Overview of Human Walking Induced Energy Harvesting Technologies and Its Possibility for Walking Robotics

Shi, Hu; Liu, Zhaoying; Mei, Xuesong

doi:10.3390/en13010086

Cited by 55 publications

(37 citation statements)

References 124 publications

(147 reference statements)

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“…Human walking represents an energy source exploitable for feeding wearable devices, such as smart insoles and socks [ 29 ]. According to the transduction mechanism, several solutions were proposed in the literature exploiting electromagnetic induction, piezoelectric, and triboelectric effects for scavenging energy from body vibrations, body inertia, and foot pressure [ 30 , 31 ].…”

Section: An Overview Of Smart Insoles For Plantar Pressure Detection and Gait Analysismentioning

confidence: 99%

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Fazio

Perrone

Velázquez

et al. 2021

Sensors

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The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., ) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

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Section: An Overview Of Smart Insoles For Plantar Pressure Detection and Gait Analysismentioning

confidence: 99%

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Fazio

Perrone

Velázquez

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Materials that have been used for piezoelectric applications include Zinc Oxide (ZnO) and Zirconate Titanate (PZT) [35]. Currently, the most effective method of harvesting kinetic energy from the human body is from walking [36]. According to the literature, almost 67 W of power can be generated from a person weighing 68 kg walking at a speed of two steps per second [37].…”

Section: Comparison Between Energy Harvesting Techniques In Selfmentioning

confidence: 99%

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

2020

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Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review.

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“…The process of human motion is the most frequent source of mechanical energy, which is sustainable and easy to obtain [11][12][13][14][15][16][17]. As wearable devices are continuously developing, people can use them to collect energy for their daily needs [18][19][20][21][22][23][24]. In recent years, TENG has attracted broad attention, as it can transform the ubiquitous mechanical energy into electrical energy.…”

Section: Introductionmentioning

confidence: 99%

Portable Mobile Gait Monitor System Based on Triboelectric Nanogenerator for Monitoring Gait and Powering Electronics

et al. 2021

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A self-powered portable triboelectric nanogenerator (TENG) is used to collect biomechanical energy and monitor the human motion, which is the new development trend in portable devices. We have developed a self-powered portable triboelectric nanogenerator, which is used in human motion energy collection and monitoring mobile gait and stability capability. The materials involved are common PTFE and aluminum foil, acting as a frictional layer, which can output electrical signals based on the triboelectric effect. Moreover, 3D printing technology is used to build the optimized structure of the nanogenerator, which has significantly improved its performance. TENG is conveniently integrated with commercial sport shoes, monitoring the gait and stability of multiple human motions, being strategically placed at the immediate point of motion during the respective process. The presented equipment uses a low-frequency stabilized voltage output system to provide power for the wearable miniature electronic device, while stabilizing the voltage output, in order to effectively prevent voltage overload. The interdisciplinary research has provided more application prospects for nanogenerators regarding self-powered module device integration.

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Overview of Human Walking Induced Energy Harvesting Technologies and Its Possibility for Walking Robotics

Cited by 55 publications

References 124 publications

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

Portable Mobile Gait Monitor System Based on Triboelectric Nanogenerator for Monitoring Gait and Powering Electronics

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