.A.F.I.); (B.V.); (J.L.H.)
| | - Clemens Becker
- Department for Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (C.N.); (C.B.); (A.S.M.)
| | - A. Stefanie Mikolaizak
- Department for Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (C.N.); (C.B.); (A.S.M.)
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12
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Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test. COMPUTERS 2020. [DOI: 10.3390/computers9030067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.
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Kinematic Mobile Drop Jump Analysis at Different Heights Based on a Smartphone Inertial Sensor. J Hum Kinet 2020; 73:57-65. [PMID: 32774537 PMCID: PMC7386144 DOI: 10.2478/hukin-2019-0131] [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] [Indexed: 11/30/2022] Open
Abstract
The purpose of this study was to describe the acceleration variables in a plyometric jump test using the inertial sensor built into an iPhone 4S® smartphone, and the jumping variables from a contact mat. A cross-sectional study was conducted involving 16 healthy young adults. Linear acceleration, flight time, contact time and jump height were measured in a drop jump test from 60 cm and from 30 cm. Greater acceleration values were found in the drop jump test from 60 cm; the same was observed for the values from the contact mat. Multiple regression analysis was performed for each drop jump test: jump height was used as the dependent variable, and the most relevant variables were used as predictor variables (weight and maximum angular velocity in the Y axis for analysis of the drop jump from 60 cm, and weight and maximum acceleration in the Z axis for the drop jump from 30 cm). We found a significant regression model for the drop jump test from 60 cm (R2 = 0.515, p “ 0.001) and for the test from 30 cm (R2 = 0.460, p “ 0.01). According to the results obtained in this study, the built-in iPhone 4S® inertial sensor is able to measure acceleration for healthy young adults performing a vertical drop jump test. The acceleration kinematic variables are higher in the drop jump test from 60 cm than from 30 cm.
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14
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Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults. SENSORS 2020; 20:s20123481. [PMID: 32575650 PMCID: PMC7349529 DOI: 10.3390/s20123481] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 02/05/2023]
Abstract
Due to the increasing age of the European population, there is a growing interest in performing research that will aid in the timely and unobtrusive detection of emerging diseases. For such tasks, mobile devices have several sensors, facilitating the acquisition of diverse data. This study focuses on the analysis of the data collected from the mobile devices sensors and a pressure sensor connected to a Bitalino device for the measurement of the Timed-Up and Go test. The data acquisition was performed within different environments from multiple individuals with distinct types of diseases. Then this data was analyzed to estimate the various parameters of the Timed-Up and Go test. Firstly, the pressure sensor is used to extract the reaction and total test time. Secondly, the magnetometer sensors are used to identify the total test time and different parameters related to turning around. Finally, the accelerometer sensor is used to extract the reaction time, total test time, duration of turning around, going time, return time, and many other derived metrics. Our experiments showed that these parameters could be automatically and reliably detected with a mobile device. Moreover, we identified that the time to perform the Timed-Up and Go test increases with age and the presence of diseases related to locomotion.
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Chander H, Burch RF, Talegaonkar P, Saucier D, Luczak T, Ball JE, Turner A, Kodithuwakku Arachchige SNK, Carroll W, Smith BK, Knight A, Prabhu RK. Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17103554. [PMID: 32438649 PMCID: PMC7277680 DOI: 10.3390/ijerph17103554] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 05/15/2020] [Accepted: 05/16/2020] [Indexed: 11/16/2022]
Abstract
Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.
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Affiliation(s)
- Harish Chander
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
- Correspondence:
| | - Reuben F. Burch
- Department of Human Factors & Athlete Engineering, Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39762, USA;
| | - Purva Talegaonkar
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (P.T.); (B.K.S.)
| | - David Saucier
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Tony Luczak
- National Strategic Planning and Analysis Research Center (NSPARC), Mississippi State University, Mississippi State, MS 39762, USA;
| | - John E. Ball
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Alana Turner
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
| | | | - Will Carroll
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Brian K. Smith
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (P.T.); (B.K.S.)
| | - Adam Knight
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
| | - Raj K. Prabhu
- Department of Agricultural and Biomedical Engineering, Mississippi State University, Mississippi State, MS 39762, USA;
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Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, Charisis V, Bostanjopoulou S, Katsarou Z, Klingelhoefer L, Mayer S, Reichmann H, Dias SB. Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3535-3538. [PMID: 31946641 DOI: 10.1109/embc.2019.8857211] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Parkinson's Disease (PD) is the second most common neurodegenerative disorder worldwide, causing both motor and non-motor symptoms. In the early stages, symptoms are mild and patients may ignore their existence. As a result, they do not undergo any related clinical examination; hence delaying their PD diagnosis. In an effort to remedy such delay, analysis of data passively captured from user's interaction with consumer technologies has been recently explored towards remote screening of early PD motor signs. In the current study, a smartphone-based method analyzing subjects' finger interaction with the smartphone screen is developed for the quantification of fine-motor skills decline in early PD using Convolutional Neural Networks. Experimental results from the analysis of keystroke typing in-the-clinic data from 18 early PD patients and 15 healthy controls have shown a classification performance of 0.89 Area Under the Curve (AUC) with 0.79/0.79 sensitivity/specificity, respectively. Evaluation of the generalization ability of the proposed approach was made by its application on typing data arising from a separate self-reported cohort of 27 PD patients' and 84 healthy controls' daily usage with their personal smartphones (data in-the-wild), achieving 0.79 AUC with 0.74/0.78 sensitivity/specificity, respectively. The results show the potentiality of the proposed approach to process keystroke dynamics arising from users' natural typing activity to detect PD, which contributes to the development of digital tools for remote pathological symptom screening.
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Abstract
The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.
