Citation

Iakovakis DE, Papadopoulou FA, Hadjileontiadis LJ. Healthc. Technol. Lett. 2016; 3(4): 263-268.

Affiliation

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.

Copyright

(Copyright © 2016, Institution of Engineering and Technology)

DOI

10.1049/htl.2016.0064

PMID

28008361

PMCID

PMC5168800

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.

Language: en