EMG-Based Hand Gestures Classification Using Machine Learning Algorithms

Nia, Nafiseh Ghaffar; Kaplanoğlu, Erkan; Nasab, Ahad

doi:10.1109/southeastcon51012.2023.10115158

Cited by 3 publications

(2 citation statements)

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“…The integration of machine learning algorithms with these wearable devices enhances their capabilities by enabling the analysis of complex bio-electrical data to detect anomalies, predict health conditions, and provide personalized recommendations. For instance, machine learning models can be trained to classify ECG signals for blood pressure estimation [6], EEG signals for emotion recognition [16], and EMG signals for gesture recognition [17]. This combination of personalized wearable devices and machine learning holds great promise in advancing preventive healthcare, early disease detection, and personalized treatment strategies, ultimately leading to improved patient outcomes and quality of life.…”

Section: Bio-electrical Wearable Devicesmentioning

confidence: 99%

“…The study focused on classifying EMG signals to control assistive devices for individuals with sensory-motor disorders. The ANN model's application demonstrates the potential of machine learning algorithms in improving the accuracy and efficiency of EMG signal classification [17]. In another work, Avramoni et al developed a sophisticated algorithm to detect pill intake using a smart wearable device with inertial measurement unit (IMU) sensors by evaluating the associated gestures.…”

Section: Bio-electrical Wearable Devicesmentioning

confidence: 99%

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Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Olyanasab,

Annabestani

2024

JPM

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This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer’s physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual’s physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

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Section: Bio-electrical Wearable Devicesmentioning

confidence: 99%

Section: Bio-electrical Wearable Devicesmentioning

confidence: 99%