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.
|Title of host publication||Irish Signals & Systems Conference 2021|
|Number of pages||7|
|Publication status||Accepted/In press - 30 Apr 2021|