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18
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Haeger M, Bock O, Zijlstra W. [Smartphone-based health promotion in old age : An explorative multi-component approach to improving health in old age]. Z Gerontol Geriatr 2020; 54:146-151. [PMID: 32052186 DOI: 10.1007/s00391-020-01700-x] [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: 12/06/2019] [Accepted: 01/27/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND As age-related health problems are often related to a combination of physiological, psychological and social aspects, it has been proposed that multi-component interventions might be particularly effective to improve older peoples' health. The present study used a smartphone-based approach to promote health through activities including physical as well as cognitive components performed in a daily life context. METHODS This study investigated the effects of different health-related variables (e.g. gait and cognition) as well as the individual motivation for physical activity. The study included 34 community-dwelling older adults (mean age 75.0 ± 3.7 years, 15 women) who took part either in smartphone-based activities (intervention group) or attended lectures (control group). The smartphone-based interventions were undertaken semiweekly. RESULTS Baseline tests showed that participants in both groups already had a high motivation for physical activities. Analyses indicated that the smartphone application was considered to be user-friendly. CONCLUSION There were no substantial health-related benefits from the activities, probably due to moderate to good health status and activity levels at baseline and too little additional activity intensity during the intervention. Hence, it is recommended that for future research the subjects included should be less active or have health restrictions.
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Affiliation(s)
- Mathias Haeger
- Institut für Physiologie und Anatomie, Deutsche Sporthochschule Köln, Am Sportpark Müngersdorf 6, 50933, Köln, Deutschland. .,Institut für Arbeitsmedizin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland.
| | - Otmar Bock
- Institut für Physiologie und Anatomie, Deutsche Sporthochschule Köln, Am Sportpark Müngersdorf 6, 50933, Köln, Deutschland
| | - Wiebren Zijlstra
- Institut für Bewegungs- und Sportgerontologie, Deutsche Sporthochschule Köln, Am Sportpark Müngersdorf 6, 50933, Köln, Deutschland
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19
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Howell DR, Lugade V, Taksir M, Meehan WP. Determining the utility of a smartphone-based gait evaluation for possible use in concussion management. PHYSICIAN SPORTSMED 2020; 48:75-80. [PMID: 31198074 DOI: 10.1080/00913847.2019.1632155] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Objectives: Our was objectives were to (1) assess the validity of a smartphone-based application to obtain spatiotemporal gait variables relative to an established movement monitoring system used previously to evaluate post-concussion gait, and (2) determine the test-retest reliability of gait variables obtained with a smartphone.Methods: Twenty healthy participants (n = 14 females, mean age = 22.2, SD = 2.1 years) were assessed at two time points, approximately two weeks apart. Two measurement systems (inertial sensor system, smartphone application) acquired and analyzed single-task and dual-task spatio-temporal gait variables simultaneously. Our primary outcome measures were average walking speed (m/s), cadence (steps/min), and stride length (m) measured by the inertial sensor system and smartphone application.Results: Correlations between the systems were high to very high (Pearson r = 0.77-0.98) at both time points, with the exception of dual-task stride length at time 2 (Pearson r = 0.55). Bland-Altman analysis for average gait speed and cadence indicated the average disagreement between systems was close to zero, suggesting little evidence for systematic bias between acquisition systems. Test-retest consistency measures using the smartphone revealed high to very high reliability for all measurements (ICC = 0.81-0.95).Conclusions: Our results indicate that sensors within a smartphone are capable of measuring spatio-temporal gait variables similar to a validated three-sensor inertial sensor system in single-task and dual-task conditions, and that data are reliable across a two-week time interval. A smartphone-based application might allow clinicians to objectively evaluate gait in the management of concussion with high ease-of-use and a relatively low financial burden.
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Affiliation(s)
- David R Howell
- Sports Medicine Center, Children's Hospital, Aurora, CO, USA.,Department of Orthopedics, University of Colorado School of Medicine, Aurora, CO, USA.,The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
| | - Vipul Lugade
- Control One LLC, Albuquerque, NM, USA.,Department of Physical Therapy, Chiang Mai University, Chiang Mai, Thailand
| | - Mikhail Taksir
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
| | - William P Meehan
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA.,Division of Sports Medicine, Department of Orthopaedics, Boston Children's Hospital, Boston, MA, USA.,Departments of Pediatrics and Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
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Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors. SENSORS 2020; 20:s20030622. [PMID: 31979189 PMCID: PMC7038232 DOI: 10.3390/s20030622] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/10/2020] [Accepted: 01/20/2020] [Indexed: 11/17/2022]
Abstract
Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.
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21
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Vourganas I, Stankovic V, Stankovic L, Michala AL. Evaluation of Home-Based Rehabilitation Sensing Systems with Respect to Standardised Clinical Tests. SENSORS 2019; 20:s20010026. [PMID: 31861514 PMCID: PMC6982997 DOI: 10.3390/s20010026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 12/16/2019] [Accepted: 12/16/2019] [Indexed: 11/21/2022]
Abstract
With increased demand for tele-rehabilitation, many autonomous home-based rehabilitation systems have appeared recently. Many of these systems, however, suffer from lack of patient acceptance and engagement or fail to provide satisfactory accuracy; both are needed for appropriate diagnostics. This paper first provides a detailed discussion of current sensor-based home-based rehabilitation systems with respect to four recently established criteria for wide acceptance and long engagement. A methodological procedure is then proposed for the evaluation of accuracy of portable sensing home-based rehabilitation systems, in line with medically-approved tests and recommendations. For experiments, we deploy an in-house low-cost sensing system meeting the four criteria of acceptance to demonstrate the effectiveness of the proposed evaluation methodology. We observe that the deployed sensor system has limitations in sensing fast movement. Indicators of enhanced motivation and engagement are recorded through the questionnaire responses with more than 83% of the respondents supporting the system’s motivation and engagement enhancement. The evaluation results demonstrate that the deployed system is fit for purpose with statistically significant (ϱc>0.99, R2>0.94, ICC>0.96) and unbiased correlation to the golden standard.
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Affiliation(s)
- Ioannis Vourganas
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK; (V.S.); (L.S.)
- Correspondence: ; Tel.: +44-141-548-2679
| | - Vladimir Stankovic
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK; (V.S.); (L.S.)
| | - Lina Stankovic
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK; (V.S.); (L.S.)
| | - Anna Lito Michala
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK;
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Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations. ELECTRONICS 2019. [DOI: 10.3390/electronics8101083] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.
