Fuzzy logic-based risk of fall estimation using smartwatch data as a means to form an assistive feedback mechanism in everyday living activities

Dimitrios E Iakovakis; Fotini A Papadopoulou; Leontios J Hadjileontiadis

doi:10.1049/htl.2016.0064

Fuzzy logic-based risk of fall estimation using smartwatch data as a means to form an assistive feedback mechanism in everyday living activities

Healthc Technol Lett. 2016 Nov 30;3(4):263-268. doi: 10.1049/htl.2016.0064. eCollection 2016 Dec.

Authors

Dimitrios E Iakovakis¹, Fotini A Papadopoulou², Leontios J Hadjileontiadis³

Affiliations

¹ Department of Electrical and Computer Engineering , Aristotle University of Thessaloniki , GR 54124 Thessaloniki , Greece.
² Department of Automation Engineering , Alexander Technological Educational Institute , Sindos GR 57400, Thessaloniki , Greece.
³ Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece; Department of Electrical and Computer Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, UAE.

Abstract

This Letter aims to create a fuzzy logic-based assistive prevention tool for falls, based on accessible sensory technology, such as smartwatch, resulting in monitoring of the risk factors of falls caused by orthostatic hypotension (OH); a drop in systolic blood pressure (DSBP) >20 mmHg due to postural changes. Epidemiological studies have shown that OH is a high risk factor for falls and has a strong impact in quality of life (QoL) of the elderly's, especially for some cases such as Parkinsonians. Based on smartwatch data, it is explored here how statistical features of heart rate variability (HRV) can lead to DSBP prediction and estimation of the risk of fall. In this vein, a pilot study was conducted in collaboration with five Greek Parkinson's Foundation patients and ten healthy volunteers. Taking into consideration, the estimated DSBP and additional statistics of the user's medical/behavioural history, a fuzzy logic inference system was developed, to estimate the instantaneous risk of fall. The latter is fed back to the user with a mechanism chosen by him/her (i.e. vibration and/or sound), to prevent a possible fall, and also sent to the attentive carers and/or healthcare professionals for a home-based monitoring beyond the clinic. The proposed approach paves the way for effective exploitation of the contribution of smartwatch data, such as HRV, in the sustain of QoL in everyday living activities.

Keywords: Parkinsonians; assisted living; assistive feedback mechanism; diseases; everyday living activities; fuzzy logic; fuzzy logic based fall risk estimation; fuzzy logic inference system; heart rate variability; orthostatic hypotension; patient monitoring; quality of life; smart phones; smartwatch data; systolic blood pressure drop.