Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for developing Human-Machine Interfaces (HMIs) with a natural control, such as intuitive robot interfaces or poly-articulated prostheses. However, real-world applications are limited by reliability problems due to motion artefacts, postural and temporal variability, and sensor re-positioning. This master thesis is the first application of deep learning on the Unibo-INAIL dataset, the first public sEMG dataset exploring the variability between subjects, sessions and arm postures by collecting data over 8 sessions of each of 7 able-bodied subjects executing 6 hand gestures in 4 arm postures. Recent studies address variability with strategies based on training set composition, which improve inter-posture and inter-day generalization of non-deep machine learning classifiers, among which the RBF-kernel SVM yields the highest accuracy. The deep architecture realized in this work is a 1d-CNN inspired by a 2d-CNN reported to perform well on other public benchmark databases. On this 1d-CNN, various training strategies based on training set composition were implemented and tested. Multi-session training proves to yield higher inter-session validation accuracies than single-session training. Two-posture training proves the best postural training (proving the benefit of training on more than one posture) and yields 81.2% inter-posture test accuracy. Five-day training proves the best multi-day training, yielding 75.9% inter-day test accuracy. All results are close to the baseline. Moreover, the results of multi-day training highlight the phenomenon of user adaptation, indicating that training should also prioritize recent data. Though not better than the baseline, the achieved classification accuracies rightfully place the 1d-CNN among the candidates for further research.

翻译：基于表面肌电（sEMG）信号的手势识别是开发具有自然控制能力的人机接口（HMI）的一种有前景的方法，例如直觉式机器人接口或多关节假肢。然而，由于运动伪影、姿势与时间变异性以及传感器重定位导致的可靠性问题，实际应用受到限制。本硕士论文首次将深度学习应用于Unibo-INAIL数据集——这是首个探索受试者、实验阶段和手臂姿势间变异性的公开sEMG数据集，通过采集7名健全受试者在8个实验阶段中执行4种手臂姿势下的6种手势数据。近期研究通过基于训练集组成的策略解决变异性问题，这些策略提升了非深度学习机器学习分类器的跨姿势和跨日泛化能力，其中RBF核SVM取得了最高准确率。本文实现的深度架构是一种受公开基准数据库中表现优异的2d-CNN启发的一维卷积神经网络（1d-CNN）。基于该1d-CNN，实现了多种基于训练集组成的训练策略并进行测试。多会话训练证明比单会话训练能获得更高的跨会话验证准确率。双姿势训练被证明是最优姿势训练策略（证实了在多于一种姿势上训练的优势），并达到81.2%的跨姿势测试准确率。五天训练被证明是最优多日训练策略，达到75.9%的跨日测试准确率。所有结果均接近基线水平。此外，多日训练的结果突显了用户适应现象，表明训练也应优先考虑近期数据。尽管未能超越基线，但所取得的分类准确率已足以将1d-CNN列为后续研究的候选方法之一。