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Hawley-Hague H, Tacconi C, Mellone S, Martinez E, Easdon A, Yang FB, Su TL, Mikolaizak AS, Chiari L, Helbostad JL, Todd C. Can smartphone technology be used to support an effective home exercise intervention to prevent falls amongst community dwelling older adults?: the TOGETHER feasibility RCT study protocol. BMJ Open 2019; 9:e028100. [PMID: 31537557 PMCID: PMC6756425 DOI: 10.1136/bmjopen-2018-028100] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 08/06/2019] [Accepted: 08/08/2019] [Indexed: 12/28/2022] Open
Abstract
INTRODUCTION Falls have major implications for quality of life, independence and cost to the health service. Strength and balance training has been found to be effective in reducing the rate/risk of falls, as long as there is adequate fidelity to the evidence-based programme. Health services are often unable to deliver the evidence-based dose of exercise and older adults do not always sufficiently adhere to their programme to gain full outcomes. Smartphone technology based on behaviour-change theory has been used to support healthy lifestyles, but not falls prevention exercise. This feasibility trial will explore whether smartphone technology can support patients to better adhere to an evidence-based rehabilitation programme and test study procedures/outcome measures. METHODS AND ANALYSIS A two-arm, pragmatic feasibility randomised controlled trial will be conducted with health services in Manchester, UK. Seventy-two patients aged 50+years eligible for a falls rehabilitation exercise programme from two community services will receive: (1) standard service with a smartphone for outcome measurement only or (2) standard service plus a smartphone including the motivational smartphone app. The primary outcome is feasibility of the intervention, study design and procedures. The secondary outcome is to compare standard outcome measures for falls, function and adherence to instrumented versions collected using smartphone. Outcome measures collected include balance, function, falls, strength, fear of falling, health-related quality of life, resource use and adherence. Outcomes are measured at baseline, 3 and 6-month post-randomisation. Interviews/focus groups with health professionals and participants further explore feasibility of the technology and trial procedures. Primarily analyses will be descriptive. ETHICS AND DISSEMINATION The study protocol is approved by North West Greater Manchester East Research Ethics Committee (Rec ref:18/NW/0457, 9/07/2018). User groups and patient representatives were consulted to inform trial design, and are involved in study recruitment. Results will be reported at conferences and in peer-reviewed publications. A dissemination event will be held in Manchester to present the results of the trial. The protocol adheres to the recommended Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist. TRIAL REGISTRATION NUMBER ISRCTN12830220; Pre-results.
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Affiliation(s)
- Helen Hawley-Hague
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology, Medicine and Health and Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
| | - Carlo Tacconi
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research, University of Bologna, Bologna, Italy
- mHealth Technologies srl, Bologna, Italy
| | - Sabato Mellone
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research, University of Bologna, Bologna, Italy
- mHealth Technologies srl, Bologna, Italy
- Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi', University of Bologna, Bologna, Italy
| | - Ellen Martinez
- Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Angela Easdon
- Pennine Care NHS Foundation Trust, Ashton-under-Lyne, UK
| | - Fan Bella Yang
- Centre for Health Economics, University of York, York, UK
| | - Ting-Li Su
- Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | | | - Lorenzo Chiari
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research, University of Bologna, Bologna, Italy
- mHealth Technologies srl, Bologna, Italy
- Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi', University of Bologna, Bologna, Italy
| | - Jorunn L Helbostad
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Chris Todd
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology, Medicine and Health and Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
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Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities. SENSORS 2019; 19:s19132845. [PMID: 31248016 PMCID: PMC6651658 DOI: 10.3390/s19132845] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 06/18/2019] [Accepted: 06/21/2019] [Indexed: 01/06/2023]
Abstract
Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e., sitting and/or lying) using only one IMU, even if it is arbitrarily oriented or subsequently re-oriented relative to the body; since the body is upright during walking, learning the IMU orientation during walking provides a reference orientation against which sitting and/or lying can be inferred. Thus, the two activities can be identified (irrespective of the cohort) by analyzing the magnitude of the angle of shortest rotation which would be required to bring the upright direction into coincidence with the average orientation from the most recent 2.5 s of IMU data. Models for HAR were trained using data obtained from a cohort of 37 older adults (83.9 ± 3.4 years) or 20 younger adults (21.9 ± 1.7 years). Test data were generated from the training data by virtually re-orienting the IMU so that it is representative of carrying the phone in five different orientations (relative to the thigh). The overall performance of the model for HAR was consistent whether the model was trained with the data from the younger cohort, and tested with the data from the older cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.782, 0.793]; total class sensitivity 95% confidence interval [84.9%, 85.6%]), or the reciprocal scenario in which the model was trained with the data from the older cohort, and tested with the data from the younger cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.765, 0.784]; total class sensitivity 95% confidence interval [82.3%, 83.7%]).
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Cosco TD, Firth J, Vahia I, Sixsmith A, Torous J. Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities. JMIR Aging 2019; 2:e10019. [PMID: 31518253 PMCID: PMC6715005 DOI: 10.2196/10019] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 11/21/2018] [Accepted: 01/30/2019] [Indexed: 11/17/2022] Open
Abstract
Worldwide, there is an unprecedented and ongoing expansion of both the proportion of older adults in society and innovations in digital technology. This rapidly increasing number of older adults is placing unprecedented demands on health care systems, warranting the development of new solutions. Although advancements in smart devices and wearables present novel methods for monitoring and improving the health of aging populations, older adults are currently the least likely age group to engage with such technologies. In this commentary, we critically examine the potential for technology-driven data collection and analysis mechanisms to improve our capacity to research, understand, and address the implications of an aging population. Alongside unprecedented opportunities to harness these technologies, there are equally unprecedented challenges. Notably, older adults may experience the first-level digital divide, that is, lack of access to technologies, and/or the second-level digital divide, that is, lack of use/skill, alongside issues with data input and analysis. To harness the benefits of these innovative approaches, we must first engage older adults in a meaningful manner and adjust the framework of smart devices to accommodate the unique physiological and psychological characteristics of the aging populace. Through an informed approach to the development of technologies with older adults, the field can leverage innovation to increase the quality and quantity of life for the expanding population of older adults.
