Fusion of Multimodal Sensor Data for Effective Human Action Recognition in the Service of Medical Platforms.

In what has arguably been one of the most troubling periods of recent medical history, with a global pandemic emphasising the importance of staying healthy, innovative tools that shelter patient well-being gain momentum. In that view, a framework is proposed that leverages multimodal data, namely inertial and depth sensor-originating data, can be integrated in health care-oriented platforms, and tackles the crucial task of human action recognition (HAR). To analyse person movement and consequently assess the patient’s condition, an effective methodology is presented that is two-fold: initially, Kinect-based action representations are constructed from handcrafted 3DHOG depth features and the descriptive power of a Fisher encoding scheme. This is complemented by wearable sensor data analysis, using time domain features and then boosted by exploring fusion strategies of minimum expense. Finally, an extended experimental process reveals competitive results in a well-known benchmark dataset and indicates the applicability of our methodology for HAR.