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Hand gesture recognition is pivotal in facilitating human–machine interaction within the Internet of Things. Nevertheless, it encounters challenges, including labeling expenses and robustness. To tackle these issues, we propose a semi-supervised learning framework guided by pseudo-label consistency. This framework utilizes a dual-branch structure with a mean-teacher network. Within this setup, a global and locally guided self-supervised learning encoder acts as a feature extractor in a teacher–student network to efficiently extract features, maximizing data utilization to enhance feature representation. Additionally, we introduce a pseudo-label Consistency-Guided Mean-Teacher model, where simulated noise is incorporated to generate newly unlabeled samples for the teacher model before advancing to the subsequent stage. By enforcing consistency constraints between the outputs of the teacher and student models, we alleviate accuracy degradation resulting from individual differences and interference from other body parts, thereby bolstering the network's robustness. Ultimately, the teacher model undergoes refinement through exponential moving averages to achieve stable weights. We evaluate our semi-supervised method on two publicly available hand gesture datasets and compare it with several state-of-the-art fully-supervised algorithms. The results demonstrate the robustness of our method, achieving an accuracy rate exceeding 99% across both datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a reinforcement learning method using a deep residual shrinkage network based on multi-reward priority experience playback for high-frequency and high-dimensional continuous vibration control. Firstly, we keep the underlying equipment unchanged and construct a vibration system simulator using FIR filters to ensure the complete fidelity of the physical model. Then, by interacting with the simulator using our proposed algorithm, we identify the optimal control strategy, which is directly applied to real-world scenarios in the form of a neural network. A multi-reward mechanism is proposed to assist the lightweight network to find a near-optimal control strategy, and a priority experience playback mechanism is used to prioritize the data to accelerate the convergence speed of the neural network and improve the data utilization efficiency. At the same time, the deep residual shrinkage network is introduced to realize adaptive denoising and lightweightness of the neural network. The experimental results indicate that under narrowband white-noise excitation ranging from 0 to 100 Hz, the DDPG algorithm achieved a vibration reduction effect of 12.728 dB, while our algorithm achieved a vibration reduction effect of 20.240 dB. Meanwhile, the network parameters were reduced by more than 7.5 times. [ABSTRACT FROM AUTHOR]