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Affiliation(s)
- Theodore D Cosco
- Gerontology Research Center, Simon Fraser University, Vancouver, BC, Canada.,Oxford Institute of Population Ageing, University of Oxford, Oxford, United Kingdom
| | - Joseph Firth
- NICM Health Research Institute, University of Western Sydney, Sydney, Australia.,Division of Psychology and Mental Health, University of Manchester, Manchester, United Kingdom.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Ipsit Vahia
- Harvard Medical School, Boston, MA, United States.,Division of Geriatrics, McLean Hospital, Belmont, MA, United States
| | - Andrew Sixsmith
- STAR Institute, Simon Fraser University, Vancouver, BC, Canada
| | - John Torous
- Harvard Medical School, Boston, MA, United States.,Department of Psychiatry and Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Boston, MA, United States
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Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61⁻70 Years Old. SENSORS 2019; 19:s19030449. [PMID: 30678268 PMCID: PMC6387343 DOI: 10.3390/s19030449] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 01/18/2019] [Accepted: 01/19/2019] [Indexed: 11/16/2022]
Abstract
Assessment of physical performance by standard clinical tests such as the 30-sec Chair Stand (30CST) and the Timed Up and Go (TUG) may allow early detection of functional decline, even in high-functioning populations, and facilitate preventive interventions. Inertial sensors are emerging to obtain instrumented measures that can provide subtle details regarding the quality of the movement while performing such tests. We compared standard clinical with instrumented measures of physical performance in their ability to distinguish between high and very high functional status, stratified by the Late-Life Function and Disability Instrument (LLFDI). We assessed 160 participants from the PreventIT study (66.3 ± 2.4 years, 87 females, median LLFDI 72.31, range: 44.33⁻100) performing the 30CST and TUG while a smartphone was attached to their lower back. The number of 30CST repetitions and the stopwatch-based TUG duration were recorded. Instrumented features were computed from the smartphone embedded inertial sensors. Four logistic regression models were fitted and the Areas Under the Receiver Operating Curve (AUC) were calculated and compared using the DeLong test. Standard clinical and instrumented measures of 30CST both showed equal moderate discriminative ability of 0.68 (95%CI 0.60⁻0.76), p = 0.97. Similarly, for TUG: AUC was 0.68 (95%CI 0.60⁻0.77) and 0.65 (95%CI 0.56⁻0.73), respectively, p = 0.26. In conclusion, both clinical and instrumented measures, recorded through a smartphone, can discriminate early functional decline in healthy adults aged 61⁻70 years.
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De Falco I, De Pietro G, Sannino G. Evaluation of artificial intelligence techniques for the classification of different activities of daily living and falls. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-03973-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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28
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Hsieh KL, Fanning JT, Rogers WA, Wood TA, Sosnoff JJ. A Fall Risk mHealth App for Older Adults: Development and Usability Study. JMIR Aging 2018; 1:e11569. [PMID: 31518234 PMCID: PMC6716481 DOI: 10.2196/11569] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 09/21/2018] [Accepted: 10/14/2018] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Falls are the leading cause of injury-related death in older adults. Due to various constraints, objective fall risk screening is seldom performed in clinical settings. Smartphones offer a high potential to provide fall risk screening for older adults in home settings. However, there is limited understanding of whether smartphone technology for falls screening is usable by older adults who present age-related changes in perceptual, cognitive, and motor capabilities. OBJECTIVE The aims of this study were to develop a fall risk mobile health (mHealth) app and to determine the usability of the fall risk app in healthy, older adults. METHODS A fall risk app was developed that consists of a health history questionnaire and 5 progressively challenging mobility tasks to measure individual fall risk. An iterative design-evaluation process of semistructured interviews was performed to determine the usability of the app on a smartphone and tablet. Participants also completed a Systematic Usability Scale (SUS). In the first round of interviews, 6 older adults participated, and in the second round, 5 older adults participated. Interviews were videotaped and transcribed, and the data were coded to create themes. Average SUS scores were calculated for the smartphone and tablet. RESULTS There were 2 themes identified from the first round of interviews, related to perceived ease of use and perceived usefulness. While instructions for the balance tasks were difficult to understand, participants found it beneficial to learn about their risk for falls, found the app easy to follow, and reported confidence in using the app on their own. Modifications were made to the app, and following the second round of interviews, participants reported high ease of use and usefulness in learning about their risk of falling. Few differences were reported between using a smartphone or tablet. Average SUS scores ranged from 79 to 84. CONCLUSIONS Our fall risk app was found to be highly usable by older adults as reported from interviews and high scores on the SUS. When designing a mHealth app for older adults, developers should include clear and simple instructions and preventative strategies to improve health. Furthermore, if the design accommodates for age-related sensory changes, smartphones can be as effective as tablets. A mobile app to assess fall risk has the potential to be used in home settings by older adults.
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Affiliation(s)
- Katherine L Hsieh
- Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign, Urbana, IL, United States
| | - Jason T Fanning
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Wendy A Rogers
- Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign, Urbana, IL, United States
| | - Tyler A Wood
- Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign, Urbana, IL, United States
| | - Jacob J Sosnoff
- Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign, Urbana, IL, United States
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Iakovakis D, Hadjidimitriou S, Charisis V, Bostantjopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Dias SB, Diniz JA, Trivedi D, Chaudhuri KR, Hadjileontiadis LJ. Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild. ACTA ACUST UNITED AC 2018. [DOI: 10.3389/fict.2018.00028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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30
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Sigcha L, Pavón I, Arezes P, Costa N, De Arcas G, López JM. Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer. SENSORS 2018; 18:s18113805. [PMID: 30404241 PMCID: PMC6263432 DOI: 10.3390/s18113805] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 10/31/2018] [Accepted: 11/01/2018] [Indexed: 01/03/2023]
Abstract
Wearable technology has had a significant growth in the last years; this is particularly true of smartwatches, due to their potential advantages and ease of use. These smart devices integrate sensors that can be potentially used within industrial settings and for several applications, such as safety, monitoring, and the identification of occupational risks. The accelerometer is one of the main sensors integrated into these devices. However, several studies have identified that sensors integrated into smart devices may present inaccuracies during data acquisition, which may influence the performance of their potential applications. This article presents an analysis from the metrological point of view to characterize the amplitude and frequency response of the integrated accelerometers in three currently available commercial smartwatches, and it also includes an analysis of the uncertainties associated with these measurements by adapting the procedures described in several International Organization for Standardization (ISO) standards. The results show that despite the technical limitations produced by the factory configuration, these devices can be used in various applications related to occupational risk assessment. Opportunities for improvement have also been identified, which will allow us to take advantage of this technology in several innovative applications within industrial settings and, in particular, for occupational health purposes.
