Electric field phase sensing for wearable orientation and localisation applications

Roggen, Daniel; Yazdan, Arash Pour; Morales, Francisco; Prance, R. J.; Prance, H.

doi:10.1145/2971763.2971774

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…When hand motions are identified by the sensor, the time period of the extracted frame is diverse even if the motions were the same types, due to the speed or distance range of the motion. Likewise, the amplitude of the motion signal also tends to change on every new motion since the subject's potential electrostatic state varies through numerous conditions such as textile of the cloth, location, or nearby machines, etc [4,9,11,12]. Thus, it is imperative to conduct normalization in order to be properly trained in deep learning models as normalizing input data is known to be a productive measure to enhance the performance.…”

Section: Normalization Of An Extracted Motion Framementioning

confidence: 99%

“…Meanwhile, non-contact type measures the electric potential signal on the surface of EF sensors induced by the disturbance of the surrounding electric field which is caused by movement of dielectric substances such as human bodies or hands due to coupling effect [6]. A few of non-contact EF sensor systems have been applied in commercial products, while several studies in academic institutions have been reported in application areas of proximity sensing, placement identification, etc [7][8][9][10]. As EF proximity sensing systems are gaining attention, recent studies have been published regarding the area of hand or body motion detection and recognition [11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

Lee¹,

Na²,

Bultakov³

et al. 2022

Computers, Materials &Amp; Continua

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In this paper, we propose a BPR-CNN (Biometric Pattern Recognition-Convolution Neural Network) classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF (Electric Field) sensors. Currently, an EF sensor or EPS (Electric Potential Sensor) system is attracting attention as a next-generation motion sensing technology due to low computation and price, high sensitivity and recognition speed compared to other sensor systems. However, it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion, due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions. This hinders the further utilization of the EF sensing; thus, it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices. In this study, we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject. This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy. After setting motion frames, we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types. Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1% accuracy, 99.25% detection rate, 98.4% motion frame matching rate and 97.7% detection & extraction success rate.

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Section: Normalization Of An Extracted Motion Framementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

Lee¹,

Na²,

Bultakov³

et al. 2022

Computers, Materials &Amp; Continua

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“…In addition, contact sensing has widely been utilized in the field of Health care [8] to monitor the electric signals from our body such as electrocardiogram [9,10], electromyography [11], and electroencephalography. Moreover, some systems that detect a person's existence and compute the localization through capacitive sensing have been devised in [12][13][14][15]. Diverse studies were performed regarding hand motion detection, especially implementing visual processing techniques based on the footage or image recognition through image sensors [16][17][18][19][20][21][22][23].…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…[7] developed a gesture recognition glove device that utilizes capacitive sensors to extract voltage signals, perceiving a certain hand gesture. Likewise, most systems that detect hand motions imply recognizing static motions with certain hand forms such as thumbs-up pose, pouch pose, open-hand pose, or American sign language (ASL) [6,7,12,24,25]. On the other hand, our hand gesture focuses on capturing dynamic motions in a non-contactive fashion such as waving a hand in different directions.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Real-time Interface Control with Motion Gesture Recognition based on Non-contact Capacitive Sensing

Lee¹,

Mandivarapu²,

Ogbazghi³

et al. 2022

Preprint

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Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, and contact sensing interface application such as human-computer interaction. However, as a non-contact proximity sensing scheme is easily affected by the disturbance of peripheral objects or surroundings, it requires considerable sensitive data processing than contact sensing, limiting the use of its further utilization. In this paper, we propose a real-time interface control framework based on non-contact hand motion gesture recognition through processing the raw signals, detecting the electric field disturbance triggered by the hand gesture movements near the capacitive sensor using adaptive threshold, and extracting the significant signal frame, covering the authentic signal intervals with 98.8% detection rate and 98.4% frame correction rate. Through the GRU model trained with the extracted signal frame, we classify the 10 hand motion gesture types with 98.79% accuracy. The framework transmits the classification result and maneuvers the interface of the foreground process depending on the input. This study suggests the feasibility of intuitive interface technology, which accommodates the flexible interaction between human to machine similar to Natural User Interface, and uplifts the possibility of commercialization based on measuring the electric field disturbance through non-contact proximity sensing which is state-of-the-art sensing technology.Preprint. Under review.

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