The implementation of surface electromyography (sEMG)-based hand gesture recognition on mobile and wearable systems is frequently restricted by the finite computing, memory, and battery capabilities of edge devices. Even though a low-density sEMG setup is a feasible hardware implementation, achieving robust recognition under such constraint conditions becomes very challenging due to the non-stationary nature and inter-subject variance. In this paper, we propose DSCAttenEMG, an efficient neural network that combines both Depthwise Separable Convolution (DSC) for local feature extraction and Multi-Head Self-Attention (MHSA) to model long-range dependencies on EMG/IMU data, using 1× 1 DSC followed by Global Average Pooling to replace high-dimensional fully connected layers. Extensive experimentation on a self-collected dataset, the public SeNic and BandMyo datasets shows that our approach achieves state-of-the-art recognition performance (94.45%, 94.11% and 92.89%) at negligible complexity (only 178–179K parameters). The model is capable of real-time inference (0.93ms on RTX 4090 GPU, 6.68ms on NVIDIA Jetson AGX Orin, 1.4/0.7ms on CPU/NPU of Qualcomm mobile platform) and has a high degree of practicality for embedded deployment (118 samples/s at$\sim$1 W on K230 edge AI platform). This amalgamation of three pivotal strengths, elevated accuracy, enhanced efficiency, and pragmatic viability, highlights its substantial potential for practical mobile and wearable applications.

This paper demonstrates that singular configuration of a two-link robot arm is advantageous in doing the task of pulling or lifting up a heavy object with small joint torques. As a result of numerical optimization, the initial configuration that minimizes the joint torques necessary for the task is close to the singular configuration where the arm is stretched out. The singular configuration has the dynamic feature that the joint angle vector can be accelerated in a certain direction almost independently of the mass of the object. Then, the joint torques can generate large kinetic energy efficiently at the singular configuration, and the energy can be utilized to pull or lift up the object. The experimental results show that the dynamic feature is practically useful, even when a feedback control is applied in order to make the object follow a planned trajectory.