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Affiliation(s)
- Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain.
| | - Ignacio Pavón
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain.
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal.
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal.
| | - Guillermo De Arcas
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain.
| | - Juan Manuel López
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain.
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Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network. ENERGIES 2018. [DOI: 10.3390/en11112866] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.
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Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018. [DOI: 10.1007/s13369-018-3496-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Rasche P, Mertens A, Brandl C, Liu S, Buecking B, Bliemel C, Horst K, Weber CD, Lichte P, Knobe M. Satisfying Product Features of a Fall Prevention Smartphone App and Potential Users' Willingness to Pay: Web-Based Survey Among Older Adults. JMIR Mhealth Uhealth 2018; 6:e75. [PMID: 29588268 PMCID: PMC5893889 DOI: 10.2196/mhealth.9467] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 02/16/2018] [Accepted: 02/23/2018] [Indexed: 01/15/2023] Open
Abstract
Background Prohibiting falls and fall-related injuries is a major challenge for health care systems worldwide, as a substantial proportion of falls occur in older adults who are previously known to be either frail or at high risk for falls. Hence, preventive measures are needed to educate and minimize the risk for falls rather than just minimize older adults’ fall risk. Health apps have the potential to address this problem, as they enable users to self-assess their individual fall risk. Objective The objective of this study was to identify product features of a fall prevention smartphone app, which increase or decrease users’ satisfaction. In addition, willingness to pay (WTP) was assessed to explore how much revenue such an app could generate. Methods A total of 96 participants completed an open self-selected Web-based survey. Participants answered various questions regarding health status, subjective and objective fall risk, and technical readiness. Seventeen predefined product features of a fall prevention smartphone app were evaluated twice: first, according to a functional (product feature is implemented in the app), and subsequently by a dysfunctional (product feature is not implemented in the app) question. On the basis of the combination of answers from these 2 questions, the product feature was assigned to a certain category (must-be, attractive, one-dimensional, indifferent, or questionable product feature). This method is widely used in user-oriented product development and captures users’ expectations of a product and how their satisfaction is influenced by the availability of individual product features. Results Five product features were identified to increase users’ acceptance, including (1) a checklist of typical tripping hazards, (2) an emergency guideline in case of a fall, (3) description of exercises and integrated workout plans that decrease the risk of falling, (4) inclusion of a continuous workout program, and (5) cost coverage by health insurer. Participants’ WTP was assessed after all 17 product features were rated and revealed a median monthly payment WTP rate of €5.00 (interquartile range 10.00). Conclusions The results show various motivating product features that should be incorporated into a fall prevention smartphone app. Results reveal aspects that fall prevention and intervention designers should keep in mind to encourage individuals to start joining their program and facilitate long-term user engagement, resulting in a greater interest in fall risk prevention.
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Affiliation(s)
- Peter Rasche
- Institute of Industrial Engineering and Ergonomics, Department of Mechanical Engineering, RWTH Aachen University, Aachen, Germany
| | - Alexander Mertens
- Institute of Industrial Engineering and Ergonomics, Department of Mechanical Engineering, RWTH Aachen University, Aachen, Germany
| | - Christopher Brandl
- Institute of Industrial Engineering and Ergonomics, Department of Mechanical Engineering, RWTH Aachen University, Aachen, Germany
| | - Shan Liu
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Benjamin Buecking
- Hand and Reconstructive Surgery, Department of Trauma, University Hospital of Giessen and Marburg, Marburg, Germany
| | - Christopher Bliemel
- Hand and Reconstructive Surgery, Department of Trauma, University Hospital of Giessen and Marburg, Marburg, Germany
| | - Klemens Horst
- Department of Orthopaedic Trauma, University of Aachen Medical Center, RWTH Aachen University, Aachen, Germany
| | - Christian David Weber
- Department of Orthopaedic Trauma, University of Aachen Medical Center, RWTH Aachen University, Aachen, Germany
| | - Philipp Lichte
- Department of Orthopaedic Trauma, University of Aachen Medical Center, RWTH Aachen University, Aachen, Germany
| | - Matthias Knobe
- Department of Orthopaedic Trauma, University of Aachen Medical Center, RWTH Aachen University, Aachen, Germany
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Leach JM, Mellone S, Palumbo P, Bandinelli S, Chiari L. Natural turn measures predict recurrent falls in community-dwelling older adults: a longitudinal cohort study. Sci Rep 2018; 8:4316. [PMID: 29531284 PMCID: PMC5847590 DOI: 10.1038/s41598-018-22492-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 02/20/2018] [Indexed: 11/09/2022] Open
Abstract
Although turning has been reported as one of the leading activities performed during a fall, and falls during turning result in 8-times more hip fractures than falls during linear gait, the quantity and quality of turns resulting in falls remain unknown since turns are rarely assessed during activities of daily living. 160 community-dwelling older adults were monitored for one week using smartphone technology. Turn measures and activity rates were quantified. Fall incidence within 12 months from continuous monitoring defined fall status, with 7/153 prospective fallers/non-fallers. Based on the analysis of 718,582 turns, prospective fallers turned less frequently, took longer to turn, and were less consistent in turn angle (p = 0.007, 0.025, and 0.038, respectively). Prospective fallers also walked slower and spent less time walking and turning and more time engaged in sedentary behavior (p = 0.043, 0.012, and 0.015, respectively). Individuals experiencing decline in the control of gait and/or turning may attempt to reduce their risk of falling by limiting their exposure and implementing cautionary movement strategies while turning. Since there was no difference in the overall active rate between prospective fallers and non-fallers, impaired gait and turning ability, specifically, may attribute to elevated fall risk within this cohort.
