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Investigating the feasibility of using wearable technologies and machine learning techniques to recognise distress and agitation in people living with dementia
Author(s) -
Steer Zeke,
Pipe Anthony,
Battle Steve,
Cheston Richard
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.039705
Subject(s) - distress , wearable computer , feeling , dementia , psychology , applied psychology , affect (linguistics) , wearable technology , clinical psychology , medicine , computer science , social psychology , communication , disease , pathology , embedded system
Background People in the later stages of dementia often express their needs and distress through their behaviour in ways that challenge care staff to respond appropriately. Technological solutions may help staff to recognise this distress at an early stage and take preventative action. The aim of this project is to develop and test the feasibility of using wearable technologies and machine learning algorithms to identify stress and agitation. Method Two laboratory‐based feasibility studies were undertaken with healthy adult volunteers. In study 1, 20 participants took part in a computer‐based exercise designed to elicit stress. In study 2, 20 participants completed eight different physical activities which a person living with dementia might undertake during a typical day, plus two additional activities which were designed to be positively‐ and negatively‐stimulating. In both studies, participants’ physiological responses were recorded using wearables – in study 1, using commercially‐available devices worn around the wrist and waist, and in study 2, using an additional device developed by the researcher, worn in a sock. Participants also provided information on their affective states – in study 1 they rated their stress, and in study 2 they completed the Positive and Negative Affect Schedule (PANAS) before and after each activity. Participants’ responses were used to train and evaluate different binary classifiers for their ability to recognise reported stress in the physiological data. Results In study 1, the K‐nearest neighbours algorithm and an ensemble classifier called bagged trees recognised periods when participants reported feeling most stressed with accuracy rates exceeding 90% (98% sensitivity, >99% specificity), using five‐fold validation. However, in study 2 which used a different group of participants, the algorithms trained in study 1 failed to reliably identify the negatively‐stimulating activity from the other activities. Conclusion Technological solutions can facilitate a proactive approach to the management of stress and agitation in dementia. However, for these kinds of solutions to be effective in practice, they must be able to generalise to new users and different environments including uncontrolled environments. These kinds of solutions also raise ethical considerations, such as privacy and data security, which are particularly important when users may lack capacity.