TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification

Li, Jiarong; Wang, Zihan; Zhao, Zihao; Jin, Yuchao; Yin, Jihong; Huang, Shao-Lun; Wang, Jiyu

doi:10.1145/3460418.3480410

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Gait analysis better can be understood by the two famous models, first is six determinants model and the other one is inverted pendulum model. [ 45 ] In six determinants model, the center of mass (COM) moves in a horizontal path with a constant velocity

{\vec{v}}_{COM}

. The stance leg generates a force

\vec{F}

which supports body weight and balance the COM by doing positive and negative work.…”

Section: Resultsmentioning

confidence: 99%

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

Babu¹,

Ranpariya

Sinha

et al. 2023

Adv Materials Technologies

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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.

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{\vec{v}}_{COM}

. The stance leg generates a force

\vec{F}

which supports body weight and balance the COM by doing positive and negative work.…”

Section: Resultsmentioning

confidence: 99%

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

Babu¹,

Ranpariya

Sinha

et al. 2023

Adv Materials Technologies

View full text Add to dashboard Cite

show abstract

“…Shi et al [ 36 , 37 ] used Convolutional Neural Networks with triboelectric capacitive sensors embedded within the floor to identify either groups of people or individuals with accuracies varying between 85 and 96%. Li et al [ 38 ] used triboelectric sensors to measure gait features, allowing eight individuals to be classified with an accuracy of 97.6% using a BLSTM network. An issue with this capacitive approach is that it requires pressure-based floor deformation to operate, introducing fatigue-based longevity concerns similar to pressure-based sensor implementations.…”

Section: Introductionmentioning

confidence: 99%

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Konings

Alam

Faulkner

et al. 2022

Sensors

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In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.

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“…Besides RF sensing, researchers have explored various approaches to positioning sensors around smart environments to enhance human activity recognition and individual identification. One notable example is the Triboelectric Nanogenerator (TENG)-based gait sensor system [88]. The TENG-based gait sensor system utilizes triboelectric nanogenerators to detect mechanical motions through electrical signals, such as human steps.…”

mentioning

confidence: 99%

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Diraco

Rescio

Siciliano

et al. 2023

Sensors

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Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.

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TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification

Cited by 9 publications

References 19 publications

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

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

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

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