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Koh V, Xuan LW, Zhe TK, Singh N, B Matchar D, Chan A. Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review. GeroScience 2024; 46:2951-2975. [PMID: 38436792 PMCID: PMC11009180 DOI: 10.1007/s11357-024-01098-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/09/2024] [Indexed: 03/05/2024] Open
Abstract
Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.
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Affiliation(s)
- Vanessa Koh
- Programme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore.
- Centre for Ageing Research and Education (CARE), Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
| | - Lai Wei Xuan
- Programme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore
| | - Tan Kai Zhe
- Future Health Technologies Programme, Singapore-ETH Centre, Singapore, Singapore
| | - Navrag Singh
- Future Health Technologies Programme, Singapore-ETH Centre, Singapore, Singapore
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - David B Matchar
- Programme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore
- Future Health Technologies Programme, Singapore-ETH Centre, Singapore, Singapore
- Department of Medicine (General Internal Medicine), Duke University Medical Center, Durham, NC, USA
| | - Angelique Chan
- Programme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore
- Centre for Ageing Research and Education (CARE), Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Future Health Technologies Programme, Singapore-ETH Centre, Singapore, Singapore
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2
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Hortobágyi T, Vetrovsky T, Uematsu A, Sanders L, da Silva Costa AA, Batistela RA, Moraes R, Granacher U, Szabó-Kóra S, Csutorás B, Széphelyi K, Tollár J. Walking on a Balance Beam as a New Measure of Dynamic Balance to Predict Falls in Older Adults and Patients with Neurological Conditions. SPORTS MEDICINE - OPEN 2024; 10:59. [PMID: 38775922 PMCID: PMC11111647 DOI: 10.1186/s40798-024-00723-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Beam walking is a new test to estimate dynamic balance. We characterized dynamic balance measured by the distance walked on beams of different widths in five age groups of healthy adults (20, 30, 40, 50, 60 years) and individuals with neurological conditions (i.e., Parkinson, multiple sclerosis, stroke, age: 66.9 years) and determined if beam walking distance predicted prospective falls over 12 months. METHODS Individuals with (n = 97) and without neurological conditions (n = 99, healthy adults, age 20-60) participated in this prospective longitudinal study. Falls analyses over 12 months were conducted. The summed distance walked under single (walking only) and dual-task conditions (walking and serial subtraction by 7 between 300 to 900) on three beams (4, 8, and 12-cm wide) was used in the analyses. Additional functional tests comprised grip strength and the Short Physical Performance Battery. RESULTS Beam walking distance was unaffected on the 12-cm-wide beam in the healthy adult groups. The distance walked on the 8-cm-wide beam decreased by 0.34 m in the 20-year-old group. This reduction was ~ 3 × greater, 1.1 m, in the 60-year-old group. In patients, beam walking distances decreased sharply by 0.8 m on the 8 versus 12 cm beam and by additional 1.6 m on the 4 versus 8 cm beam. Beam walking distance under single and dual-task conditions was linearly but weakly associated with age (R2 = 0.21 for single task, R2 = 0.27 for dual-task). Age, disease, and beam width affected distance walked on the beam. Beam walking distance predicted future falls in the combined population of healthy adults and patients with neurological conditions. Based on receiver operating characteristic curve analyses using data from the entire study population, walking ~ 8.0 of the 12 m maximum on low-lying beams predicted future fallers with reasonable accuracy. CONCLUSION Balance beam walking is a new but worthwhile measure of dynamic balance to predict falls in the combined population of healthy adults and patients with neurological conditions. Future studies are needed to evaluate the predictive capability of beam walking separately in more homogenous populations. Clinical Trial Registration Number NCT03532984.
