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

Abstract

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.

Triboelectric Nanogenerators and Hybridized Systems for Enabling Next-Generation IoT Applications

“…In recent years, advanced energy harvesting technologies have been developed to effectively scavenge the indoor energies, e.g., biomechanical energy from human activities [ 134 , 135 ], waste heat energy from appliances [ 136 ], and indoor light [ 137 ], to directly power smart electronic devices and realize sustainable systems without too much maintenance operations. TENG, due to its wide selection of materials and high energy conversion efficiency for low-frequency excitation, is quite suitable to be designed as different household products, e.g., floor [ 138 – 142 ] and bed sheet [ 143 ], and wearable devices [ 13 , 22 , 144 – 146 ] for indoor biomechanical energy harvesting and sensing. Moreover, by integrating TENGs with other energy harvesting mechanisms such as EMG, PENG, PyENG, TEG, and SC, hybrid generators could be implemented toward effective indoor energy harvesting applications.…”

Triboelectric Effect Enabled Self-Powered, Point-of-Care Diagnostics: Opportunities for Developing ASSURED and REASSURED Devices

“…Such a supercapacitor system was then utilized to run a temperature sensor requiring ~1.45 V to work which was powered by hand-flapping of the FEP-Au TENG. More recently, Zhang et al [ 113 ] have developed wearable triboelectric-effect-based socks for scavenging low-frequency energy from natural human body movement to power a Bluetooth module and transmit the body temperature value detected by the embedded temperature sensor via an Internet of Things (IoT) framework. The textile-based contact-separation TENG comprised of four functional layers: a nitrile thin film and silicone rubber film with patterned frustum structures (acting as triboelectric layers), and two conductive textiles attached to the back of the two contact electrification layers for charge collection.…”

Flourishing energy harvesters for future body sensor network: from single to multiple energy sources

“…The rapid advancement in materials, sensors, circuits, and wireless transmission technologies will give way to the body sensor network (bodyNET) ( Niu et al., 2019 ; Tian et al., 2019 ), which enables human physiological signal detection not only on the skin but also inside the body as shown in Figure 1 A ( Lee et al., 2019a ; Zheng et al., 2020 ). Flexible electronic technologies allow the sensors to exist in various forms, including electronic skins that are directly attached to the skin ( Chen et al., 2019a ; Oh and Bao, 2019 ; Pu et al., 2017b ), clothes that are worn on the human body ( Chen et al., 2020b ; Shi et al., 2020c ), glasses ( Vera Anaya et al., 2020 ), face masks ( Zhang et al., 2020a ), watches ( Quan et al., 2015 ), gloves ( Sundaram et al., 2019 ), insoles ( Wu et al., 2020b ), socks ( Zhang et al., 2020c ), shoes ( Li et al., 2017 ), and implantable devices ( Arab Hassani et al., 2020 ; Hinchet et al., 2019 ; Xiang et al., 2016 ), to provide comprehensive monitoring of the user's health status and motions. For instance, the sensors attached to the skin or worn on the body can record body temperature, pulse, respiration rate, blood pressure, etc.…”