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EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals
Surface Electromyography (sEMG) is widely studied for its applications in rehabilitation, prosthetics, robotic arm control, and human-machine interaction. However, classifying Activities of Daily Living (ADL) using sEMG signals often requires extensi
Externí odkaz:
http://arxiv.org/abs/2410.00586
Autor:
Narayan, KG Raghavendra, Mookherji, Srijanee, Odelu, Vanga, Prasath, Rajendra, Turlapaty, Anish Chand, Das, Ashok Kumar
With rapid technological growth, security attacks are drastically increasing. In many crucial Internet-of-Things (IoT) applications such as healthcare and defense, the early detection of security attacks plays a significant role in protecting huge re
Externí odkaz:
http://arxiv.org/abs/2308.00943
Recent literature suggests that the surface electromyography (sEMG) signals have non-stationary statistical characteristics specifically due to random nature of the covariance. Thus suitability of a statistical model for sEMG signals is determined by
Externí odkaz:
http://arxiv.org/abs/2307.03403
Statistical models of Surface electromyography (sEMG) signals have several applications such as better understanding of sEMG signal generation, improved pattern recognition based control of wearable exoskeletons and prostheses, improving training str
Externí odkaz:
http://arxiv.org/abs/2301.05417
In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25
Externí odkaz:
http://arxiv.org/abs/2301.03325
The probability density function (pdf) of surface Electromyography (sEMG) signals follows any one of the standalone standard distributions: the Gaussian or the Laplacian. Further, the choice of the model is dependent on muscle contraction force (MCF)
Externí odkaz:
http://arxiv.org/abs/2301.01080
Publikováno v:
in IEEE Sensors Journal, vol. 19, no. 24, pp. 12196-12204, 15 Dec.15, 2019
Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this sce
Externí odkaz:
http://arxiv.org/abs/1811.03795
Publikováno v:
Healthcare, Vol 10, Iss 2, p 324 (2022)
COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects
Externí odkaz:
https://doaj.org/article/576f2e22acf04c238e60dcb0fe3ee97c
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