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Affiliation(s)
- Julia M Leach
- Personal Health Systems Laboratory, Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Viale Risorgimento, 2, 40136, Bologna, Italy.
| | - Sabato Mellone
- Personal Health Systems Laboratory, Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Viale Risorgimento, 2, 40136, Bologna, Italy
| | - Pierpaolo Palumbo
- Personal Health Systems Laboratory, Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Viale Risorgimento, 2, 40136, Bologna, Italy
| | - Stefania Bandinelli
- Azienda Sanitaria Toscana Centro, Firenze, Piero Palagi Hospital, Viale Michelangelo 41, 50125, Firenze, Italy
| | - Lorenzo Chiari
- Personal Health Systems Laboratory, Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Viale Risorgimento, 2, 40136, Bologna, Italy
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Lapierre N, Neubauer N, Miguel-Cruz A, Rios Rincon A, Liu L, Rousseau J. The state of knowledge on technologies and their use for fall detection: A scoping review. Int J Med Inform 2018; 111:58-71. [DOI: 10.1016/j.ijmedinf.2017.12.015] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 12/06/2017] [Accepted: 12/20/2017] [Indexed: 01/23/2023]
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36
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Hu X, Zhao J, Peng D, Sun Z, Qu X. Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model. SENSORS 2018; 18:s18020421. [PMID: 29389857 PMCID: PMC5855500 DOI: 10.3390/s18020421] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/24/2018] [Accepted: 01/30/2018] [Indexed: 11/26/2022]
Abstract
Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial–lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior–posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly.
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Affiliation(s)
- Xinyao Hu
- Institute of Human Factors and Ergonomics, Shenzhen University, Shenzhen 518060, China.
| | - Jun Zhao
- Institute of Human Factors and Ergonomics, Shenzhen University, Shenzhen 518060, China.
| | - Dongsheng Peng
- Institute of Human Factors and Ergonomics, Shenzhen University, Shenzhen 518060, China.
| | - Zhenglong Sun
- Institute of Robotics and Intelligent Manufacturing, the Chinese University of Hong Kong, Shenzhen 518172, China.
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, Shenzhen University, Shenzhen 518060, China.
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Shawen N, Lonini L, Mummidisetty CK, Shparii I, Albert MV, Kording K, Jayaraman A. Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications. JMIR Mhealth Uhealth 2017; 5:e151. [PMID: 29021127 PMCID: PMC5656773 DOI: 10.2196/mhealth.8201] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 08/08/2017] [Accepted: 08/10/2017] [Indexed: 01/27/2023] Open
Abstract
Background Automatically detecting falls with mobile phones provides an opportunity for rapid response to injuries and better knowledge of what precipitated the fall and its consequences. This is beneficial for populations that are prone to falling, such as people with lower limb amputations. Prior studies have focused on fall detection in able-bodied individuals using data from a laboratory setting. Such approaches may provide a limited ability to detect falls in amputees and in real-world scenarios. Objective The aim was to develop a classifier that uses data from able-bodied individuals to detect falls in individuals with a lower limb amputation, while they freely carry the mobile phone in different locations and during free-living. Methods We obtained 861 simulated indoor and outdoor falls from 10 young control (non-amputee) individuals and 6 individuals with a lower limb amputation. In addition, we recorded a broad database of activities of daily living, including data from three participants’ free-living routines. Sensor readings (accelerometer and gyroscope) from a mobile phone were recorded as participants freely carried it in three common locations—on the waist, in a pocket, and in the hand. A set of 40 features were computed from the sensors data and four classifiers were trained and combined through stacking to detect falls. We compared the performance of two population-specific models, trained and tested on either able-bodied or amputee participants, with that of a model trained on able-bodied participants and tested on amputees. A simple threshold-based classifier was used to benchmark our machine-learning classifier. Results The accuracy of fall detection in amputees for a model trained on control individuals (sensitivity: mean 0.989, 1.96*standard error of the mean [SEM] 0.017; specificity: mean 0.968, SEM 0.025) was not statistically different (P=.69) from that of a model trained on the amputee population (sensitivity: mean 0.984, SEM 0.016; specificity: mean 0.965, SEM 0.022). Detection of falls in control individuals yielded similar results (sensitivity: mean 0.979, SEM 0.022; specificity: mean 0.991, SEM 0.012). A mean 2.2 (SD 1.7) false alarms per day were obtained when evaluating the model (vs mean 122.1, SD 166.1 based on thresholds) on data recorded as participants carried the phone during their daily routine for two or more days. Machine-learning classifiers outperformed the threshold-based one (P<.001). Conclusions A mobile phone-based fall detection model can use data from non-amputee individuals to detect falls in individuals walking with a prosthesis. We successfully detected falls when the mobile phone was carried across multiple locations and without a predetermined orientation. Furthermore, the number of false alarms yielded by the model over a longer period of time was reasonably low. This moves the application of mobile phone-based fall detection systems closer to a real-world use case scenario.
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Affiliation(s)
- Nicholas Shawen
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States
| | - Luca Lonini
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | | | - Ilona Shparii
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States.,Department of Computer Science, Loyola University Chicago, Chicago, IL, United States
| | - Mark V Albert
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States.,Department of Computer Science, Loyola University Chicago, Chicago, IL, United States
| | - Konrad Kording
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States.,Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, United States
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States.,Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, United States
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38
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Increasing fall risk awareness using wearables: A fall risk awareness protocol. J Biomed Inform 2016; 63:184-194. [DOI: 10.1016/j.jbi.2016.08.016] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Revised: 08/12/2016] [Accepted: 08/14/2016] [Indexed: 11/19/2022]
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39
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Advances in Long Term Physical Behaviour Monitoring. BIOMED RESEARCH INTERNATIONAL 2016; 2016:6745760. [PMID: 27148552 PMCID: PMC4842354 DOI: 10.1155/2016/6745760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Accepted: 03/22/2016] [Indexed: 11/17/2022]
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Mobility in Old Age: Capacity Is Not Performance. BIOMED RESEARCH INTERNATIONAL 2016; 2016:3261567. [PMID: 27034932 PMCID: PMC4789440 DOI: 10.1155/2016/3261567] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 01/11/2016] [Accepted: 02/09/2016] [Indexed: 11/18/2022]
Abstract
Background. Outcomes of laboratory-based tests for mobility are often used to infer about older adults' performance in real life; however, it is unclear whether such association exists. We hypothesized that mobility capacity, as measured in the laboratory, and mobility performance, as measured in real life, would be poorly linked. Methods. The sample consisted of 84 older adults (72.5 ± 5.9 years). Capacity was assessed via the iTUG and standard gait parameters (stride length, stride velocity, and cadence). Performance was assessed in real life over a period of 6.95 ± 1.99 days using smartphone technology to calculate following parameters: active and gait time, number of steps, life-space, mean action-range, and maximum action-range. Correlation analyses and stepwise multiple regression analyses were applied. Results. All laboratory measures demonstrated significant associations with the real-life measures (between r = .229 and r = .461). The multiple regression analyses indicated that the laboratory measures accounted for a significant but very low proportion of variance (between 5% and 21%) in real-life measures. Conclusion. In older adults without mobility impairments, capacity-related measures of mobility bear little significance for predicting real-life performance. Hence, other factors play a role in how older people manage their daily-life mobility. This should be considered for diagnosis and treatment of mobility deficits in older people.
