Detecting muscle contractions using strain gauges

Zizoua, Cherif; Raison, Maxime; Boukhenous, Samir; Attari, Mokhtar; Achiche, Sofiane

doi:10.1049/el.2016.2986

Cited by 11 publications

(8 citation statements)

References 8 publications

(8 reference statements)

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“…[58] uses a predefined threshold, which is calibrated at the beginning of the measurements. Another technique is to set the threshold to the RMS of the last contraction and to compare this with the RMS of the incoming frames while incrementally decreasing it with a decay parameter.…”

Section: Prior Art and Comparisonsmentioning

confidence: 99%

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Ergeneci¹,

Gokcesu

Ertan³

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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Abstract-Wearable technology has gained increasing popularity in the applications of healthcare, sports science and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition and fatigue tracking. To this end, we introduce an embedded, 8-channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multi-channel data acquisition unit. For the first stage, we propose a low cost, dry and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multi-channel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used, commercially available product and our data acquisition system achieves 4.583 dB SNR gain with 98.9784% accuracy in the detection of the contractions.

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Section: Prior Art and Comparisonsmentioning

confidence: 99%

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Ergeneci¹,

Gokcesu

Ertan³

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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show abstract

“…However, sEMG sensor needs a higher sampling frequency. This will limits the number of sensors that the processors can manage [20]. The objective of this paper is to develop a device which enable us to measure muscle strain/ Muscle Contractions (MCs) with a sampling frequency compared with the movement frequencies.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…5. The advantage of a low sampling frequency compared with sEMG is the potential development of the usage of strain gauges [20].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…To collect the accurately estimated data, the expert therapists consider the general condition of the patients. These are Flaccid, Intermediate and Spastic conditions [20]. Based on these Conditions, the expert physical therapist classify the strain measurements and the corresponding pain level using his past experience and by asking the patients.…”

Section: Data Acquisitionmentioning

confidence: 99%

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Design and Analysis of Fuzzy Based Proportional-Integral-Derivative Controller for Elbow-Forearm Rehabilitation Robot

Addisie¹,

Weldegiorgis²,

Amertet³

2020

IJIGSP

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Nowadays, the use of Rehabilitation Robots for stroke patients has been growing rapidly. However, there was a limited scope of using such Rehabilitation Robots for patients suffer from an accidental physical fracture. Since the pain condition of such accidents needs a critical treatment, precise control of such robotic manipulators is mandatory. This paper presents the design and control of the Elbow-Forearm Rehabilitation Robot by considering the pain level of the patient. This design consists of the mechatronic design processes including mechanical design, controller design, and Virtual prototyping using ADAMS-MATLAB Co-simulation. The pain level is estimated using three parameters i.e the patient general condition, the muscle strain, and the duration of exercise from the beginning of rehabilitation. Based on these three input parameters, the manipulator's desired range of motion has been determined using the Fuzzy Logic System. The output of this fuzzy logic system would be an input to the main control system. ADAMS-MATLAB Cosimulation is carried out based on three reference inputs i.e Step, sinusoidal and the proposed fuzzy reference input. Using step input, we have discussed the step response characteristics of the developed system. The Co-simulation of the ADAMS dynamic model is realized with a 30 degree oscillating motion by providing a sinusoidal input. Finally, using the developed fuzzy reference input, we have done a Co-simulation of ADAMS plant. The simulation result demonstrates that the proposed PID controller with gains Kp=0.001 and Ki=0.01 yields 99.6% of accuracy in the tracking of the reference input as compared to the simulation without introducing controller which has an accuracy of 94.9%. The simulation also shows that derivative gain (Kd) of the PID controller has no effect on the system so that it is over damping system. From the above three simulation schemes, we can conclude that the Elbow-Forearm rehabilitation robot could be controlled as per the desired signal. Since this desired signal is developed from the pain level of the patient, we can say that the system is controlled as per the pain level of the patient.

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“…In addition, the ability to incorporate sEMG electrodes with other types of physiological sensors for multimodal measurements is also important in several applications. [ 7,8,15–19 ] For example, a combined sEMG and force sensor system is essential for evaluating the muscle strength level during the isometric contraction of skeleton muscles. [ 16 ] Despite the existing reports, the concurrent collection of the sEMG signals and other epidermal physiological parameters remains as a challenge due to the lack of mechanically compliant multifunctional sensors that match the physical properties of the skin.…”

Section: Introductionmentioning

confidence: 99%

Stretchable, Multifunctional Epidermal Sensor Patch for Surface Electromyography and Strain Measurements

Wang

Cai

Hao

et al. 2021

Advanced Intelligent Systems

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The concurrent collection of surface electromyography (sEMG) and strain signals is important for many applications, such as human–machine interaction, sign language recognition, and clinical evaluation of muscle function. Nevertheless, the conventional sensor systems made of rigid, bulky components cannot provide a reliable, conformal interface for accurate, continuous measurements of the epidermal physiological signals. Herein, a skin‐interfaced, multifunctional epidermal sensor patch with characteristics of mechanical softness, large stretchability, and wearable conformability for multimodal measurements of sEMG signals and associated skin deformations from various muscle activities and joint motions is reported. The sensor patch features two pairs of stretchable sEMG electrodes and two thin, miniaturized strain sensors, which are connected by stretchable filamentary serpentine interconnects in an open‐meshed structure. Experimental and computational studies reveal the design and operation of the sensor patch, which exhibit stable and repetitive performance even under a 30% stretching strain level. Demonstrations of the sensor patch on the wrist for simple sign language recognition and on the lower back for the flexion‐relaxation phenomenon illustrate its potential for the comprehensive assessment of the muscle activities and related motions of muscle joints.

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Detecting muscle contractions using strain gauges

Cited by 11 publications

References 8 publications

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Design and Analysis of Fuzzy Based Proportional-Integral-Derivative Controller for Elbow-Forearm Rehabilitation Robot

Stretchable, Multifunctional Epidermal Sensor Patch for Surface Electromyography and Strain Measurements

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