A stretchable, conductive rubber sensor to detect muscle contraction for prosthetic hand control

Bifulco, Paolo; Esposito, Daniele; Savino, Sergio; Niola, Vincenzo; Iuppariello, Luigi; Cesarelli, Mario

doi:10.1109/ehb.2017.7995389

Cited by 22 publications

(25 citation statements)

References 15 publications

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“…Since, especially in biomedical applications, these misinterpretations can provoke dangerous situations such as undesired prosthesis movements, alternative or redundant approaches would be beneficial. In addition to mechanical or optical measurement setups, the passive electrical characteristics of muscles provide information regarding contractions and relaxations [5], [10]- [13]. The determination of the electrical bioimpedance of muscles or muscle regions is called electrical impedance myography (EIM) and is still a niche field in biomedical engineering [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-Frequency Impedance Myography: The PhaseX Effect

Kusche¹,

Ryschka²

2021

IEEE Sensors J.

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Muscle contraction is often detected via EMG in prosthetics. However, signal disturbances due to electrode motions can lead to misinterpretations. Therefore, alternative measurement approaches are desired to increase the reliability of the results. In this work, a novel approach based on impedance myography is proposed. By means of an equivalent circuit of a muscle, its electrical characteristics during contractions are analyzed. In this analysis, a new biomedical marker named the PhaseX Effect is described. This effect is based on the specific behavior of the phase response when the muscle is contracted and is interesting due to its high signal robustness and low signal processing requirements. The resilience of this effect against electrode motion is also analyzed. Measurements of the complex impedance myography spectra are performed on the forearms of three subjects during relaxation and contraction of the corresponding muscle. The subject measurements show the expected behavior of the muscle model. Actual muscle contractions can easily be detected via a simple analysis of the phase response. For a better visualization, the measurements are repeated while acquiring a synchronized video of the moving forearm. The particular effect of the phase response during muscle contraction can be used as a new marker that can be beneficial in several applications such as prostheses control. The PhaseX Effect has high reliability and low signal processing requirements, making it advantageous over other muscle activity markers. The combination of a reliable marker and simple signal analysis promises to become a new method for prostheses control.

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Section: Introductionmentioning

confidence: 99%

Multi-Frequency Impedance Myography: The PhaseX Effect

Kusche¹,

Ryschka²

2021

IEEE Sensors J.

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“…The encouraging results obtained with few sensors suggest the possibility to adopt this HMI also in hand prosthesis control (Polisiero et al, 2013;Bifulco et al, 2017;Sreenivasan et al, 2018), thanks to the similarity of the FSR-based sensors outputs and the EMG-LE. Indeed, the small size and flatness of the sensors make it possible to embed them inside the prosthesis socket.…”

Section: Discussionmentioning

confidence: 84%

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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“…The device reads the muscle contractions by using a conductive rubber stretch sensor, which has been shown to directly detect the contractions of these muscles and produce a similar response to those shown when using a surface EMG sensor. The underlying mechanism correlates the resistance change with the stretch of the sensor, which influences the output voltage and allows for the detection of circumference change of the forearm [10]. The stretch sensor was characterized by conducting static tests that measured the voltage output of the sensor as it was stretched (Fig.…”

Section: Methodsmentioning

confidence: 99%

Haptic Neurofeedback Device for Parkinson’s Patients

Krigbaum

Rascon

Patil

et al. 2019

2019 Design of Medical Devices Conference

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A method for the design and development of a feedback device for the treatment of focal dystonic symptoms of Parkinson’s patients is described in this work. This device utilizes haptic feedback produced via a soft pneumatic actuator incorporating zero-volume chambers and channels, to signal to the patient that they are falling into a dystonic pattern so that they themselves can manually break out of the pattern preserving and prolonging the use of the afflicted limb, in this case the hand. The system of detection for the dystonic symptoms is a conductive rubber stretch sensor that detects changes to the circumference of the forearm as the muscles begin to involuntarily contract. The signal from the sensor then feeds into a microcontroller that will activate the onboard pump that will in-turn pulse the soft pneumatic actuator producing a gentle but noticeable haptic sensation on the patient’s arm.

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A stretchable, conductive rubber sensor to detect muscle contraction for prosthetic hand control

Cited by 22 publications

References 15 publications

Multi-Frequency Impedance Myography: The PhaseX Effect

Multi-Frequency Impedance Myography: The PhaseX Effect

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

Haptic Neurofeedback Device for Parkinson’s Patients

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