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Affiliation(s)
- Tibor Hortobágyi
- Department of Neurology, Somogy County Kaposi Mór Teaching Hospital, 7400, Kaposvár, Hungary
- Department of Sport Biology, Institute of Sport Sciences and Physical Education, University of Pécs, 7622, Pécs, Hungary
- Department of Kinesiology, Hungarian University of Sports Science, 1123, Budapest, Hungary
- Center for Human Movement Sciences, Medical Center, University of Groningen, University of Groningen, 9713 AV, Groningen, The Netherlands
- Institute of Sport Research, Sports University of Tirana, Tirana, Albania
| | - Tomas Vetrovsky
- Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic
| | - Azusa Uematsu
- Faculty of Sociology, Otemon Gakuin University, Ibaraki, Osaka, 567-8502, Japan
| | - Lianne Sanders
- Lentis Center for Rehabilitation, Groningen, The Netherlands
| | - Andréia Abud da Silva Costa
- Center for Human Movement Sciences, Medical Center, University of Groningen, University of Groningen, 9713 AV, Groningen, The Netherlands
- Biomechanics and Motor Control Lab, School of Physical Education and Sport of Ribeirao Preto, University of Sao Paulo, Ribeirão Preto, Brazil
- Graduate Program in Rehabilitation and Functional Performance, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Rosangela Alice Batistela
- Biomechanics and Motor Control Lab, School of Physical Education and Sport of Ribeirao Preto, University of Sao Paulo, Ribeirão Preto, Brazil
- Graduate Program in Rehabilitation and Functional Performance, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Renato Moraes
- Biomechanics and Motor Control Lab, School of Physical Education and Sport of Ribeirao Preto, University of Sao Paulo, Ribeirão Preto, Brazil
- Graduate Program in Rehabilitation and Functional Performance, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Urs Granacher
- Department of Sport and Sport Science, Exercise and Human Movement Science, University of Freiburg, Freiburg, Germany.
| | - Szilvia Szabó-Kóra
- Faculty of Health Sciences, Doctoral School of Health Sciences, University of Pécs, 7622, Pécs, Hungary
| | - Bence Csutorás
- Department of Neurology, Somogy County Kaposi Mór Teaching Hospital, 7400, Kaposvár, Hungary
| | - Klaudia Széphelyi
- Faculty of Health Sciences, Doctoral School of Health Sciences, University of Pécs, 7622, Pécs, Hungary
| | - József Tollár
- Department of Neurology, Somogy County Kaposi Mór Teaching Hospital, 7400, Kaposvár, Hungary
- Faculty of Health Sciences, Doctoral School of Health Sciences, University of Pécs, 7622, Pécs, Hungary
- Digital Development Center, Széchenyi István University, 9026, Győr, Hungary
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Pécs Medical School, 7622, Pécs, Hungary
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3
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Fernandez-Bermejo J, Martinez-Del-Rincon J, Dorado J, Toro XD, Santofimia MJ, Lopez JC. Edge Computing Transformers for Fall Detection in Older Adults. Int J Neural Syst 2024; 34:2450026. [PMID: 38490957 DOI: 10.1142/s0129065724500266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold-Based Algorithm (TBA) and a neural network with low number of parameters based on a Transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity, and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the other approach.
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Affiliation(s)
- Jesús Fernandez-Bermejo
- Faculty of Social Science and Information Technology, University of Castilla-La Mancha, 45600 Talavera de la Reina, Toledo, Spain
| | - Jesús Martinez-Del-Rincon
- The Centre for Secure Information Technologies (CSIT), Institute of Electronics, Communications & Information Technology, Queen's University of Belfast, Belfast BT3 9DT, UK
| | - Javier Dorado
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
| | - Xavier Del Toro
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
| | - María J Santofimia
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
| | - Juan C Lopez
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
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4
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Harris C, Tang Y, Birnbaum E, Cherian C, Mendhe D, Chen MH. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies. Arch Clin Neuropsychol 2024; 39:290-304. [PMID: 38520381 DOI: 10.1093/arclin/acae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 03/25/2024] Open
Abstract
Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.
