Pattern matching techniques to automatically detect standardised functional tests from wearable sensors: -

Vini Vijayan , James Connolly, Joan Condell, Philip Gardiner, Nigel McKelvey

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

Wearable sensor technology has steadily grown in availability within a wide variety of well-established consumer and medical devices. Wearable sensors have been used in many health care applications for monitoring the patient at home and throughout their rehabilitation. Data collected from wearable sensors allow monitoring of patient recovery during rehabilitation, and can assist clinicians in their diagnosis. Activities of Daily Living (ADL) is considered as an assessment criterion for various disease conditions. Wearable devices enable collection of information associated with different standard functional tests that measure ADL. In an ambulatory monitoring setting, the volume of data collected by wearable sensors can become complex and difficult to process. Extraction of standardised functional tests can be laboursome, and often fraught with misclassification of movement. Hence it is difficult to analyse and make conclusions/predictions from movement datasets using manual assessment techniques. This paper examines whether standard functional tests can be automatically detected and extracted from wearable sensor data using Artificial Intelligence (AI) techniques.
Original languageEnglish
Title of host publicationIrish Signals & Systems Conference 2021
Chapter-
Pages1-7
Number of pages7
Publication statusAccepted/In press - 30 Apr 2021

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