Abstract
Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information to physicians to diagnose changes in mobility and physical performance at an early stage and increases the chances of rehabilitation. This research introduces an approach for predicting the score of the Timed Up & Go test and Short-Physical-Performance-Battery assessment using IMU data and deep neural networks. The approach is validated on real-world data of a cohort of 20 frail or (pre-) frail older adults of an average of 84.7 years. The deep neural networks achieve an accuracy of about 95% for both tests for participants known by the network.
Highlights
With the demographic change a lot of challenges arise, in the fields of medicine and healthcare
The approach is validated on real-world data of a cohort of 20 frail or frail older adults of an average of
The results showed that the models were performing well on all classes on known participants, but significantly worse on unknown participants
Summary
With the demographic change a lot of challenges arise, in the fields of medicine and healthcare. Older adults need frequent medical attention for maintaining their health and physical performance. Standardised geriatrics assessments are used for monitoring the physical performance and estimating the risk of falling of older adults. Long-term monitoring on a regular basis assessments exceed the logistic capacities of medical professionals. Another disadvantage of assessments is the test situation itself, because people tend to give their best effort in test situations and studies showed that capacity is not performance [2,3,4]. Sensor assisted monitoring of the mobility and the physical performance in everyday life situations can give valuable information to health professionals for diagnosis and therapy. IMU sensors are low-cost and unobtrusive sensors for measuring the body’s specific force and angular acceleration. IMUs are not dependent on external infrastructure like satellites and can be used inside as well as outside without any loss of accuracy
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