Human Machine Interaction and Meanings of Machine Learning - A Case of Hand Posture Recognition from Wrist-Worn Camera
Trung-Hieu Le, Nguyen Thuy Dung, Dinh Tran Ngoc Huy et al (2021) stated that Hand Gestures Recognition can be supported with IoTs Solution System Design.
Moreover, Hand gestures have been shown to be an efficient way for human-machine interaction. Authors presents a new way to capture hand gestures using the wrist-worn camera. The wrist-worn device is designed as a watch with an integrated camera that is much easier and comfortable to wear in daily life context. Hieu, L.T et al (2019) specified that Hand gestures for human machine interaction using wearable sensors have more potentiality than ambient sensing thanks to its low-cost, light weight and mostly scalablity every where at anytime. Despite the fact of existing works on human hand gestures using wearable sensors, each focuses on a specific application and difficult to be generalized.. Experimental results show that with limited camera angles, the postures are highly distinctive and easily discriminated with the highest performance of 98.85% and 97.40% in terms of precision and recall, which motivates a wide range of applications and new research directions for human-machine interaction, wearables, the Internet of Things (IoT) and so on.