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Affiliation(s)
- Che Harris
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Yingfei Tang
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Eliana Birnbaum
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Christine Cherian
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Dinesh Mendhe
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Michelle H Chen
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
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5
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Mason R, Barry G, Robinson H, O'Callaghan B, Lennon O, Godfrey A, Stuart S. Validity and reliability of the DANU sports system for walking and running gait assessment. Physiol Meas 2023; 44:115001. [PMID: 37852268 DOI: 10.1088/1361-6579/ad04b4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 10/20/2023]
Abstract
Objective. Gait assessments have traditionally been analysed in laboratory settings, but this may not reflect natural gait. Wearable technology may offer an alternative due to its versatility. The purpose of the study was to establish the validity and reliability of temporal gait outcomes calculated by the DANU sports system, against a 3D motion capture reference system.Approach. Forty-one healthy adults (26 M, 15 F, age 36.4 ± 11.8 years) completed a series of overground walking and jogging trials and 60 s treadmill walking and running trials at various speeds (8-14 km hr-1), participants returned for a second testing session to repeat the same testing.Main results. For validity, 1406 steps and 613 trials during overground and across all treadmill trials were analysed respectively. Temporal outcomes generated by the DANU sports system included ground contact time, swing time and stride time all demonstrated excellent agreement compared to the laboratory reference (intraclass correlation coefficient (ICC) > 0.900), aside from ground contact time during overground jogging which had good agreement (ICC = 0.778). For reliability, 666 overground and 511 treadmill trials across all speeds were examined. Test re-test agreement was excellent for all outcomes across treadmill trials (ICC > 0.900), except for swing time during treadmill walking which had good agreement (ICC = 0.886). Overground trials demonstrated moderate to good test re-test agreement (ICC = 0.672-0.750), which may be due to inherent variability of self-selected (rather than treadmill set) pacing between sessions.Significance. Overall, this study showed that temporal gait outcomes from the DANU Sports System had good to excellent validity and moderate to excellent reliability in healthy adults compared to an established laboratory reference.
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Affiliation(s)
- Rachel Mason
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Gillian Barry
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | | | | | | | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcasle upon Tyne, United Kingdom
| | - Samuel Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States of America
- Northumbria Healthcare NHS Foundation Trust, North Shields, United Kingdom
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6
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Zhou L, Fischer E, Brahms CM, Granacher U, Arnrich B. DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units. Sci Data 2023; 10:543. [PMID: 37604913 PMCID: PMC10442385 DOI: 10.1038/s41597-023-02391-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/17/2023] [Indexed: 08/23/2023] Open
Abstract
In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.
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Affiliation(s)
- Lin Zhou
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany.
| | - Eric Fischer
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany
| | - Clemens Markus Brahms
- Division of Training and Movement Sciences, University of Potsdam, 14469, Potsdam, Germany
- Department of Sport and Sport Science, Exercise and Human Movement Science, University of Freiburg, 79102, Freiburg, Germany
| | - Urs Granacher
- Department of Sport and Sport Science, Exercise and Human Movement Science, University of Freiburg, 79102, Freiburg, Germany
| | - Bert Arnrich
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany.
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7
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Seo K, Takayanagi N, Sudo M, Yamashiro Y, Chiba I, Makino K, Lee S, Niki Y, Shimada H. Association between daily gait speed patterns and cognitive impairment in community-dwelling older adults. Sci Rep 2023; 13:2783. [PMID: 36797381 PMCID: PMC9935628 DOI: 10.1038/s41598-023-29805-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/10/2023] [Indexed: 02/18/2023] Open
Abstract
Gait speed over a short distance is associated with cognitive impairment in older adults. Recently, daily gait speed has been assessed using accelerometers. However, because daily gait speed is only weakly correlation with gait speed over a short distance, its association with cognitive impairment needs to be investigated. The present study compared the daily gait speed patterns of normal cognition (NC), mild cognitive impairment (MCI), and general cognitive impairment (GCI) subjects measured every 3 h for two weeks using accelerometers. A total of 1959 participants were classified into the NC (N = 1519), MCI (N = 353), and GCI groups (N = 87). The results showed that the average daily gait speed of the GCI group was significantly lower than that of the NC group (p = 0.03). Furthermore, the average daily gait speeds of the MCI and NC groups were the same. However, the average daily gait speed of the MCI group during a specific time (12-15 o'clock) was significantly lower than that of the NC group (p < 0.01). These results suggest that changes in daily patterns may be detected by measuring daily gait speed, which depends on the degree of cognitive function.
