Recognition of Material Surfaces with Smart Gloves Based on Machine Learning

Liu, Yajun; Zhang, Shenchao; Luo, Qi

doi:10.1109/wcmeim54377.2021.00054

Cited by 1 publication

(1 citation statement)

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“…They used machine learning algorithms like XGBoost to achieve 98% classification accuracy. The system extracts time and frequency domain characteristics to train the models [55]. Introducing an advanced system for the ongoing wireless monitoring of arterial blood pressure, this technology, created by Li et al, features a thin, soft, and miniaturized design.…”

Section: Electro-mechanical Wearable Devicesmentioning

confidence: 99%

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: Electro-mechanical Wearable Devicesmentioning

confidence: 99%