A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors

Jung, Pyeong-Gook; Lim, Gukchan; Kim, Seonghyok; Kong, Kyoungchul

doi:10.1109/tii.2015.2405413

Cited by 107 publications

(61 citation statements)

References 17 publications

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“…In principle, specific muscular activities are directly related to the changes of its longitudinal contraction or elongation and associated expansion of the cross‐sectional area . Based on this principle, we have shown the capacity of the EII sensors to precisely track minute changes of the local skin interfacial pressure (Figure S8, Supporting Information).…”

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confidence: 87%

Imperceptible Epidermal–Iontronic Interface for Wearable Sensing

2017

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Recent development of epidermal electronics provides an enabling means to continuous monitoring of physiological signals and close tracking of physical activities without affecting quality of life. Such devices require high sensitivity for low-magnitude signal detection, noise reduction for motion artifacts, imperceptible wearability with long-term comfortableness, and low-cost production for scalable manufacturing. However, the existing epidermal pressure sensing devices, usually involving complex multilayer structures, have not fully addressed the aforementioned challenges. Here, the first epidermal-iontronic interface (EII) is successfully introduced incorporating both single-sided iontronic devices and the skin itself as the pressure sensing architectures, allowing an ultrathin, flexible, and imperceptible packaging with conformal epidermal contact. Notably, utilizing skin as part of the EII sensor, high pressure sensitivity and high signal-to-noise ratios are achieved, along with ultralow motion artifacts for both internal (body) and external (environmental) mechanical stimuli. Monitoring of various vital signals, such as blood pressure waveforms, respiration waveforms, muscle activities and artificial tactile sensation, is successfully demonstrated, implicating a broad applicability of the EII devices for emerging wearable applications.

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confidence: 87%

Imperceptible Epidermal–Iontronic Interface for Wearable Sensing

2017

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“…As an alternative to sEMG, other sensors can monitor the mechanical muscular activity, and some are briefly presented below. A pressure sensors array coupled to air-bladders mounted on an armband was proposed to detect hand motion (accuracy of 90%) by monitoring the swelling of muscles (Jung et al, 2015). The air bladders are cumbersome, uncomfortable, and not widely adaptable.…”

Section: Introductionmentioning

confidence: 99%

A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition

et al. 2020

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Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.

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“…Noninvasive methods mainly make measurements on the skin. In recent years, many technologies have been developed for wearable computing, such as fall detector [2], motion of fingers measurement [3], gesture recognition [4], etc. Meanwhile, more attentions have been paid to wearable continuous body temperature measurement (CBTM) [5]- [15].…”

Section: Introductionmentioning

confidence: 99%

Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks

Song

Zeng

Wang

et al. 2018

IEEE Trans. Ind. Inf.

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Continuous body temperature measurement (CBTM) is of great significance for human health state monitoring. To avoid interfering with users' daily activities, CBTM is usually achieved using wearable noninvasive thermometers. Current wearable noninvasive thermometers employ steady-state models used in nonwearable thermometers; as a result, the reaction time is long and the measurement can be disturbed by users' activities. However, there is no work to solve these issues. In this paper, first, differences between wearable and nonwearable temperature measurement are analyzed. Second, the relationship among the human body temperature, the skin temperature, and the device temperature is modeled based on artificial neural networks (ANNs). Third, this paper proposes a novel multiple ANNsbased wearable CBTM method. Experiments show that the reaction time of the proposed method is about one-tenth of that of other popular wearable noninvasive CBTM methods, while the accuracy and the robustness are improved.Index Terms-Artificial neural network (ANN), continues body temperature measurement (CBTM), noninvasive, wearable computing.

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A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors

Cited by 107 publications

References 17 publications

Imperceptible Epidermal–Iontronic Interface for Wearable Sensing

Imperceptible Epidermal–Iontronic Interface for Wearable Sensing

A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition

Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks

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