Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study |
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| Institution: | 1. Scientific Direction, National Institute of Health and Science on Aging – I.N.R.C.A., Ancona, Italy;2. Center for Study of Movement, Cognition and Mobility, Department of Neurology, Tel Aviv Sourasky Medical Center;3. Rush Alzheimer’s Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center;4. Sagol School of Neuroscience and Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University;5. General Practice, School of Medicine, N.U.I. Galway, Galway, Ireland;6. Spring Techno GmbH & Co. KG, Bremen, Germany;7. Geriatrics and Geriatric Emergency Care, National Institute of Health and Science on Aging – I.N.R.C.A., Ancona, Italy;1. Australian Catholic University, School of Exercise Science, Banyo, Queensland, Australia;2. University of Portsmouth, Department of Sport and Exercise Science, Hampshire, United Kingdom;3. Asia-Pacific Centre for Neuromodulation, Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia;1. Institut National de la Santé et de la Recherche Médicale (INSERM), Unité 1093, Cognition Action et Plasticité sensori-motrice, BP 27877, F-21078 Dijon, France;2. Hôpital de Jour de Champmaillot, Pôle gérontologique, CHU Dijon, 2 rue Jules Violle, 21000 Dijon, France;1. National Institute of Occupational Health, Department of Work Psychology and Physiology, Oslo, Norway;2. Technische Universität Braunschweig, Department of Mechanical Engineering, Braunschweig, Germany;1. Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands;2. Department of Radiology, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands;3. Department of Neuroscience, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands;4. Department of Neurology, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands;1. Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238, USA;2. Department of Physical Therapy, University of Dayton,300 College Park, Fitz Hall 207, Dayton, OH, 45469-2925, USA;3. Department of Mathematics, University of Dayton, 300 College Park, Dayton, OH, 45469-2316, USA;1. Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland;2. Centre Hospitalier Universitaire Vaudois (CHUV), Service de gériatrie et réadaptation gériatrique, 1011 Lausanne, Switzerland |
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| Abstract: | Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting. |
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| Keywords: | Fall risk Gait analysis Insole Pressure sensors Self-learning analysis algorithms Pattern analysis |
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