Virtual Reality as Assessment Tool of the Risk of Falls in the Elderly

Clanché, Fabien; Personeni, Gabin; Renaux, Alexandre; Muhla, Frédéric; Bastogne, Thierry; Gauchard, Gérome C.

doi:10.11648/j.ijssn.20231101.12

Cited by 3 publications

(1 citation statement)

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“…The head-mounted display (HMD) was used along with motion sensors to record kinematic data during the tests. The results indicated that the participants who were at high risk of falls took longer execution time for interventions and a number of steps [50]. Garcia introduced a VR tool to assess the risk of falls in the elderly.…”

Section: Related Workmentioning

confidence: 99%

A Virtual Reality-Based Simulation Tool for Assessing the Risk of Falls in Older Adults

Ahmad,

Gouveia,

Bermúdez i Badia

2024

Preprint

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Falls are considered a significant cause of disability, pain, and premature deaths in older adults, often due to sedentary lifestyles and various risk factors. Combining immersive virtual reality (IVR) with physical exercise, or exergames, enhances motivation and personalizes training, effectively preventing falls by improving strength and balance in older people. IVR technology may increase the ecological validity of the assessments. The main goal of our study was to assess the feasibility of using a KAVE-based VR platform combining simulations of Levadas and a Cable Car to perform a balanced assessment and profiling of the older adult population for high risk-of-falls and the related user experience. A VR-based platform using a Wii balance board and a CAVE was developed to assess balance and physical fitness. Validated by the Biodex Balance System (BBS), 25 older adults participated in this study. The usability and presence were measured through the System Usability Scale and ITC-SOPI questionnaires, respectively. The IVR system showed a high presence and a good usability score of 75. Significant effects were found in the maximum excursion of the Centre of Pressure (COP) on the anterior-posterior axis during the Cable Car Simulation (CCS), correlating with BBS metrics. Multiple discriminative analysis models and the support vector machine classified fall risk with moderate to high accuracy, precision, and recall. The system accurately identified all high-risk participants using the Leave-One-Out method. This study suggests that an IVR-based platform based on simulations with high ecological validity can be used to assess physical fitness and identify individuals at a higher risk of falls.

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Section: Related Workmentioning

confidence: 99%