Application of magnetic field sensors for hand gesture recognition with neural networks

Meier, Phil; Rohrmann, Kris; Sandner, Marvin; Prochaska, Marcus

doi:10.1109/lifetech.2019.8884006

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…In addition to that, researchers pointed that the third aspect that is related to hardware issues is the need for computational power which posed a challenge if applied to a portable device. Therefore, the computation was done on board the wearable device to limit the amount of information that needed to be sent wirelessly and to keep the power consumption of the system low [64], [65], [66].…”

Section: ) the Challenges Related To Hardwarementioning

confidence: 99%

“…The majority of the papers reviewed were focused on each selected author's data set. The vast majority of studies have focused on a small number of gestures such as some alphabets [27], [31], [107] [66], [34] , [108], [107] and some numbers [65], [51], few words gestures [8], [47], [101], [89] or a combination of the alphabets, numbers, and words [109], [82]. Figure 8 indicated the American Sign Language (ASL) alphabets and numbers [57].…”

Section: Datasetmentioning

confidence: 99%

“…When the optical fiber is bent, the light intensity at the receiving end changes, and thus the bending angle can be detected [98], [21].Another sensor was exploited to detect the shape of the finger by an applied magnetic field sensor. The sensor uses a magnetic induction technology which provides high accuracy in measurement and the ability to measure a high degree of freedom of hand movements (DOF) [65], is lightweight, and has a low manufacturing cost [14]. In addition to this, a different sensor that was used for measuring the bending of the fingers is the strain sensor.…”

Section: Sensormentioning

confidence: 99%

“…Fuzzy logic has been utilized in various of sectors that require human decision-making, including the recognition of Sign Language [88]. Furthermore, the Neural Network algorithm has been used to detect static gestures [65], [72]. For hand gesture recognition, a combination of fuzzy logic and neural networks has been used to improve an individual's hand gesture recognition rate [111].…”

Section: Gesture Recognition Techniquesmentioning

confidence: 99%

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A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

et al. 2022

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Sign language is the predominant mode of communication for the Deaf community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor-based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

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Section: ) the Challenges Related To Hardwarementioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Sensormentioning

confidence: 99%

Section: Gesture Recognition Techniquesmentioning

confidence: 99%

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A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

et al. 2022

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“…[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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A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

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Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

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Application of magnetic field sensors for hand gesture recognition with neural networks

Cited by 11 publications

References 17 publications

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

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

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

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