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Hamm J, Money AG, Atwal A, Paraskevopoulos I. Fall prevention intervention technologies: A conceptual framework and survey of the state of the art. J Biomed Inform 2016; 59:319-45. [PMID: 26773345 DOI: 10.1016/j.jbi.2015.12.013] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 12/14/2015] [Accepted: 12/20/2015] [Indexed: 11/28/2022]
Abstract
In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.
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Affiliation(s)
- Julian Hamm
- Department of Computer Science, Brunel University London, UK.
| | - Arthur G Money
- Department of Computer Science, Brunel University London, UK.
| | - Anita Atwal
- Department of Clinical Sciences, Brunel University London, UK.
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Tropea P, Martelli D, Aprigliano F, Micera S, Monaco V. Effects of aging and perturbation intensities on temporal parameters during slipping-like perturbations. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5291-4. [PMID: 26737485 DOI: 10.1109/embc.2015.7319585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The aim of this study was to analyze the modifications of temporal parameters during slipping-like perturbations associated both with aging and perturbation intensities. Twelve participants equally distributed from two age groups (elderly and young) were recorded while, during steady locomotion, managing unexpected slipping-like perturbations, in forward direction, at different intensity and amplitude of foot shift. Two metrics were extrapolated from the analysis of the ground reaction force supplied by ad hoc platform aimed at destabilizing the balance control. The results indicated that the analyzed timing variables, both for elderly and young, are strongly modified by intensity of the perturbation, but only slight altered by the amplitude. Concerning the comparison about the two groups, elderly people seem to have slower reactive response than young subjects. These findings support further investigations in order to gain a better understanding of fall dynamics in elderly people.
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Knowles LM, Skeath P, Jia M, Najafi B, Thayer J, Sternberg EM. New and Future Directions in Integrative Medicine Research Methods with a Focus on Aging Populations: A Review. Gerontology 2015; 62:467-76. [DOI: 10.1159/000441494] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 10/06/2015] [Indexed: 11/19/2022] Open
Abstract
This review discusses existing and developing state-of-the-art noninvasive methods for quantifying the effects of integrative medicine (IM) in aging populations. The medical conditions of elderly patients are often more complex than those of younger adults, making the multifaceted approach of IM particularly suitable for aging populations. However, because IM interventions are multidimensional, it has been difficult to examine their effectiveness and mechanisms of action. Optimal assessment of IM intervention effects in the elderly should include a multifaceted approach, utilizing advanced analytic methods to integrate psychological, behavioral, physiological, and biomolecular measures of a patient's response to IM treatment. Research is presented describing methods for collecting and analyzing psychological data; wearable unobtrusive devices for monitoring heart rate variability, activity and other behavioral responses in real time; immunochemical methods for noninvasive molecular biomarker analysis, and considerations and analytical approaches for the integration of these measures. The combination of methods and devices presented in this review will provide new approaches for evaluating the effects of IM interventions in real-life ambulatory settings of older adults, and will extend the concept of mobile health to the domains of IM and healthy aging.
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Ellis RJ, Ng YS, Zhu S, Tan DM, Anderson B, Schlaug G, Wang Y. A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson's Disease. PLoS One 2015; 10:e0141694. [PMID: 26517720 PMCID: PMC4627774 DOI: 10.1371/journal.pone.0141694] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2015] [Accepted: 10/11/2015] [Indexed: 11/18/2022] Open
Abstract
Background A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs. Methods The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously. Results Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes. Conclusion These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient.
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Affiliation(s)
- Robert J. Ellis
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore
| | - Yee Sien Ng
- Department of Rehabilitation Medicine, Singapore General Hospital, Outram Rd, Singapore, 169608, Singapore
| | - Shenggao Zhu
- NUS Graduate School for Integrative Sciences and Engineering, 28 Medical Drive, Singapore, 117456, Singapore
| | - Dawn M. Tan
- Department of Rehabilitation Medicine, Singapore General Hospital, Outram Rd, Singapore, 169608, Singapore
| | - Boyd Anderson
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore
| | - Gottfried Schlaug
- Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Palmer 127, Boston, MA, 02215, United States of America
| | - Ye Wang
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, 28 Medical Drive, Singapore, 117456, Singapore
- * E-mail:
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Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement. SENSORS 2015; 15:18901-33. [PMID: 26263998 PMCID: PMC4570352 DOI: 10.3390/s150818901] [Citation(s) in RCA: 133] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 07/27/2015] [Accepted: 07/28/2015] [Indexed: 01/22/2023]
Abstract
Advances in mobile technology have led to the emergence of the “smartphone”, a new class of device with more advanced connectivity features that have quickly made it a constant presence in our lives. Smartphones are equipped with comparatively advanced computing capabilities, a global positioning system (GPS) receivers, and sensing capabilities (i.e., an inertial measurement unit (IMU) and more recently magnetometer and barometer) which can be found in wearable ambulatory monitors (WAMs). As a result, algorithms initially developed for WAMs that “count” steps (i.e., pedometers); gauge physical activity levels; indirectly estimate energy expenditure and monitor human movement can be utilised on the smartphone. These algorithms may enable clinicians to “close the loop” by prescribing timely interventions to improve or maintain wellbeing in populations who are at risk of falling or suffer from a chronic disease whose progression is linked to a reduction in movement and mobility. The ubiquitous nature of smartphone technology makes it the ideal platform from which human movement can be remotely monitored without the expense of purchasing, and inconvenience of using, a dedicated WAM. In this paper, an overview of the sensors that can be found in the smartphone are presented, followed by a summary of the developments in this field with an emphasis on the evolution of algorithms used to classify human movement. The limitations identified in the literature will be discussed, as well as suggestions about future research directions.
