Name
Title | ||
|---|---|---|
How Can Affect Be Detected and Represented in Technological Support for Physical Rehabilitation? |
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 article, 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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3299095 | ACM Transactions on Computer-Human Interaction (TOCHI) |
Keywords | Field | DocType |
Chronic pain, affective computing, affective interaction, physical rehabilitation | Chronic pain,Mood,Distress,Best practice,Computer science,Anxiety,Physical therapy,Affective computing,Affect (psychology),Personalization | Journal |
Volume | Issue | ISSN |
26 | 1 | 1073-0516 |
Citations | PageRank | References |
6 | 0.74 | 29 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Temitayo A. Olugbade | 1 | 44 | 6.14 |
| Aneesha Singh | 2 | 100 | 11.76 |
| Nadia Bianchi-Berthouze | 3 | 1239 | 98.61 |
| Nicolai Marquardt | 4 | 30 | 2.42 |
| Hane Aung | 5 | 89 | 6.81 |
| Amanda Williams | 6 | 126 | 11.24 |