Wei, Xijia;
Olugbade, Temitayo;
Shi, Fangzhan;
Wu, Shuang;
Williams, Amanda;
Gold, Nicholas;
Cho, Youngjun;
... Bianchi-Berthouze, Nadia; + view all
(2023)
Leveraging WiFi Sensing Toward Automatic Recognition of Pain Behaviors.
In:
(Proceedings) Affective Computing and Intelligent Interaction (ACII2023).
IEEE
(In press).
Preview |
Text
Wei_WiFi_Sensing_CP_PB_Detection_ACII2023.pdf Download (1MB) | Preview |
Abstract
WiFi sensing has been well explored for recognizing human activity types. However, research is limited in the possibility of its use in identifying affective expressions such as behaviors associated with pain experience. As critical groundwork, we investigated the use of channel state information from WiFi devices for capturing speed and lateral asymmetry attributes of physical activity. These two attributes are body movement qualities associated with hesitation and guarding, respectively, which are pain behaviors that are valuable to address in physical rehabilitation for people with chronic pain. We obtained mean F1 scores of 0.92 and 0.90 for automatic detection of movement speed levels and lateral asymmetry. These findings suggest that WiFi sensors could be a valuable alternative or supplement to traditional motion capture systems, for unobtrusive, continuous evaluation for hesitation and guarding behaviors in everyday physical activity in the home.
| Type: | Proceedings paper |
|---|---|
| Title: | Leveraging WiFi Sensing Toward Automatic Recognition of Pain Behaviors |
| Event: | Affective Computing and Intelligent Interaction (ACII2023) |
| Location: | MIT Media Lab, MA, USA |
| Dates: | 10 Sep 2023 - 13 Sep 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ieeexplore.ieee.org/xpl/conhome/1002992/al... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Affect recognition, chronic pain, pain behavior, wireless sensing, machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174958 |
Archive Staff Only
 |
View Item |
