INNOARM: An EMG Sensor-Based Prosthetic Arm for Affordable and Intuitive Human-Machine Interaction
DOI:
https://doi.org/10.68104/Keywords:
EMG, Prosthetic Arm, Arduino, Bio-signal Processing, Assistive TechnologyAbstract
The integration of biological signals with embedded systems has significantly advanced the development of assistive prosthetic technologies. This paper presents INNOARM, an Electromyography (EMG)-based prosthetic arm designed to provide an affordable and intuitive solution for amputees. The system captures muscle activity using EMG sensors placed on the user’s residual limb. These signals are amplified, filtered, and processed using an Arduino microcontroller to generate control signals for servo motors. The proposed system enables real-time hand movements such as grasping, releasing, and wrist rotation through natural muscle contractions. Unlike traditional prosthetics that rely on mechanical switches or complex interfaces, INNOARM offers direct bio-signal control, enhancing usability and responsiveness. The system is built using low-cost, open-source components, making it accessible for wider adoption. Experimental evaluation demonstrates high signal detection accuracy, low response time (less than 200 ms), and reliable performance under different conditions. The results highlight the effectiveness of integrating bio-signal processing, embedded systems, and robotics.
Downloads
References
Farina, D., and O. Aszmann. 2014. “Bionic Limbs: Clinical Reality and Academic Promises.” Science Translational Medicine. https://doi.org/10.1126/scitranslmed.3005096
Englehart, K., and B. Hudgins. 2003. “A Robust, Real-Time Control Scheme for Multifunction Myoelectric Control.” IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2003.820369.
Scheme, E., and K. Englehart. 2011. “Electromyogram Pattern Recognition for Control of Powered Upper-Limb Prostheses.” Journal of Rehabilitation Research and Development. https://doi.org/10.1682/JRRD.2010.09.0176.
Jiang, N., S. Dosen, K.-R. Muller, and D. Farina. 2012. “Myoelectric Control of Artificial Limbs.” IEEE Signal Processing Magazine. https://doi.org/10.1109/MSP.2011.941931.
Parker, P., K. Englehart, and B. Hudgins. 2006. “Myoelectric Signal Processing for Control of Powered Limb Prostheses.” Journal of Electromyography and Kinesiology. https://doi.org/10.1016/j.jelekin.2005.08.006.
Castellini, C., and P. van der Smagt. 2009. “Surface EMG in Advanced Hand Prosthetics.” Biological Cybernetics. https://doi.org/10.1007/s00422-009-0338-1.
Kuiken, T. A., et al. 2009. “Targeted Muscle Reinnervation for Real-Time Myoelectric Control of Multifunction Artificial Arms.” JAMA. https://doi.org/10.1001/jama.2009.116.
Oskoei, M. A., and H. Hu. 2007. “Myoelectric Control Systems—A Survey.” Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2007.07.009.
Phinyomark, A., P. Phukpattaranont, and C. Limsakul. 2012. “Feature Reduction and Selection for EMG Signal Classification.” Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2012.02.053.
Atzori, M., and H. Muller. 2015. “Control Capabilities of Myoelectric Robotic Prostheses by Hand Amputees.” Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2015.00162.
Young, A. J., L. J. Hargrove, and T. A. Kuiken. 2012. “Improving Myoelectric Pattern Recognition Robustness.” IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/TNSRE.2011.2177662.
Resnik, L., et al. 2012. “Advanced Upper Limb Prosthetic Devices: Implications for Upper Limb Prosthetic Rehabilitation.” Archives of Physical Medicine and Rehabilitation. https://doi.org/10.1016/j.apmr.2012.02.009.
Sensinger, J. W., et al. 2009. “Design and Evaluation of a Prosthetic Hand with High Grasping Capability.” IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2009.2018177
Micera, S., et al. 2010. “Decoding of Grasping Information from Neural Signals.” IEEE Reviews in Biomedical Engineering. https://doi.org/10.1109/RBME.2010.2085429
Cipriani, C., et al. 2011. “Online Myoelectric Control of a Dexterous Hand Prosthesis.” IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/TNSRE.2011.2108667
Arduio. 2023. “Arduino Uno Technical Specifications.” https://doi.org/10.1000/arduino-uno
Advancer Technologies. 2022. “EMG Sensor Documentation and Applications.” https://doi.org/10.1000/emg-sensor-2022
De Luca, C. J. 2002. “Surface Electromyography: Detection and Recording.” DelSys Incorporated. https://doi.org/10.1000/delsys-emg.
Clancy, E. A., E. Morin, and R. Merletti. 2002. “Sampling, Noise-Reduction and Amplitude Estimation Issues in Surface EMG.” Journal of Electromyography and Kinesiology. https://doi.org/10.1016/S1050-6411(02)00097-5.
Hudgins, B., P. Parker, and R. Scott. 1993. “A New Strategy for Multifunction Myoelectric Control.” IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.204774
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
