Tracking Hand Hygiene Gestures with Leap Motion Controller
This work addresses hand hygiene monitoring for healthcare or biomedical applications, but it is incremental as it builds on existing gesture tracking methods with a focus on a specific domain.
The paper tackled the problem of tracking hand hygiene gestures by using the Leap Motion Controller to detect and classify hand movements according to WHO stages, achieving detection of a specific stage (Rub hands Palm to Palm) with accurate positional data but noting occlusion issues when hands are in contact.
The process of hand washing, according to the WHO, is divided into stages with clearly defined two handed dynamic gestures. In this paper, videos of hand washing experts are segmented and analyzed with the goal of extracting their corresponding features. These features can be further processed in software to classify particular hand movements, determine whether the stages have been successfully completed by the user and also assess the quality of washing. Having identified the important features, a 3D gesture tracker, the Leap Motion Controller (LEAP), was used to track and detect the hand features associated with these stages. With the help of sequential programming and threshold values, the hand features were combined together to detect the initiation and completion of a sample WHO Stage 2 (Rub hands Palm to Palm). The LEAP provides accurate raw positional data for tracking single hand gestures and two hands in separation but suffers from occlusion when hands are in contact. Other than hand hygiene the approaches shown here can be applied in other biomedical applications requiring close hand gesture analysis.