Human Identification from Walking Signal based on Measurement of Current Generated by Electrostatic Induction

Kurita, Koichi

doi:10.5057/kei.11.183

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…As in the case of the movement of the palm, an electrostatic induction current is also detected when the participant passes by the vicinity of the electrode in a walking motion. As shown in Figure 4 , the sensor for detecting an electrostatic induction current (with area S E ) is placed in the vicinity of the human body, and a capacitance is formed between the electrode and human body [ 23 , 24 , 25 , 26 , 27 ]. Under the same assumption as in the case of measuring the human body potential by the movement of the palm, the electrostatic induction current I , transiently flowing to the electrode by the walking motion, is given by the following equation.…”

Section: Detection Principlementioning

confidence: 99%

A Non-Contact and Real-Time Measurement Technique of Human Body Potential Using Electrostatic Induction Current Accompanied by Human Body Motion

Kurita

2022

Sensors

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This paper describes a non-contact and real-time measurement technique of human body potential using ultra-sensitive electrostatic induction. When a participant moves his/her palm to a position approximately 30 cm away from an electrostatic induction sensor, electrostatic induction current flows transiently. It is clarified whether estimation of the human body potential is possible by simultaneously measuring the velocity of the participant’s palm and distance between the participant’s palm and sensor. In addition, even when the participant walks at a position approximately 50 cm away from the electrostatic induction sensor, it is confirmed that the estimation of human body potential is possible.

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Section: Detection Principlementioning

confidence: 99%

A Non-Contact and Real-Time Measurement Technique of Human Body Potential Using Electrostatic Induction Current Accompanied by Human Body Motion

Kurita

2022

Sensors

Self Cite

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“…Human identity recognition typically relies on one or more internal physiological characteristics [2][3][4] (such as fingerprints, iris patterns, palm lines, and other external body features) or behavioral characteristics [5][6][7] (such as typing style, gait, voice rhythm, and other behavioral patterns). Among these, gait recognition is an emerging biometric technology that offers several advantages over fingerprint and face recognition, including non-invasiveness, long-range identification capability, minimal environmental influence, and difficulty in disguising.…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

et al. 2023

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This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.

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“…Therefore, the ambient electric field created by the radiation from the AC powerlines (ever-present in buildings) is altered by the presence of a human target. This change can be measured with Electric Potential Sensors (EPS) and used for both identification of subjects [39] as well positioning of them [40][41][42][43]. Unfortunately, such opportunistic, passive electric sensing is vulnerable to ambient electrical field noise and interference.…”

Section: Introductionmentioning

confidence: 99%

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

et al. 2020

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Passive indoor positioning, also known as Device-Free Localization (DFL), has applications such as occupancy sensing, human-computer interaction, fall detection, and many other location-based services in smart buildings. Vision-, infrared-, wireless-based DFL solutions have been widely explored in recent years. They are characterized by respective strengths and weaknesses in terms of the desired accuracy, feasibility in various real-world scenarios, etc. Passive positioning by tracking the footsteps on the floor has been put forward as one of the promising options. This article introduces CapLoc, a floor-based DFL solution that can localize a subject in real-time using capacitive sensing. Experimental results with three individuals walking 39 paths on the CapLoc show that it can detect and localize a single target's footsteps accurately with a median localization error of 0.026 m. The potential for fall detection is also shown with the outlines of various poses of the subject lying upon the floor.

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Human Identification from Walking Signal based on Measurement of Current Generated by Electrostatic Induction

Cited by 10 publications

References 27 publications

A Non-Contact and Real-Time Measurement Technique of Human Body Potential Using Electrostatic Induction Current Accompanied by Human Body Motion

A Non-Contact and Real-Time Measurement Technique of Human Body Potential Using Electrostatic Induction Current Accompanied by Human Body Motion

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

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