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Affiliation(s)
- Kanako Seo
- Tokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501, Japan.
| | - Naoto Takayanagi
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Motoki Sudo
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Yukari Yamashiro
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Ippei Chiba
- grid.419257.c0000 0004 1791 9005Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka, Obu, Aichi 474-8511 Japan ,grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi 980-8573 Japan
| | - Keitaro Makino
- grid.419257.c0000 0004 1791 9005Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka, Obu, Aichi 474-8511 Japan
| | - Sangyoon Lee
- grid.419257.c0000 0004 1791 9005Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka, Obu, Aichi 474-8511 Japan
| | - Yoshifumi Niki
- grid.419719.30000 0001 0816 944XTokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-Ku, Tokyo, 131-8501 Japan
| | - Hiroyuki Shimada
- grid.419257.c0000 0004 1791 9005Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka, Obu, Aichi 474-8511 Japan
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8
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Bargiotas I, Wang D, Mantilla J, Quijoux F, Moreau A, Vidal C, Barrois R, Nicolai A, Audiffren J, Labourdette C, Bertin-Hugaul F, Oudre L, Buffat S, Yelnik A, Ricard D, Vayatis N, Vidal PP. Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall. J Neurol 2023; 270:618-631. [PMID: 35817988 PMCID: PMC9886639 DOI: 10.1007/s00415-022-11251-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 02/03/2023]
Abstract
Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.
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Affiliation(s)
- Ioannis Bargiotas
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France. .,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.
| | - Danping Wang
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Juan Mantilla
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Flavien Quijoux
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,ORPEA Group, Puteaux, France
| | - Albane Moreau
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Catherine Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Otorhinolaryngology (ENT), AP-HP, Hôpital Universitaire Pitié Salpêtrière, Paris, 75013, France
| | - Remi Barrois
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Alice Nicolai
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Julien Audiffren
- Department of Neuroscience, University of Fribourg, Fribourg, Switzerland
| | - Christophe Labourdette
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | | | - Laurent Oudre
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Stephane Buffat
- Laboratoire d'accidentologie de biomécanique et du comportement des conducteurs, GIE Psa Renault Groupes, Nanterre, France
| | - Alain Yelnik
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Physical and Rehabilitation Medicine (PRM), AP- HP, GH St Louis, Lariboisière, F. Widal, Paris, 75010, France
| | - Damien Ricard
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Neurology, AP-HP, Hôpital d'Instruction des Armées de Percy, Service de Santé des Armées, Clamart, 92140, France.,École d'application du Val-de-Grâce, Service de Santé des Armée, Paris, France
| | - Nicolas Vayatis
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Pierre-Paul Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
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9
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Meyer BM, Tulipani LJ, Gurchiek RD, Allen DA, Solomon AJ, Cheney N, McGinnis RS. Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis. PLOS DIGITAL HEALTH 2022; 1:e0000120. [PMID: 36812538 PMCID: PMC9931255 DOI: 10.1371/journal.pdig.0000120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 09/02/2022] [Indexed: 11/06/2022]
Abstract
Falls are frequent and associated with morbidity in persons with multiple sclerosis (PwMS). Symptoms of MS fluctuate, and standard biannual clinical visits cannot capture these fluctuations. Remote monitoring techniques that leverage wearable sensors have recently emerged as an approach sensitive to disease variability. Previous research has shown that fall risk can be identified from walking data collected by wearable sensors in controlled laboratory conditions however this data may not be generalizable to variable home environments. To investigate fall risk and daily activity performance from remote data, we introduce a new open-source dataset featuring data collected from 38 PwMS, 21 of whom are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from eleven body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients. To demonstrate the utility of these data, we explore the use of free-living walking bouts for characterizing fall risk in PwMS, compare these data to those collected in controlled environments, and examine the impact of bout duration on gait parameters and fall risk estimates. Both gait parameters and fall risk classification performance were found to change with bout duration. Deep learning models outperformed feature-based models using home data; the best performance was observed with all bouts for deep-learning and short bouts for feature-based models when evaluating performance on individual bouts. Overall, short duration free-living walking bouts were found to be the least similar to laboratory walking, longer duration free-living walking bouts provided more significant differences between fallers and non-fallers, and an aggregation of all free-living walking bouts yields the best performance in fall risk classification.
