Recent Advances in Self-Powered Wearable Flexible Sensors for Human Gaits Analysis

Abstract

The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology.

Keywords: exoskeleton; motion detection; robotics; wearable flexible sensors.

Figure 1

Schematic diagram of the self-powered wearable flexible sensors for human gaits analysis. Reproduced with permission from Ref. [68]. Copyright 2021, Science. Reproduced with permission from Ref. [69]. Copyright 2024, Wiley. Reproduced with permission from Ref. [70]. Copyright 2023, Wiley. Reproduced with permission from Ref. [71]. Copyright 2021, Wiley. Reproduced with permission from Ref. [72]. Copyright 2022, Wiley. Reproduced with permission from Ref. [73]. Copyright 2020, Springer Nature. Reproduced with permission. from Ref. [74]. Copyright 2017, American Chemistry Society. Reproduced with permission from Ref. [75]. Copyright 2019, Elsevier. Reproduced with permission from Ref. [76]. Copyright 2017, Wiley.

Figure 3

Enhancement methods for piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs). (a) Fabrication process of the polydopamine (PDA)-modified barium titanate (BTO)/poly(vinylidene fluoride) (PVDF) composite film. Reproduced with permission from Ref. [87]. Copyright 2020, Elsevier. (b) Three-dimensional hierarchically interlocked PVDF/ZnO fibers-based physiological monitoring electronics. Reproduced with permission from Ref. [93]. Copyright 2020, Elsevier. (c) Scanning electron microscopy (SEM) image of polyvinylidene fluoride-trifluoroethylen [P(VDF-TrFE)]/BTO nanocomposite micropillar array. Reproduced with permission from Ref. [91]. Copyright 2020, Wiley. (d) Pyramid-shaped P(VDF-TrFE)-based PENGs. Reproduced with permission from Ref. [92]. Copyright 2020, Wiley. (e) 3D stacked triboelectric sensor with micro-cone structures for skin-contact physiological signal perception. Reproduced with permission from Ref. [95]. Copyright 2024, Wiley. (f) Textile-based TENGs (T-TENGs) with mm-scale frustum structure. Reproduced with permission from Ref. [73]. Copyright 2020, Springer Nature. (g) Structure diagram of a TENG with arch-shaped structure. Reproduced with permission from Ref. [96]. Copyright 2017, Elsevier. (h) 3D schematic of a flexible comb triboelectric–electret nanogenerator. Reproduced with permission from Ref. [97]. Copyright 2017, Royal Society of Chemistry.

Figure 4

Joint sensors for human motion detection. (a) Wearable energy generator based on PVDF/BTO piezoelectric fibers. Reproduced with permission from Ref. [104]. Copyright 2020, Wiley. (b) Stretchable-rubber-based triboelectric nanogenerator for body motion detection. Reproduced with permission from Ref. [105]. Copyright 2015, Wiley. (c) Comparison of a normal person’s gait with that of a paretic patient detected by the tendon monitoring system. Reproduced with permission from Ref. [68]. Copyright 2021, Science. (d) Structure and application scenarios of dual ratchet sensing (DRS) system in detecting states of slow walking, fast walking, onset of Parkinson’s, knee injuries, and falls in elderly individuals by DRS system. Reproduced with permission from Ref. [69]. Copyright 2024, Wiley.

Figure 5

Force sensors for gait recognition based on the piezoelectric principles. (a) PVDF strips-based wearable and wireless gait monitoring system. Reproduced with permission from Ref. [106]. Copyright 2022, Elsevier. (b) Foot pressure monitoring according to the gait patterns. Reproduced with permission from Ref. [107]. Copyright 2022, Wiley. (c) Smart insole based on the fabricated pressure piezo-array for foot pressure distribution monitoring. Reproduced with permission from Ref. [108]. Copyright 2018, Wiley. (d) Self-powered sock for walking, running and jumping detection and the gait distribution of the in-toeing and out-toeing walking Reproduced with permission from Ref. [109]. Copyright 2024, Elsevier. (e) Application of multi-local strain sensor for gait evaluation. Reproduced with permission from Ref. [110]. Copyright 2020, Elsevier. (f) Smart insole integrated with five piezoelectric textile sensors for normal, pigeon-toed, and splayfooted postures detection. Reproduced with permission from Ref. [111]. Copyright 2022, Springer Nature.

Figure 6

Triboelectric nanogenerators (TENGs) for gait patterns monitoring. (a) TENG-based smart insole to detect Parkinson’s symptoms and fall. Reproduced with permission from Ref. [72]. Copyright 2022, Wiley. (b) Gait analysis based on TENG smart insole for real-time walking, jogging, and jumping states detection. Reproduced with permission from Ref. [77]. Copyright 2024, Wiley. (c) Polyadiohexylenediamine (PA66)/PVDF nanofibers coaxial yarns based TENGs for standard, in-toeing, and out-toeing gait. Reproduced with permission from Ref. [112]. Copyright 2024, Wiley.

Figure 2

Schematic illustrations of transduction principles of self-powered wearable flexible sensors for human gaits analysis: (a) piezoelectricity and (b) triboelectricity.

Figure 7

Self-powered inertial sensors for human gait analysis. (a) Gait analysis of human running. Reproduced with permission. from Ref. [74]. Copyright 2017, American Chemistry Society. (b) Self-powered gyroscope ball for four human activity states detection such as, standing, walking slowly, walking fast, and running. Reproduced with permission from Ref. [76]. Copyright 2017, Wiley. (c) Self-powered 3D activity inertial sensor monitors three types of exercise (standing, walking, and running). Reproduced with permission from Ref. [75]. Copyright 2019, Elsevier.

Figure 8

Gait evaluation algorithms. (a) Human body movement detection using the fast Fourier transform (FFT) calculation. Reproduced with permission from Ref. [71]. Copyright 2022, Wiley. (b) Gait identification using the 1D CNN structure and the confusion map of gait patterns prediction. Reproduced with permission from Ref. [73]. Copyright 2020, Springer Nature. (c) Schematics of the application of the CNN algorithm in conjunction with Bayesian optimization and the classification results. Reproduced with permission from Ref. [69]. Copyright 2024, Wiley.