Electromyography pattern-recognition based prosthetic limb control using various machine learning techniques

Ghildiyal, Sushil; Mani, Geetha; Nersisson, Ruban

doi:10.1080/03091902.2022.2062064

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Sensory restoration in neuro-prosthetics involves recording sensory information from the PNS, transmitting it to the brain, and integrating it with other sensory inputs, often in real-time ( 78 , 79 ). Ghildiyal et al ( 80 ) applied electromyography pattern recognition for controlling prosthetic limbs using various machine learning techniques. They developed a force-controlled prosthetic limb that improves the self-reliance, quality of life, and mental strength of amputees.…”

Section: Neuro-prostheticsmentioning

confidence: 99%

“…Sensory feedback is crucial for users to perceive the environment, exert the right amount of force, and interact safely. Sensory restoration in neuro-prosthetics involves recording sensory information from the PNS, transmitting it to the brain, and integrating it with other sensory inputs, often in real-time (78,79). Ghildiyal et al (80) applied electromyography pattern recognition for controlling prosthetic limbs using various machine learning techniques.…”

Section: Neuro-prostheticsmentioning

confidence: 99%

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Transforming medicine: artificial intelligence integration in the peripheral nervous system

Qian,

Alhaskawi,

Dong

et al. 2024

Front. Neurol.

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In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI’s applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system’s interface.

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Section: Neuro-prostheticsmentioning

confidence: 99%

Section: Neuro-prostheticsmentioning

confidence: 99%

Transforming medicine: artificial intelligence integration in the peripheral nervous system

Qian,

Alhaskawi,

Dong

et al. 2024

Front. Neurol.

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show abstract

“…Refs. [55][56][57][58][59] combined the problems of existing surface EMG detection products. This paper designed a graphene-based flexible dry electrode as the acquisition electrode and designed and fabricated a surface EMG acquisition system device with flexible signal acquisition and processing hardware.…”

Section: Introductionmentioning

confidence: 99%

Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation

Liu

Li²,

Zhang³

et al. 2022

Micromachines

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Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.

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Analysis of Machine and Deep Learning Algorithms for Pattern Recognition in Medical Data

Gowru,

Appa Rao

2024

2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)

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Electromyography pattern-recognition based prosthetic limb control using various machine learning techniques

Cited by 3 publications

References 20 publications

Transforming medicine: artificial intelligence integration in the peripheral nervous system

Transforming medicine: artificial intelligence integration in the peripheral nervous system

Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation

Analysis of Machine and Deep Learning Algorithms for Pattern Recognition in Medical Data

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