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Casilari E, Luque R, Morón MJ. Analysis of Android Device-Based Solutions for Fall Detection. SENSORS 2015; 15:17827-94. [PMID: 26213928 PMCID: PMC4570297 DOI: 10.3390/s150817827] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 07/14/2015] [Accepted: 07/17/2015] [Indexed: 11/16/2022]
Abstract
Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.
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Affiliation(s)
- Eduardo Casilari
- Departamento de Tecnología Electrónica, ETSI Telecomunicación, Universidad de Málaga, 29071 Málaga, Spain.
| | - Rafael Luque
- Departamento de Tecnología Electrónica, ETSI Telecomunicación, Universidad de Málaga, 29071 Málaga, Spain.
| | - María-José Morón
- Departamento de Tecnología Electrónica, ETSI Telecomunicación, Universidad de Málaga, 29071 Málaga, Spain.
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Cattelani L, Palumbo P, Palmerini L, Bandinelli S, Becker C, Chesani F, Chiari L. FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community. J Med Internet Res 2015; 17:e41. [PMID: 25693419 PMCID: PMC4376110 DOI: 10.2196/jmir.4064] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 01/08/2015] [Accepted: 01/10/2015] [Indexed: 01/22/2023] Open
Abstract
Background About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. Objective The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. Methods FRAT-up is based on the assumption that a subject’s fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. Results The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration. Conclusions FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface. Trial Registration ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study);
http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).
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Affiliation(s)
- Luca Cattelani
- Department of Electrical, Electronic, and Information Engineering - DEI, University of Bologna, Bologna, Italy
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Cui X, Baker JM, Liu N, Reiss AL. Sensitivity of fNIRS measurement to head motion: an applied use of smartphones in the lab. J Neurosci Methods 2015; 245:37-43. [PMID: 25687634 DOI: 10.1016/j.jneumeth.2015.02.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 02/04/2015] [Accepted: 02/06/2015] [Indexed: 11/18/2022]
Abstract
BACKGROUND Powerful computing capabilities in small, easy to use hand-held devices have made smart technologies such as smartphones and tablets ubiquitous in today's society. The capabilities of these devices provide scientists with many tools that can be used to improve the scientific method. METHOD Here, we demonstrate how smartphones may be used to quantify the sensitivity of functional near-infrared spectroscopy (fNIRS) signal to head motion. By attaching a smartphone to participants' heads during the fNIRS scan, we were able to capture data describing the degree of head motion. RESULTS Our results demonstrate that data recorded from an off-the-shelf smartphone accelerometer may be used to identify correlations between head-movement and fNIRS signal change. Furthermore, our results identify correlations between the magnitudes of head-movement and signal artifact, as well as a relationship between the direction of head movement and the location of the resulting signal noise. CONCLUSIONS These data provide a valuable proof-of-concept for the use of off-the-shelf smart technologies in neuroimaging applications.
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Affiliation(s)
- Xu Cui
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine
| | - Joseph M Baker
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine
| | - Ning Liu
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine
| | - Allan L Reiss
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine
- Department of Radiology, Stanford University School of Medicine
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Sprint G, Cook DJ, Weeks DL. Toward Automating Clinical Assessments: A Survey of the Timed Up and Go. IEEE Rev Biomed Eng 2015; 8:64-77. [PMID: 25594979 DOI: 10.1109/rbme.2015.2390646] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Older adults often suffer from functional impairments that affect their ability to perform everyday tasks. To detect the onset and changes in abilities, healthcare professionals administer standardized assessments. Recently, technology has been utilized to complement these clinical assessments to gain a more objective and detailed view of functionality. In the clinic and at home, technology is able to provide more information about patient performance and reduce subjectivity in outcome measures. The timed up and go (TUG) test is one such assessment recently instrumented with technology in several studies, yielding promising results toward the future of automating clinical assessments. Potential benefits of technological TUG implementations include additional performance parameters, generated reports, and the ability to be self-administered in the home. In this paper, we provide an overview of the TUG test and technologies utilized for TUG instrumentation. We then critically review the technological advancements and follow up with an evaluation of the benefits and limitations of each approach. Finally, we analyze the gaps in the implementations and discuss challenges for future research toward automated self-administered assessment in the home.
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Benzinger P, Lindemann U, Becker C, Aminian K, Jamour M, Flick SE. Geriatric rehabilitation after hip fracture. Role of body-fixed sensor measurements of physical activity. Z Gerontol Geriatr 2014; 47:236-42. [PMID: 23780628 DOI: 10.1007/s00391-013-0477-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
BACKGROUND The demand for geriatric rehabilitation will drastically increase over the next years. It will be increasingly important to demonstrate the efficacy and effectiveness of geriatric rehabilitation. One component is the use of objective and valid assessment procedures. These should be understandable to patients, relevant for goal attainment, and able to document change. A number of currently used physical capacity measures have floor effects. The use of body-fixed sensor technology for monitoring physical activity is a possible supplement for the assessment during geriatric rehabilitation to overcome floor effects and directly monitor improvement of mobility as a component of geriatric rehabilitation in many patients. METHODS The observational study with a pre-post design examined 65 consecutive geriatric hip fracture inpatients. Measurements were performed on admission and 2 weeks later. The capacity measures included gait speed, chair rise time, a balance test, 2-Minute-Walk test and the Timed-Up-and-Go test. Physical activity was measured over 9 h using body-fixed sensor technology and expressed as cumulated walking and walking plus standing (time on feet). RESULTS Body-fixed sensors allowed direct measurement of physical activity in all patients available for testing. Cumulated walking and standing (time on feet) increased from a median 83.6 to 102.6 min. Cumulated walking increased from a median 7.0 to 16.3 min. The comparison with the physical capacity measures demonstrated a modest to fair correlation (rs = 0.455 and 0.653). This indicates that physical capacity measures are not the same construct as physical activity. CONCLUSION Body-fixed sensor-based assessment of physical activity was feasible even in geriatric patients with severe mobility problems and decreased the number of patients with missing data both on admission and 2 weeks later. Body-fixed sensor data documented change in activity level.
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Affiliation(s)
- P Benzinger
- Geriatric Rehabilitation , Robert Bosch Krankenhaus, Auerbachstr. 110, 70376, Stuttgart, Germany,
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