What is it about?
This work is about using heart rate variability signals and users' medical information to predict the amplitude of blood pressure drops due to standing hypotension and quantifying the instantaneous risk of fall. A pilot study was conducted in collaboration with healthy and Parkinson's volunteers to capture the data using postural changing experiment. The information of estimated risk is feeded back to users' smartwatch with vibration and/or audio to notify the user when high risk of fall is detected.
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Why is it important?
The importance of falls prevention is of a high importance in improving the Quality of Life in the elderlies. The exploitation of wearable technology as a means to capture physiological signals and create solutions using Signal Processing and Machine Learning adds value to everyday devices. Especially target groups with high risk of fall such as Parkinson's patients could benefit from this technology.
Perspectives
This work proposes a falls management and prevention system based on smartwatch, beyond the clinical facilities, which can be personalized to different patients' status and is independent from extra wiring or invasive technology. It is software based and can be easily deployed to market's smartwatches which provide rich data about users.
Mr Dimitrios Iakovakis
Aristotle University of Thessaloniki
Read the Original
This page is a summary of: Fuzzy logic-based risk of fall estimation using smartwatch data as a means to form an assistive feedback mechanism in everyday living activities, Healthcare Technology Letters, October 2016, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/htl.2016.0064.
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