What is it about?
With the rapid development of deep learning and computing power, human–computer interactions, and interfaces are attracting attentions in industrial and academic research. The research of gesture recognition is emerging and provides a new way of studying the human–computer interactions. However, compared with the entire human body, human hands are dexterous organs with more complex and flexible joints, which makes hand gesture recognition a challenging problem. Here, a robust and cost-effective gesture recognition system is reported through the soft optoelectronic sensors. An array of polymer-encapsulated U-shaped microfiber (UMF) attached to a glove is fabricated for sensitive finger motion detection. A deep learning network (VGGNet) is developed to process the optical signals for analyzing and classifying hand gestures. The experiments show that VGGNet has high recognition accuracy of 99.2% for the test datasets with ten classified gestures.
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Why is it important?
This work provides a potential optical interface in studying gesture recognition and biomechanical signatures, which can also be applied in virtual reality systems and interactive game platforms.
Perspectives
In future work, it is promising to realize more complex gesture recognition by increasing the number of optical sensors and using the neural networks with more complex architectures. Moreover, the correlation between different fingers can be added to the training process of neural networks as prior knowledge to enhance the accuracy of networks, for example, in the form of the relation model or matrix between five fingers.
Jin-hui Chen
Xiamen University
Read the Original
This page is a summary of: Soft Optoelectronic Sensors with Deep Learning for Gesture Recognition, Advanced Materials Technologies, July 2022, Wiley,
DOI: 10.1002/admt.202101698.
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