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Affiliation(s)
- Brett M. Meyer
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, Vermont, United States of America
- Department of Biomedical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts, United States of America
| | - Lindsey J. Tulipani
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Reed D. Gurchiek
- Department of Neurological Sciences, Larner College of Medicine at the University of Vermont, Burlington, Vermont, United States of America
| | - Dakota A. Allen
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, Vermont, United States of America
| | - Andrew J. Solomon
- Department of Computer Science, University of Vermont, Burlington, Vermont, United States of America
| | - Nick Cheney
- Department of Biomedical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts, United States of America
| | - Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, Vermont, United States of America
- * E-mail:
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10
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The Effect of Dual-Task Motor-Cognitive Training in Adults with Neurological Diseases Who Are at Risk of Falling. Brain Sci 2022; 12:brainsci12091207. [PMID: 36138943 PMCID: PMC9497151 DOI: 10.3390/brainsci12091207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/30/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022] Open
Abstract
Falls are common in patients with neurological diseases and can be very problematic. Recently, there has been an increase in fall prevention research in people with neurological diseases; however, these studies are usually condition-specific (e.g., only MS, PD or stroke). Here, our aim was to evaluate and compare the efficacy of an advanced and innovative dual-task, motor-cognitive rehabilitation program in individuals with different neurological diseases who are at risk of falling. We recruited 95 consecutive adults with neurological diseases who are at risk of falling and divided them into four groups: 31 with cerebrovascular disease (CVD), 20 with Parkinson’s disease (PD), 23 with traumatic brain injury (TBI) and 21 with other neurological diseases (OND). Each patient completed a dual-task, motor-cognitive training program and underwent two test evaluations to assess balance, gait, fear of falling and walking performance at the pre-and post-intervention. We found that our experimental motor-cognitive, dual-task rehabilitation program was an effective method for improving walking balance, gait, walking endurance and speed, and fear of falling, and that it reduced the risk of falls in patients with different neurological diseases. This study presents an alternative approach for people with chronic neurological diseases and provides innovative data for managing this population.
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11
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Bedtime Monitoring for Fall Detection and Prevention in Older Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127139. [PMID: 35742388 PMCID: PMC9223068 DOI: 10.3390/ijerph19127139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/03/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.
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12
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Regterschot GRH, Ribbers GM, Bussmann JBJ. Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice. SENSORS 2021; 21:s21144744. [PMID: 34300484 PMCID: PMC8309586 DOI: 10.3390/s21144744] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 07/06/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Gerrit Ruben Hendrik Regterschot
- Department of Rehabilitation Medicine, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands; (G.M.R.); (J.B.J.B.)
- Correspondence:
| | - Gerard M. Ribbers
- Department of Rehabilitation Medicine, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands; (G.M.R.); (J.B.J.B.)
- Rijndam Rehabilitation, Westersingel 300, 3015 LJ Rotterdam, The Netherlands
| | - Johannes B. J. Bussmann
- Department of Rehabilitation Medicine, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands; (G.M.R.); (J.B.J.B.)
