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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 118
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| Paper Title: | “Hastha” |
| Authors Name: | Muskan Nadaf , Mandar Goavakar , Manoj Badiger , Pruthvi Melageri |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206955
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| Published Paper Id: | IJRTI2511091 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | This project aims to develop a real-time adaptive myoelectronic prosthetic arm controlled through Spiking Neural Networks (SNNs). By utilizing Electromyography (EMG) sensors placed on the residual arm, the system detects muscle signals that correspond to various hand gestures such as open, close, pinch, and point. These muscle signals are used as input for the SNN model, which is trained to recognize the signals and map them to the appropriate prosthetic hand movements. Unlike conventional prosthetics, which rely on pre-programmed motions or basic control systems, this project emphasizes real-time, adaptive control through the continuous learning capability of SNNs. As the user interacts with the system, the SNN learns and refines its responses, allowing for smoother and more intuitive control of the prosthetic hand. This approach presents a significant step forward in creating a responsive, user-friendly prosthetic solution that can adapt to both simple and complex tasks, offering users greater freedom and functionality. The system also holds potential for future applications in more complex activities, such as typing or fine motor tasks, by further training and refining the model. |
| Keywords: | The project “Hastha: An SNN-Based Adaptive Myoelectric Prosthetic Hand” aims to overcome the limitations of traditional prosthetics, which often rely on fixed gestures and limited adaptability. By using Electromyography (EMG) signals from the user’s muscles and processing them with Spiking Neural Networks (SNNs), the system can interpret natural movements and continuously learn through biological principles like Spike Timing Dependent Plasticity (STDP). This enables the prosthetic hand to provide real-time, personalized, and intuitive control while remaining affordable and modular. Ultimately, the project seeks to offer amputees a practical, intelligent, and user-friendly solution that enhances independence and quality of life. |
| Cite Article: | " “Hastha”", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a777-a787, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511091.pdf |
| Downloads: | 000227 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511091
Registration ID:206955
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a777-a787 Country: Belagavi, Karnataka, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511091 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511091 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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