Deep Learning Enabled Perceptive Wearable Sensor: An Interactive Gadget for Tracking Movement Disorder

Abstract

Biosignals of diverse body posture contain a key information about the physiological, kinesiological, and anatomical status of human body that can facilitate in early assessing the neurological maladies such as Parkinson's disease, multiple sclerosis, and other neurological disorders. Early detection and timely intervention of specific diagnosis for curing the disorders is considered as one of the effective step of diagnosis. Existing technologies of capturing human movements have the limitations of low latencies, bulky counterpart, cost‐intensive, and power consuming. A highly sensitive (≈440 mV N−1), confirmable, and flexible wearable gadget is introduced for addressing such complexities; moreover, the gadget is fabricated by recycling waste material. Subsequently, it is interfaced with various deep/machine learning algorithms for classification/prediction of different hand poses; particularly unsupervised k‐means clustering is used for observing discrete classes, and different supervised algorithms such as k‐nearest neighbor, support vector machine, deep neural network (DNN), and pattern recognition that provide the high degree of prediction accuracy (up to ≈98%) for classification of different postures. Thus, artificial intelligence‐aided wearable gadget can not only offer an unique solution for autonomously tracking various body movements but also assist in reducing waste materials, which is otherwise a threat to the environment.

Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

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Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

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