Sensing body motions based on charges generated on the body

Zhang, Renyun; Hummelgård, Magnus; Örtegren, Jonas; Yang, Ya; Andersson, Henrik; Balliu, Enkeleda; Blomquist, Nicklas; Engholm, Magnus; Olsen, Martin; Wang, Zhong Lin; Olin, Håkan

doi:10.1016/j.nanoen.2019.06.038

Cited by 41 publications

(22 citation statements)

References 33 publications

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“…Differ from the widely used IMU sensors that require high computing power and high computing power for precise body movement monitoring 50 , our deep learning-enabled sock with the minimal number of triboelectric sensors offers an alternative solution for the detection and analysis of the individual gait. Previously, analysis strategies for triboelectric outputs are to manually extract the shallow features from a single waveform, such as frequency, amplitude, interval of peaks, and holding time, which cannot achieve recognition of complicated features with subtle differences among them and is very susceptible to environmental variations, resulting in reduced recognition accuracy 37,49,78 . The technology fusion of the emerging AI with wearable electronics promoted enormous advances to form a whole intelligent system in technologies related to the process of data acquisition, processing/analysis, and transmission 79,80 .…”

Section: Development Of Deep Learning-enabled Socksmentioning

confidence: 99%

Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

et al. 2020

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The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.

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Section: Development Of Deep Learning-enabled Socksmentioning

confidence: 99%

Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

et al. 2020

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“…Body movement monitoring plays an important role in daily life especially for sports training, medical diagnosis/rehabilitation, and security ( Cui et al., 2019 ; He et al., 2018a ; Lin et al., 2018 , 2019 ; Zhang et al., 2019b ; Zou et al., 2019 ). In these particular applications, the flexibility, stretch ability, and shape-adaptability of sensor are essential for keeping signals stable and device available ( Alam et al., 2020 ; An et al., 2018 ; Gogurla et al., 2019 ; Han et al., 2019 ; Liu et al., 2018 ; Ma et al., 2020 ; Sarkar et al., 2019 ).…”

Section: Triboelectric-sensor-based Biomedical Monitoringmentioning

confidence: 99%

Wearable triboelectric sensors for biomedical monitoring and human-machine interface

Tang

et al. 2021

iScience

145

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“…Basing on the contact electrification and electrical induction from the body, Zhang et al provided a new universal body motion sensor (UBS) to detect motions [136]. The contact electrification and electrical induction could produce a potential difference from variable charges between the body and the ground, which is closely connected to different body motions from the toes, feet, legs, waist, fingers, arms, and head.…”

Section: Motionmentioning

confidence: 99%

Nanogenerator-Based Self-Powered Sensors for Wearable and Implantable Electronics

et al. 2020

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Wearable and implantable electronics (WIEs) are more and more important and attractive to the public, and they have had positive influences on all aspects of our lives. As a bridge between wearable electronics and their surrounding environment and users, sensors are core components of WIEs and determine the implementation of their many functions. Although the existing sensor technology has evolved to a very advanced level with the rapid progress of advanced materials and nanotechnology, most of them still need external power supply, like batteries, which could cause problems that are difficult to track, recycle, and miniaturize, as well as possible environmental pollution and health hazards. In the past decades, based upon piezoelectric, pyroelectric, and triboelectric effect, various kinds of nanogenerators (NGs) were proposed which are capable of responding to a variety of mechanical movements, such as breeze, body drive, muscle stretch, sound/ultrasound, noise, mechanical vibration, and blood flow, and they had been widely used as self-powered sensors and micro-nanoenergy and blue energy harvesters. This review focuses on the applications of self-powered generators as implantable and wearable sensors in health monitoring, biosensor, human-computer interaction, and other fields. The existing problems and future prospects are also discussed.

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Sensing body motions based on charges generated on the body

Cited by 41 publications

References 33 publications

Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

Wearable triboelectric sensors for biomedical monitoring and human-machine interface

Nanogenerator-Based Self-Powered Sensors for Wearable and Implantable Electronics

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