How Can Affect Be Detected and Represented in Technological Support for Physical Rehabilitation?

Olugbade, Temitayo A., Singh, Aneesha, Bianchi-Berthouze, Nadia, Marquardt, Nicolai, Aung, Min S. H. and Williams, Amanda C. de C. (2019) How Can Affect Be Detected and Represented in Technological Support for Physical Rehabilitation? ACM Transactions on Computer-Human Interaction, 26 (1). pp. 1-29. ISSN 1073-0516

Full text not available from this repository.

Abstract

Although clinical best practice suggests that affect awareness could enable more effective technological support for physical rehabilitation through personalisation to psychological needs, designers need to consider what affective states matter and how they should be tracked and addressed. In this paper, we set the standard by analysing how the major affective factors in chronic pain (pain, fear/anxiety, and low/depressed mood) interfere with everyday physical functioning. Further, based on discussion of the modality that should be used to track these states to enable technology to address them, we investigated the possibility of using movement behaviour to automatically detect the states. Using two body movement datasets on people with chronic pain, we show that movement behaviour enables very good discrimination between two emotional distress levels (F1=0.86), and three pain levels (F1=0.9). Performance remained high (F1=0.78 for two pain levels) with a reduced set of movement sensors. Finally, in an overall discussion, we suggest how technology-provided encouragement and awareness can be personalised given the capability to automatically monitor the relevant states, towards addressing the barriers that they pose. In addition, we highlight movement behaviour features to be tracked to provide technology with information necessary for such personalisation.

Item Type: Article
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 17 Oct 2019 15:30
Last Modified: 22 Apr 2020 08:13
URI: https://ueaeprints.uea.ac.uk/id/eprint/72680
DOI: 10.1145/3310282

Actions (login required)

View Item View Item