Intelligent recognition system for hand gestures

Honda, Yoshiyuki; Weber, Stefan; Lueth, Tim C.

doi:10.1109/cne.2007.369746

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…According to the basic equations for the plate capacitor, if there are pressures between the human body and the sensing band, the capacitance signals will be positively correlated to the pressure of that spot within a proper range. However, as the capacitance signal changes were determined by the distance between the human body and the metal electrodes, not just the pressure, measuring the capacitance signals can obtain more sensitive information than only pressure signals (Honda et al, 2007 ).…”

Section: Discussionmentioning

confidence: 99%

Forearm Motion Recognition With Noncontact Capacitive Sensing

et al. 2018

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This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness. Nine motion patterns combined with 16 motion transitions are investigated on seven participants. We also designed an automatic data labeling method based on inertial signals from the measured hand, which greatly accelerated the training procedure. With the capacitive sensing system and the designed recognition algorithm, the method produced an average recognition of over 92%. Correct decisions could be made with approximately a 347-ms delay from the relaxed state to the time point of motion initiation. The confounding factors that affect the performances are also analyzed, including the sliding window length, the motion types and the external disturbances. We found the average accuracy increased to 98.7% when five motion patterns were recognized. The results of the study proved the feasibility and revealed the problems of the noncontact capacitive sensing approach on upper-limb motion sensing and recognition. Future efforts in this direction could be worthwhile for achieving more promising outcomes.

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Section: Discussionmentioning

confidence: 99%

Forearm Motion Recognition With Noncontact Capacitive Sensing

et al. 2018

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Section: Introductionmentioning

confidence: 99%

“…A human movement intention study was conducted using a tactile sensor to detect flexion changes on the skin surface [ 37 , 38 , 39 , 40 ]. Furthermore, researchers have developed a wearable upper-arm sensor comprising multiple tactile sensors to detect movement [ 37 , 38 , 39 , 40 ]. In addition, a study was conducted using a thimble-shaped tactile sensor that was wearable on the finger to detect its movement intention [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Wearable Sensor for Forearm Motion Detection Using a Carbon-Based Conductive Layer-Polymer Composite Film

Park¹

2022

Sensors

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In this study, we developed a fabrication method for a bracelet-type wearable sensor to detect four motions of the forearm by using a carbon-based conductive layer-polymer composite film. The integral material used for the composite film is a polyethylene terephthalate polymer film with a conductive layer composed of a carbon paste. It is capable of detecting the resistance variations corresponding to the flexion changes of the surface of the body due to muscle contraction and relaxation. To effectively detect the surface resistance variations of the film, a small sensor module composed of mechanical parts mounted on the film was designed and fabricated. A subject wore the bracelet sensor, consisting of three such sensor modules, on their forearm. The surface resistance of the film varied corresponding to the flexion change of the contact area between the forearm and the sensor modules. The surface resistance variations of the film were converted to voltage signals and used for motion detection. The results demonstrate that the thin bracelet-type wearable sensor, which is comfortable to wear and easily applicable, successfully detected each motion with high accuracy.

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“…A touch system was introduced for gesture recognition of the prosthetic hand [7]. The system is based on 54 force sensitive resistors (FSRs) and a classification algorithm was used to classify hand gestures.…”

Section: Introductionmentioning

confidence: 99%

Detecting muscle contractions using strain gauges

et al. 2016

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Myoelectric prostheses aim to help amputees to experience partial function of the absent organ. The sensors usually used to control the prostheses are surface electromyography (sEMG) electrodes, of which the number tends to increase with the increase of the number of degrees of freedom in the recent prostheses, i.e. dozens of sensors today. However, sEMG requires a high sampling frequency, traditionally about 1000 Hz, which drastically limits the number of sensors that the processors can manage. The objective is to develop a device enabling to measure muscle contractions (MCs) with a sampling frequency compared with the movement frequencies. Strain gauges are known for their accuracy, so using them to detect MCs could help to predict the movement intentions of the amputee. The designed devise includes the integration of four strain gauges in silicone rubber that is similar to human skin. The reliability of the sensor results is demonstrated by a comparison with Ag/AgCl electrodes of an electromyography system. The correlation coefficient is very high (0.89) between the tensions measured by the sEMG and the strain gauges. So the advantage of a low sampling frequency compared with sEMG is the potential development of matrices with many strain gauges.

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Intelligent recognition system for hand gestures

Cited by 14 publications

References 3 publications

Forearm Motion Recognition With Noncontact Capacitive Sensing

Forearm Motion Recognition With Noncontact Capacitive Sensing

Wearable Sensor for Forearm Motion Detection Using a Carbon-Based Conductive Layer-Polymer Composite Film

Detecting muscle contractions using strain gauges

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