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13
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Lower-Extremity Intra-Joint Coordination and Its Variability between Fallers and Non-Fallers during Gait. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11062840] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Falling is one of the most common causes of hip fracture and death in older adults. A comparison of the biomechanics of the gait in fallers and non-fallers older adults, especially joint coordination and coordination variability, enables the understanding of mechanisms that underpin falling. Therefore, we compared lower-extremity intra-joint coordination and its variability between fallers and non-fallers older adults during gait. A total of 26 older adults, comprising 13 fallers, took part in this study. The participants walked barefoot at a self-selected speed on a 10-m walkway. Gait kinematics in the dominant leg during 10 cycles were captured with 10 motion tracking cameras at a sampling rate of 100 Hz. Spatiotemporal gait parameters, namely, cadence, walking speed, double support time, stride time, width, and length, as well as intra-joint coordination and coordination variability in the sagittal plane were compared between the two groups. Results showed that fallers walked with significant lower cadence, walking speed, and stride length but greater double support and stride time than non-fallers. Significant differences in the ankle-to-knee, knee-to-hip, and ankle-to-hip coordination patterns between fallers and non-fallers and less coordination variability in fallers compared to non-fallers in some instants of the gait cycles were observed. The differences in spatiotemporal gait parameters in fallers compared to non-fallers may indicate an adaptation resulting from decreased efficiency to decrease the risk of falling. Moreover, the differences in segment coordination and its variability may indicate an inconsistency in neuromuscular control. It may also indicate reduced ability to control the motion of the leg in preparation for foot contact with the ground and the knee and ankle motions during loading response. Finally, such differences may show less control in generating power during the push-off phase in fallers.
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14
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Marques DL, Neiva HP, Pires IM, Zdravevski E, Mihajlov M, Garcia NM, Ruiz-Cárdenas JD, Marinho DA, Marques MC. An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults. SENSORS 2021; 21:s21062050. [PMID: 33803927 PMCID: PMC8000467 DOI: 10.3390/s21062050] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 02/17/2021] [Accepted: 03/11/2021] [Indexed: 12/26/2022]
Abstract
Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.
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Affiliation(s)
- Diogo Luís Marques
- Department of Sport Sciences, University of Beira Interior, 6201-001 Covilhã, Portugal; (D.L.M.); (H.P.N.); (D.A.M.)
| | - Henrique Pereira Neiva
- Department of Sport Sciences, University of Beira Interior, 6201-001 Covilhã, Portugal; (D.L.M.); (H.P.N.); (D.A.M.)
- Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, 6201-001 Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal; (I.M.P.); (N.M.G.)
- Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
- Health Sciences Research Unit: Nursing, School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia;
| | - Martin Mihajlov
- Laboratory for Open Systems and Networks, Jozef Stefan Institute, 1000 Ljubljana, Slovenia;
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal; (I.M.P.); (N.M.G.)
| | - Juan Diego Ruiz-Cárdenas
- Physiotherapy Department, Faculty of Health Sciences, Catholic University of Murcia, 30107 Murcia, Spain;
| | - Daniel Almeida Marinho
- Department of Sport Sciences, University of Beira Interior, 6201-001 Covilhã, Portugal; (D.L.M.); (H.P.N.); (D.A.M.)
- Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, 6201-001 Covilhã, Portugal
| | - Mário Cardoso Marques
- Department of Sport Sciences, University of Beira Interior, 6201-001 Covilhã, Portugal; (D.L.M.); (H.P.N.); (D.A.M.)
- Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, 6201-001 Covilhã, Portugal
- Correspondence:
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15
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Rehman RZU, Zhou Y, Del Din S, Alcock L, Hansen C, Guan Y, Hortobágyi T, Maetzler W, Rochester L, Lamoth CJC. Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6992. [PMID: 33297395 PMCID: PMC7729621 DOI: 10.3390/s20236992] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 11/28/2020] [Accepted: 12/04/2020] [Indexed: 12/17/2022]
Abstract
Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.
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Affiliation(s)
- Rana Zia Ur Rehman
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (L.A.); (L.R.)
| | - Yuhan Zhou
- Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 AV Groningen, The Netherlands; (Y.Z.); (T.H.); (C.J.C.L.)
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (L.A.); (L.R.)
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (L.A.); (L.R.)
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany; (C.H.); (W.M.)
| | - Yu Guan
- School of Computing, Newcastle University, Newcastle Upon Tyne NE4 5TG, UK;
| | - Tibor Hortobágyi
- Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 AV Groningen, The Netherlands; (Y.Z.); (T.H.); (C.J.C.L.)
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany; (C.H.); (W.M.)
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (L.A.); (L.R.)
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - Claudine J. C. Lamoth
- Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 AV Groningen, The Netherlands; (Y.Z.); (T.H.); (C.J.C.L.)
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