## 8 Conclusions
### Recommendations for Future Work  
The first limitation is that the research examines only five specific hand gestures, whereas the GRABMyo dataset offers data on 16 different hand gestures. In reality, the variety of hand gestures is virtually limitless, restricted only by an individual's creativity.  
The second limitation is that the GRABMyo dataset only captures EMG signals from the forearm, while human signals originate from the brain as EEG signals before being converted by alpha motor neurons into EMG signals as illustrated in Figure 31. Future research could explore the relationship between brain activity and muscle activity to enhance artificial intelligence for controlling arm movements. The next limitation is that this research demonstrates communication with the arm operating on a half-duplex network, meaning that it only allows for a one-way transmission. Investigation can be done into the signals that are generated by the neurons, providing feedback to the senses that provide sensory information. Further exploration into incorporating bidirectional communication capabilities would enhance the system's functionality, enabling more comprehensive interaction and feedback between the user and the prosthetic device.  
The primary focus of our analysis was on electromyographic (EMG) data, with the objective of enhancing the control systems for prosthetic devices through improved EMG signal processing and machine learning techniques. While EMG data provide valuable insights into muscle activity, it is important to acknowledge that this study does not include electroencephalographic (EEG) data analysis or the combination of EEG and EMG data for a more comprehensive analysis of motor activation patterns. The decision to concentrate exclusively on EMG data was driven by several factors. EMG signals are directly associated with muscle activity, making them particularly relevant for applications in prosthetic control. Additionally, the complexity and computational requirements of EEG signal processing were beyond the scope of this study, which aimed to develop efficient and practical solutions for prosthetic device control based on muscle activity alone. Incorporating EEG data and combining it with EMG data could offer a more detailed understanding of the neural and muscular coordination during motor tasks. EEG signals reflect brain activity and can provide insights into the intention behind motor actions, which, when combined with EMG signals, could enhance the precision and responsiveness of prosthetic control systems. Previous studies have demonstrated the feasibility and benefits of integrating EEG and EMG data. These studies utilized various methodologies to successfully integrate and analyze EEG and EMG data. Our future research work will explore the integration of EEG and EMG data to develop more advanced control systems for prosthetic devices. Proposed methodologies could include the simultaneous recording and synchronization of EEG and EMG signals, advanced signal processing techniques to extract relevant features from both data types, and machine learning algorithms designed to  
Figure 31: Diagram depicting the schematic pathway of nerves extending from the brain to the hand [30; 31; 32; 33].  
handle multimodal inputs. Potential challenges include managing the increased complexity of data processing and ensuring the real-time responsiveness of the control systems.  
Conceptualization, Methodology, Investigation, Supervision, Resources and Software, C.L.K.; Methodology, Investigation and Data Curation, C.K.H.; Project administration, Resources, Supervision, Visualization and Formal analysis, F.K.T.; Investigation, Supervision, Data Curation and Funding Acquisition, Y.Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.