SUMMARY In order to improve the training efficiency and establish a multi-person cooperative training simulation system, including “virtual human,” in the process of virtual reality-based astronaut training, it is necessary to plan the velocity at which astronauts carry the target object. A velocity planning algorithm, combining a traditional six-stage acceleration/deceleration algorithm, based on a time-discrete model with high-order dynamic constraints, considering the elastic damping torque of the space suit, is proposed. The described algorithm is verified on MATLAB to prove its feasibility. Compared to other algorithms, the planning time of the proposed algorithm is significantly reduced.

Sensor‐based gait recognition has recently been one of the most challenging task in personal identity verification. But the variations of sensor location would affect the quality of collected data, which results in the instability of recognition performance. To tackle with this, this paper proposes a unified discriminative Auto‐Encoder (AE) framework to directly extract discriminative features under different sensor locations, and simplifies the AE‐based framework at the same time. Firstly, we change traditional mean square error of AE into spectral angle distance to keep the geometry of data; Secondly, similar identity code is introduce to extract discriminative features; Moreover, a scatterness regularization is added to ensure the dispersion of the same class. The experimental results show the superiority of the proposed method over other state‐of‐the‐art methods. This paper proposes a unified autoencoder framework for gait recognition based on similarity identity code, which not only reduces the impact of different sensor location, but also simplifies the original progressive autoencoder scheme. The framework includes four steps: sensor‐based data collection, preprocessing, unified autoencoder algorithm and SVM recognition.

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The human-computer interaction has been widely used in many fields, such intelligent prosthetic control, sports medicine, rehabilitation medicine, and clinical medicine. It has gradually become a research focus of social scientists. In the field of intelligent prosthesis, sEMG signal has become the most widely used control signal source because it is easy to obtain. The off-line sEMG control intelligent prosthesis needs to recognize the gestures to execute associated action. In order solve this issue, this paper adopts a CNN plus BiLSTM to automatically extract sEMG features and recognize the gestures. The CNN plus BiLSTM can overcome the drawbacks in the manual feature extraction methods. The experimental results show that the proposed gesture recognition framework can extract overall gesture features, which can improve the recognition rate.