Detection of collaborative activity with Kinect depth cameras
This addresses health monitoring for elderly individuals by enabling detection of social interactions, but it is incremental as a preliminary approach with noted limitations.
The paper tackled the problem of monitoring elderly health by detecting collaborative activities in home environments using Kinect depth cameras, achieving validation through a compressed 24-hour scenario but noting the need for artifact removal to improve specificity and sensitivity.
The health status of elderly subjects is highly correlated to their activities together with their social interactions. Thus, the long term monitoring in home of their health status, shall also address the analysis of collaborative activities. This paper proposes a preliminary approach of such a system which can detect the simultaneous presence of several subjects in a common area using Kinect depth cameras. Most areas in home being dedicated to specific tasks, the localization enables the classification of tasks, whether collaborative or not. A scenario of a 24 hours day shrunk into 24 minutes was used to validate our approach. It pointed out the need of artifacts removal to reach high specificity and good sensitivity.