Walking pattern analysis using deep learning for energy harvesting smart shoes with IoT

Shah, Neel; Kamdar, Laxit; Gokalgandhi, Drashti; Mehendale, Ninad

doi:10.1007/s00521-021-05864-4

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…Nike has also developed Nike Adapt BB shoes that automatically tighten the laces by pressing a button. However, there is no such innovation for masks that can adjust the fitting based on the displacement of the mask on the skin while performing activities [ 22 , 23 , 24 , 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Adjustment of PPE Masks Using IoT Sensor Fusion

Manchanda

Lee

Poznanski

et al. 2023

Sensors

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The COVID-19 pandemic has led to a dramatic increase in the use of PPE by the general public as well as health professionals. Scientists and health organizations have developed measures to protect people and minimize the catastrophic outcomes of COVID, including social distancing, frequent and periodic sanitizing, vaccinations, protective coverings, and face masks. During this time, the usage of protective face masks has increased dramatically. A mask only provides full safety to the user if it is a proper fit on their face. The aim of this paper is to automatically analyze and improve the fit of a face mask using IoT sensors. This paper describes the creation of a 3D-printed smart face mask that uses sensors to determine the current mask fit and then automatically tightens mask straps. This is evaluated using adjustment response time and the quality of fit achieved using the automatic adjustment approach with a range of sensor types.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Adjustment of PPE Masks Using IoT Sensor Fusion

Manchanda

Lee

Poznanski

et al. 2023

Sensors

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“…The promotion of financial development and renewable energy utilization on environmental quality makes TI particularly important (Shah et al, 2021). Total factor productivity is calculated by using the Malmquist index.…”

Section: Ti Efficiencymentioning

confidence: 99%

Crowding effect of institutional openness based on the big data algorithm on the efficiency of new energy technology innovation

Cao

Qian

2023

Front. Bioeng. Biotechnol.

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In recent years, new energy vehicles, as a high-tech industry, have developed rapidly. This paper uses “number of new energy project personnel” and “hours of R&D (research and development) personnel” as design indicators to evaluate the investment of innovative talents in enterprises. This paper first introduces the supporting factors of the innovation environment in the input of innovation resources, and conducts research from four perspectives: human resources, innovation R&D, technology acquisition, and environmental support. In the construction of the innovation output index system, this paper outlines that the technological innovation (TI) achievements of enterprises are related to factors such as technological capabilities, profits, and market competitiveness of enterprises. Finally, this paper evaluates it from three aspects: the research and development achievements, the economic benefits obtained and the competitive benefits of the enterprise. The results show that from 2018 to 2022, the average technological innovation efficiency of new energy enterprises is 1.06; TI’s efficiency indicators in the past five years are all above 1, and the overall improvement trend of TI is relatively stable. The new energy vehicle collaborative innovation system constructed in this paper will promote the overall development of the new energy vehicle industry.

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“…Recently, Shah et al implemented deep learning algorithms on gait signals collected from a piezoelectric-based smart shoe and achieved up to 96.2% accuracy in differentiating gait patterns. 136 Further research efforts have explored the application of convolutional neural network (CNN) and artificial neural network (ANN) architectures for achieving deep learning from sensor gait data. These demonstrate a promising step toward more robust and automated personalized healthcare.…”

Section: Piezoelectric Devicesmentioning

confidence: 99%

“…For gait monitoring applications, machine learning models have the potential to leverage data output to perform real-time tracking of human motion. Recently, Shah et al implemented deep learning algorithms on gait signals collected from a piezoelectric-based smart shoe and achieved up to 96.2% accuracy in differentiating gait patterns . Further research efforts have explored the application of convolutional neural network (CNN) and artificial neural network (ANN) architectures for achieving deep learning from sensor gait data.…”

Section: Findings: Applications For Self-powered Piezoelectric Devicesmentioning

confidence: 99%

“…134 More recent efforts have begun to investigate the energy harvesting performance of self-powered piezoelectric devices and their potential to monitor human gait signals. For example, Wang et 136 Further research efforts have explored the application of convolutional neural network (CNN) and artificial neural network (ANN) architectures for achieving deep learning from sensor gait data. These demonstrate a promising step toward more robust and automated personalized healthcare.…”

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confidence: 99%

See 1 more Smart Citation

On-Body Piezoelectric Energy Harvesters through Innovative Designs and Conformable Structures

Fernandez

Cai

Chen

et al. 2021

ACS Biomater. Sci. Eng.

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Recent advancements in wearable technology have improved lifestyle and medical practices, enabling personalized care ranging from fitness tracking, to real-time health monitoring, to predictive sensing. Wearable devices serve as an interface between humans and technology; however, this integration is far from seamless. These devices face various limitations such as size, biocompatibility, and battery constraints wherein batteries are bulky, are expensive, and require regular replacement. On-body energy harvesting presents a promising alternative to battery power by utilizing the human body's continuous generation of energy. This review paper begins with an investigation of contemporary energy harvesting methods, with a deep focus on piezoelectricity. We then highlight the materials, configurations, and structures of such methods for self-powered devices. Here, we propose a novel combination of thin-film composites, kirigami patterns, and auxetic structures to lay the groundwork for an integrated piezoelectric system to monitor and sense. This approach has the potential to maximize energy output by amplifying the piezoelectric effect and manipulating the strain distribution. As a departure from bulky, rigid device design, we explore compositions and microfabrication processes for conformable energy harvesters. We conclude by discussing the limitations of these harvesters and future directions that expand upon current applications for wearable technology. Further exploration of materials, configurations, and structures introduce interdisciplinary applications for such integrated systems. Considering these factors can revolutionize the production and consumption of energy as wearable technology becomes increasingly prevalent in everyday life.

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Walking pattern analysis using deep learning for energy harvesting smart shoes with IoT

Cited by 9 publications

References 14 publications

Automated Adjustment of PPE Masks Using IoT Sensor Fusion

Automated Adjustment of PPE Masks Using IoT Sensor Fusion

Crowding effect of institutional openness based on the big data algorithm on the efficiency of new energy technology innovation

On-Body Piezoelectric Energy Harvesters through Innovative Designs and Conformable Structures

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