EMG Analysis of Lower Limb Muscles for Developing Robotic Exoskeleton Orthotic Device

Mishra, Ashish Kumar; Srivastava, Amit; Tewari, Ravi Prakash; Mathur, Rakesh

doi:10.1016/j.proeng.2012.07.139

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…According to some studies that studied the EMG values of the normal people in several positions such as running, sitting. The EMG values for normal human body were between 200-350 mv [78]- [80].…”

Section: Electronic Designmentioning

confidence: 99%

Smart Robotic Exoskeleton: Constructing Using 3D Printer Technique for Ankle-Foot Rehabilitation

Salih,

Aboud

2023

Journal of Robotics and Control

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Patients with spinal cord injury (SCI), stroke, and coronavirus patients must undergo a rehabilitation process involving programmed exercises to regain their ability to perform activities of daily living (ADL). This study focuses on the rehabilitation of the foot-ankle joint to restore ADL through the design and implementation of a rehabilitation exoskeleton with three degrees of freedom (abduction/adduction, inversion/eversion, and plantarflexion/dorsiflexion movements). increase the patients cause worker fatigue, emotional exhaustion, a lack of motivation, and feelings of frustration, all contributing to a decrease in work efficacy and productivity. The robotic exoskeleton was developed to overcome this limitation and support the medical rehabilitation section. The main goal of this study is to develop a portable exoskeleton that is comfortable, lightweight, and has a range of motion (ROM) compatible with human anatomy to ensure that movements outside of this range are minimized, the anthropometric parameters of a typical human lower foot have been considered. In addition, it's a home-based rehabilitation device which means the exoskeleton can be used in any environment due to its lightweight and small size to accelerate the rehabilitation process and increase patient comfort. The proposed autonomous exoskeleton structure is designed in Solid Works and constructed with polylactic acid (PLA) plastic, the reason PLA was chosen is its lightweight, available, stiff material, and low cost, using 3D printing technology the exoskeleton was manufacturing. Electromyography (EMG) and angle data were extracted using EMG MyoWare and gyroscope sensors, respectively, to control the exoskeleton. It was evaluated on its own then with 2 normal subjects and 17 patients with stroke, spinal cord injury (SCI), and coronavirus. The limitation that has been faced was that the sessions were limited due to the limited time provided for the study. According to the improvement rate, the exoskeleton has a significant impact on regaining muscle activity and improving the range of motion of foot-ankle joints for the three types of patients. The rate of improvement was 300%, 94%, and 133.3% for coronavirus, SCI, and stoke respectively. These results demonstrate that this exoskeleton can be utilized for physiotherapy exercises.

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Section: Electronic Designmentioning

confidence: 99%

Smart Robotic Exoskeleton: Constructing Using 3D Printer Technique for Ankle-Foot Rehabilitation

Salih,

Aboud

2023

Journal of Robotics and Control

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“…Furthermore, Hu et al [ 34 ] proposed a method to estimate the joint angles of lower limbs (i.e., hip, knee and ankle angles) using the minimal number of IMUs of only four sensors. Mishra et al [ 35 ] and Yin et al [ 36 ] used the surface EMG signals of EMG sensors on different muscles in gait analysis and measured speed to develop EMG-driven speed-control for exoskeleton motion control.…”

Section: Related Workmentioning

confidence: 99%

Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning

Karakish

Fouz

Elsawaf

2022

Sensors

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Achieving a normal gait trajectory for an amputee’s active prosthesis is challenging due to its kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase segmentation are essential parameters in controlling an active prosthesis. Recently, the most practiced algorithm in gait trajectory generation is the neural network. Deploying such a complex Artificial Neural Network (ANN) algorithm on an embedded system requires performing the calculations on an external computational device; however, this approach lacks mobility and reliability. In this paper, more simple and reliable ANNs are investigated to be deployed on a single low-cost Microcontroller (MC) and hence provide system mobility. Two neural network configurations were studied: Multi-Layered Perceptron (MLP) and Convolutional Neural Network (CNN); the models were trained on shank and foot IMU data. The data were collected from four subjects and tested on a fifth to predict the trajectory of 200 ms ahead. The prediction was made for two cases: with and without providing the current phase of the gait. Then, the models were deployed on a low-cost microcontroller (ESP32). It was found that with fewer data (excluding the current gait phase), CNN achieved a better correlation coefficient of 0.973 when compared to 0.945 for MLP; when including the current phase, both network configurations achieved better correlation coefficients of nearly 0.98. However, when comparing the execution time required for the prediction on the intended MC, MLP was much faster than CNN, with an execution time of 2.4 ms and 142 ms, respectively. In summary, it was found that when training data are scarce, CNN is more efficient within the acceptable execution time, while MLP achieves relative accuracy with low execution time with enough data.

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“…As for the muscle activity, the RMS electromyography (EMG) of Tibialis Anterior (TA) and Gastrocnemius (G) muscles were collected using a Biopac instrument and Acqknowledge software. Firstly, the raw EMG data were gathered with a sampling frequency of 1000 Hz [37]- [40]. Then, the raw EMG is filtered using a low pass filtered (cut-off frequency of 6 Hz) and rectified before the RMS EMG is calculated [8].…”

Section: A Data Collectionmentioning

confidence: 99%

“…Replacing the muscle's work using an actuator that works in unison will decrease the muscle activity [42], [43]. TA works in dorsiflexion, where it keeps ω during P1 [44], and ensures toe clearance during P4 [37]. Meanwhile, G is responsible for plantarflexion, where it maintains the forward force during P2 to avoid over-extending [45].…”

Section: B Data Processing and Analysismentioning

confidence: 99%

Improving Passive Ankle Foot Orthosis System Using Estimated Ankle Velocity Reference

Adiputra¹,

Rahman

Ubaidillah

et al. 2020

IEEE Access

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This study aims to investigate an appropriate ankle velocity reference (ωref), which is the average ankle velocity of a certain healthy subject when walking with a certain walking speed. The goal is to improve a Passive Controllable Ankle Foot Orthosis (PICAFO) by implementing the ankle velocity reference. Firstly, the function to estimate ωref, based on walking speed and body mass index (BMI), is obtained from 16 able-bodied subjects walking gait data. The effect of controlled stiffness (based on ωref) to the user's ankle kinematics and muscle activity was evaluated by comparing it to other settings, such as walking barefooted and various constant damping stiffness (0%, 30%, 60%, and 100% of the maximum damping stiffness). Two able-bodied subjects (normal and overweight) participated in the evaluation, where they had to walk at two different walking speeds (1 and 2 km/h). The result showed that ankle kinematics and muscle activity were improved when ω was controlled during walking speed of 1 km/h for both subjects. In terms of ankle kinematics, the toe clearance occurred, and walking stability increased. In terms of muscle activity, the average muscle activity had reduced by ~29% for the normal subject and by ~57% for an overweight subject, which shows that PICAFO provides necessary damping stiffness to replace the muscle works partially. In the future, by using the ωref based on walking speed and BMI, the therapists can skip the trial and error process of finding an appropriate PICAFO prescription for a post-stroke patient.

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EMG Analysis of Lower Limb Muscles for Developing Robotic Exoskeleton Orthotic Device

Cited by 16 publications

References 7 publications

Smart Robotic Exoskeleton: Constructing Using 3D Printer Technique for Ankle-Foot Rehabilitation

Smart Robotic Exoskeleton: Constructing Using 3D Printer Technique for Ankle-Foot Rehabilitation

Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning

Improving Passive Ankle Foot Orthosis System Using Estimated Ankle Velocity Reference

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