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Kinect-based choice reaching and stepping reaction time tests for clinical and in-home assessment of fall risk in older people: a prospective study
Authors:Email author" target="_blank">Andreas?EjupiEmail author  Yves?J?Gschwind  Matthew?Brodie  Wolfgang?L?Zagler  Stephen?R?Lord  Kim?Delbaere
Institution:1.Assistive Healthcare Information Technology Group,Austrian Institute of Technology,Vienna,Austria;2.Vienna University of Technology,Vienna,Austria;3.Neuroscience Research Australia,University of New South Wales,Sydney,Australia
Abstract:

Background

Quick protective reactions such as reaching or stepping are important to avoid a fall or minimize injuries. We developed Kinect-based choice reaching and stepping reaction time tests (Kinect-based CRTs) and evaluated their ability to differentiate between older fallers and non-fallers and the feasibility of administering them at home.

Methods

A total of 94 community-dwelling older people were assessed on the Kinect-based CRTs in the laboratory and were followed-up for falls for 6 months. Additionally, a subgroup (n?=?20) conducted the Kinect-based CRTs at home. Signal processing algorithms were developed to extract features for reaction, movement and the total time from the Kinect skeleton data.

Results

Nineteen participants (20.2 %) reported a fall in the 6 months following the assessment. The reaction time (fallers: 797?±?136 ms, non-fallers: 714?±?89 ms), movement time (fallers: 392?±?50 ms, non-fallers: 358?±?51 ms) and total time (fallers: 1189?±?170 ms, non-fallers: 1072?±?109 ms) of the reaching reaction time test differentiated well between the fallers and non-fallers. The stepping reaction time test did not significantly discriminate between the two groups in the prospective study. The correlations between the laboratory and in-home assessments were 0.689 for the reaching reaction time and 0.860 for stepping reaction time.

Conclusion

The study findings indicate that the Kinect-based CRT tests are feasible to administer in clinical and in-home settings, and thus represents an important step towards the development of sensor-based fall risk self-assessments. With further validation, the assessments may prove useful as a fall risk screen and home-based assessment measures for monitoring changes over time and effects of fall prevention interventions.
Keywords:
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