Thesis
Deep learning and sEMG signals based human hand gesture recognition
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2026
- Thesis identifier
- T17648
- Person Identifier (Local)
- 201777615
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Hand prosthesis have helped amputees to partially live normal lives as healthy people for ages since invented. However, due to the technical limitations such as the essential time required for signal sending and receiving, the state-of-the-art hand prosthesis still cannot fully restore real hand functions. As Artificial Intelligence (AI) technologies, especially deep learning and artificial neural networks, nowadays show an impressive performance when building smart machines, it is possible to use them to bring improvements to conventional hand prosthesis. This advanced AI prosthesis can learn real hand functions through self-learning and eventually, fully achieve all real hand functions. Moreover, with the help of current biologically inspired neural network models and spiking neural networks (SNN), the power consumption and reaction delays of a prosthesis can be further minimized. A novel approach which attempts to address the long-existing problems such as frequent misclassification faced by current hand prosthesis is presented in this thesis. Through converting the raw surface electromyograph (sEMG) signals from amputees with different hand amputation levels and able-bodied people into heatmaps, with an applied properly designed convolution neural network which extracts and learns the features contained within the heatmaps, the mis-trigger disadvantage rapidly decreases. The experimental results, from 8 hand gestures so far, indicate that this novel approach is effective. Moreover, the relationship between the number of sensors, used to record the sEMG signals, and the recognition accuracy is also examined and presented in this thesis. According to the experimental results, the optimal or required number of sensors when recording sEMG signals can be minimized for different hand gestures without classification accuracy degradation. This thesis also contains another novelty: a spiking sEMG signal maps-based hand gesture classification algorithm. The algorithm employs a spiking neural network as the classifier, as well as a common heatmap technique which efficiently converts the raw sEMG signals into common heatmaps that can be used by the spiking neural network. The classification results obtained by two different sEMG datasets denote high robustness of the novel algorithm. Moreover, further evaluation of the experimental results denotes that both the mis-trigger issue as well as power consumption are reduced when applying this novel algorithm. Additionally, to further utilize the potential of sEMG signals, another novel algorithm which aims at frequency domain features is presented in this thesis. This algorithm successfully extracts the frequency domain features by applying singular system analysis (SSA) to the raw sEMG signals and converts them into frequency density maps (FDM). With the help of a proposed SNN, the algorithm is proved to be efficient for different hand gestures. In addition, further evaluation of this algorithm indicates that it demonstrates a significant reduction in computational costs, training time, power consumption whilst, at the same time, results in lower classification errors/mis-triggers when compared to other state of the art hand gesture recognition methodologies.
- Advisor / supervisor
- Petropoulakis, L. (Lykourgos)
- Soragahan, J. J.
- Resource Type
- DOI
- Date Created
- 2022
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PDF of thesis T17648 | 2026-03-17 | Public | Download |