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Original Articles

Objective fall risk detection in stroke survivors using wearable sensor technology: a feasibility study

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Pages 393-399 | Received 27 Oct 2014, Accepted 14 Feb 2015, Published online: 15 Mar 2016
 

Abstract

Background: Stroke survivors often have persistent neural deficits related to motor function and sensation, which increase their risk of falling, most of which occurs at home or in community settings. The use of wearable technology to monitor fall risk and gait in stroke survivors may prove useful in enhancing recovery and/or preventing injuries.

Objective: Determine the feasibility of using wearable technology (PAMSys™) to objectively monitor fall risk and gait in home and community settings in stroke survivors.

Methods: In this feasibility study, we used the PAMSys to identify fall risk indicators (postural transitions: duration in seconds, and number of unsuccessful attempts), and gait (steps, speed, duration) for 48 hours during usual daily activities in stroke survivors (n = 10) compared to age-matched controls (n = 10). A questionnaire assessed device acceptability.

Results: Stroke survivors mean age was 70 ± 8 years old, were mainly Caucasian (60%) women (70%), and not significantly different than the age-matched controls (all P-values >0.20). Stroke survivors (100%) reported that the device was comfortable to wear, didn't interfere with everyday activities, and were willing to wear it for another 48 hours. None reported any difficulty with the device while sleeping, removing/putting back on for showering or changing clothes. When compared to controls, stroke survivors had significantly worse fall risk indicators and walked less (P < 0.05).

Conclusion:: Stroke survivors reported high acceptability of 48 hours of continuous PAMSys monitoring. The use of in-home wearable technology may prove useful in monitoring fall risk and gait in stroke survivors, potentially enhancing recovery.

Acknowledgements

The authors would like to thank all of the study participants and our dedicated study staff: Daniella Boros, BS; and Gurtej Grewal, PhD for their help with recruitment and data collection.

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