A capacitive proximity sensing scheme for human motion detection

Arshad, Atika; Khan, Sheroz; Alam, A. H. M. Zahirul; Kadir, Kushsairy; Tasnim, Rumana; Ismail, Ahmad Fadzil

doi:10.1109/i2mtc.2017.7969712

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…Computer vision-based fall detection methods detect whether a fall has occurred by passively acquiring human motion information from the monitoring device and processing the acquired video or image. The vision-based method does not require the user to wear any device and has a good user experience and high detection accuracy [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

Zhao

Zhang

Shang

2022

Sensors

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Falls pose a great danger to social development, especially to the elderly population. When a fall occurs, the body’s center of gravity moves from a high position to a low position, and the magnitude of change varies among body parts. Most existing fall recognition methods based on deep learning have not yet considered the differences between the movement and the change in amplitude of each body part. Besides, some problems exist such as complicated design, slow detection speed, and lack of timeliness. To alleviate these problems, a lightweight subgraph-based deep learning method utilizing skeleton information for fall recognition is proposed in this paper. The skeleton information of the human body is extracted by OpenPose, and an end-to-end lightweight subgraph-based network is designed. Sub-graph division and sub-graph attention modules are introduced to add a larger perceptual field while maintaining its lightweight characteristics. A multi-scale temporal convolution module is also designed to extract and fuse multi-scale temporal features, which enriches the feature representation. The proposed method is evaluated on a partial fall dataset collected in NTU and on two public datasets, and outperforms existing methods. It indicates that the proposed method is accurate and lightweight, which means it is suitable for real-time detection and rapid response to falls.

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Section: Related Workmentioning

confidence: 99%

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

Zhao

Zhang

Shang

2022

Sensors

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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%

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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“…(3) Displacement sensing: As the object being sensed moves a distance from the electrodes, the capacitance changes. The representative applications working in this modality are detections of displacement, distance [24], location [25], movement orientation [27] and human-computer interaction [28].…”

Section: Background a Definition Of Capacitive Displacement Sensingmentioning

confidence: 99%

A Review on Applications of Capacitive Displacement Sensing for Capacitive Proximity Sensor

et al. 2020

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Capacitive proximity sensors (CPSs) are ubiquitous because of their simple design, low cost and low consumption. Capacitive displacement sensing, as one of the three sensing modalities, works for long distance and can be unitized to measure more physical quantities compared with capacitive volume and deformation sensing. In this paper, we firstly introduce the concept of capacitive displacement sensing. After that, we present applications of capacitive displacement sensing under three broad categories: distance measurements, indirect measurements, and the applications applied in smart environments. Finally, we discuss the challenges and possible solutions for CPSs development. We show that both the detection range and accuracy of CPS can be improved by multi-sensor fusion, and the application scenarios can be extensive through machine/deep learning approaches. We aim to provide a comprehensive, and state-of-theart review of the capacitive displacement sensing, and inspire more researchers and developers to find wide application perspectives.INDEX TERMS Capacitive proximity sensor (CPS), capacitive displacement sensing, distance measurement, indirect measurement, smart environment.

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A capacitive proximity sensing scheme for human motion detection

Cited by 17 publications

References 10 publications

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

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

A Review on Applications of Capacitive Displacement Sensing for Capacitive Proximity Sensor

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