Drinking gesture spotting and identification using single wrist-worn inertial sensor

Stroke is the primary cause of serious long-Term disability in the world. A long period of a rehabilitation program is required for those patients with the function loss of upper limb motor. In order to track the progression of the rehabilitation, the approaches to assessment of upper limb performance is the important task to evaluate the effectiveness of therapies. However, the typical assessment approaches suffer some issues, such as subjective, time-consuming, human resource limitation. In this works, we develop the drinking activity monitoring system using wrist-worn inertial sensor for performance assessment of upper-limb movement. Such drinking activity monitoring system can support clinical profession to keep track of the progress and provide the adequate assistance for the patients. In the proposed drinking gesture monitoring system, the drinking gesture spotting model is proposed to observe the drinking gesture during daily living. The rule-based transition detection (RTD) model is proposed for identification of elementary motions including extension and flexion. The proposed drinking activity monitoring system have the 92% and 90% in accuracy for drinking gesture spotting and transition detection, respectively. Such results show that the proposed drinking activity monitoring using single wrist-worn sensor is reliable.