Origami dynamics based soft piezoelectric energy harvester for machine learning assisted self-powered gait biometric identification

Huang, Cenling; Tan, Ting; Wang, Zhemin; Zhang, Shimin; Yang, Fangzhou; Lin, Zhiliang; Yan, Zhimiao

doi:10.1016/j.enconman.2022.115720

Cited by 27 publications

(12 citation statements)

References 33 publications

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“…were extracted to substitute 1000 potentially redundant data points. The full list of 21 characteristic indexes in time domain and frequency for voltage data [87] included maximum value, minimum value, mean value, peak-to-peak value, corrected mean value, variance, standard deviation, kurtosis, skewness, root mean square factor, waveform factor, peak factor, pulse factor, margin factor, the center of gravity frequency, mean square frequency, root mean square frequency, frequency variance, frequency standard deviation, power spectrum entropy, and singular spectrum entropy.…”

Section: Methodsmentioning

confidence: 99%

A Metamaterial Computational Multi‐Sensor of Grip‐Strength Properties with Point‐of‐Care Human‐Computer Interaction

Chen,

Li,

Wang

et al. 2023

Advanced Science

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Grip strength is a biomarker of frailty and an evaluation indicator of brain health, cardiovascular morbidity, and psychological health. Yet, the development of a reliable, interactive, and point‐of‐care device for comprehensive multi‐sensing of hand grip status is challenging. Here, a relation between soft buckling metamaterial deformations and built piezoelectric voltage signals is uncovered to achieve multiple sensing of maximal grip force, grip speed, grip impulse, and endurance indicators. A metamaterial computational sensor design is established by hyperelastic model that governs the mechanical characterization, machine learning models for computational sensing, and graphical user interface to provide visual cues. A exemplify grip measurement for left and right hands of seven elderly campus workers is conducted. By taking indicators of grip status as input parameters, human‐computer interactive games are incorporated into the computational sensor to improve the user compliance with measurement protocols. Two elderly female schizophrenic patients are participated in the real‐time interactive point‐of‐care grip assessment and training for potentially sarcopenia screening. The attractive features of this advanced intelligent metamaterial computational sensing system are crucial to establish a point‐of‐care biomechanical platform and advancing the human‐computer interactive healthcare, ultimately contributing to a global health ecosystem.

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Section: Methodsmentioning

confidence: 99%

A Metamaterial Computational Multi‐Sensor of Grip‐Strength Properties with Point‐of‐Care Human‐Computer Interaction

Chen,

Li,

Wang

et al. 2023

Advanced Science

Self Cite

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“…In another demonstration, Huang et al [208] introduced an energy harvester utilizing the KOSs, where PVDF piezoelectric sheets were strategically positioned at the mountain creases, functioning as hinges that bend along the crease axis, see figure 29(b). Similar to a bending beam, the PVDF sheets undergo strain away from their neutral axis, resulting in the creation of an electric potential difference.…”

Section: Vibratory Energy Harvestingmentioning

confidence: 99%

The Kresling origami spring: a review and assessment

Masana,

Dalaq,

Khazaaleh

et al. 2024

Smart Mater. Struct.

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Structures inspired by the Kresling origami pattern have recently emerged as a foundation for building functional engineeringsystems with versatile characteristics that target niche applications spanning different technological fields. Their light weight,deployability, modularity, and customizability are a few of the key characteristics that continue to drive their implementationin robotics, aerospace structures, metamaterial and sensor design, switching, actuation, energy harvesting and absorption,and wireless communications, among many other examples. This work aims to perform a systematic review of the literatureto assess the potential of the Kresling origami springs as a structural component for engineering design keeping threeobjectives in mind: i) facilitating future research by summarizing and categorizing the current literature, ii) identifying the currentshortcomings and voids, and iii) proposing directions for future research to fill those voids.

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“…New materials and technologies are emerging for energy harvesting. In [45], a sensing device combines flexible polyvinylidene fluoride (PVDF) piezoelectric film with kresling origami structure to design a new piezoelectric kresling origami generator, which can achieve high-efficiency and broadband energy harvesting performance. In [46], multiscale metamaterials with super-normal functions on energy manipulation are utilized in multi-field renewable energy harvesting and absorbing, which can enhance the local energy density by confining and focusing the energy before it is harvested.…”

Section: A Energy Harvesting Iotmentioning

confidence: 99%

Deep Learning on Energy Harvesting IoT Devices: Survey and Future Challenges

2022

IEEE Access

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Internet-of-Things (IoT) devices are becoming both intelligent and green. On the one hand, Deep Neural Network (DNN) compression techniques make it possible to run deep learning applications on devices equipped with low-end microcontrollers (MCUs). By performing deep learning on IoT devices, in-situ decision-making can be made, which can improve the responsiveness of such devices to the environment and reduce data uploading to edge servers or clouds to save valuable network bandwidth. On the other hand, many IoT devices in the future will be powered by energy harvesters instead of batteries to reduce environmental pollution and achieve permanent service free of battery maintenance. As the energy output of energy harvesters is tiny and unstable, energy harvesting IoT (EH-IoT) devices will experience frequent power failures during their execution, making the software task hard to progress. The deep learning tasks running on such devices must face this challenge and, at the same time, ensure satisfactory execution efficiency. We believe deploying deep learning on EH-IoT devices that execute intermittently will be a challenging yet promising research direction. To motivate research in this direction, this paper summarizes existing solutions and provides an in-depth discussion of future challenges that deserve further investigation. With IoT devices becoming more intelligent and green, DNN inference on EH-IoT devices will generate a much more significant impact in the future in academia and industry.

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Origami dynamics based soft piezoelectric energy harvester for machine learning assisted self-powered gait biometric identification

Cited by 27 publications

References 33 publications

A Metamaterial Computational Multi‐Sensor of Grip‐Strength Properties with Point‐of‐Care Human‐Computer Interaction

A Metamaterial Computational Multi‐Sensor of Grip‐Strength Properties with Point‐of‐Care Human‐Computer Interaction

The Kresling origami spring: a review and assessment

Deep Learning on Energy Harvesting IoT Devices: Survey and Future Challenges

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