Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset

Pham, Van-Tien; Tran, Thanh-Hai; Vu, Hai

doi:10.1109/rivf51545.2021.9642078

Cited by 2 publications

(1 citation statement)

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“…Using wearable cameras is an alternative solution. Some works proposed using a camera mounted on the head or the chest of the subject [9], [10]. We argue that such mounting positions are not comfortable for daily use.…”

Section: Introductionmentioning

confidence: 93%

Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction

Nguyêñ

Tran³

et al. 2023

IEEE Access

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In this work, we study the ability to use hand gestures for human-machine interaction from wrist-worn sensors. Towards this goal, we design a wrist-worn prototype to capture RGB video stream of hand gestures. Then we built a new wrist-worn gesture dataset (named WiGes) with various subjects in interaction with home appliances in different environments. To the best of our knowledge, this is the first benchmark released for studying hand gestures from a wrist-worn camera. We then evaluate various CNN models for vision-based recognition. Furthermore, we deeply analyze the models that produce the best trade-off between accuracy, memory requirement, and computational cost. We point out that among studied architectures, MoviNet produces the highest accuracy. Then, we introduce a new MoviNet-based two-stream architecture that takes both RGB and optical flow into account. Our proposed architecture increases the Top-1 accuracy by 1.36% and 3.67% according to two evaluation protocols. Our dataset, baselines, and proposed model analysis give instructive recommendations for human-machine interaction using hand-held devices.INDEX TERMS Convolutional neural network, hand gesture recognition, human-machine interaction, wearable sensors.

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Section: Introductionmentioning

confidence: 93%