Integration of Forearm sEMG Signals with IMU Sensors for Trajectory Planning and Control of Assistive Robotic Arm

Schabron, Bridget; Reust, Adam; Desai, Jaydip; Yihun, Yimesker

doi:10.1109/embc.2019.8856699

Cited by 6 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Surface EMG signals of participants were recorded and used to train custom neural networks for each test subject. Based on our previous research on hand gesture-based powered wheelchair control and virtual robotic arm control, it was decided to use the scaled conjugate backpropagation type of neural network, due to its higher accuracy and shorter training time [ 24 , 25 ]. The MATLAB neural network pattern recognition toolbox offers several different graphical representations of the accuracy of neural networks, including confusion matrices and error histograms.…”

Section: Resultsmentioning

confidence: 99%

Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Schabron

Desai

Yihun

2021

Sensors

Self Cite

View full text Add to dashboard Cite

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.

show abstract

Section: Resultsmentioning

confidence: 99%

Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Schabron

Desai

Yihun

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, for decades, researchers in academia and industry have tried to develop more innovative and more inclusive types of interfaces. Voice control and speech recognition systems [40], electrooculography-based interfaces [41], eye-gaze detection devices [42], vision-based gesture recognition algorithms [43], and bio signal-based interfaces [44] are developed to translate user intentions to robot commands to perform specific tasks. In addition to the standard interfaces, various approaches were also proposed for intelligent detection and grasping of objects within the workspace of the ARM [45].…”

Section: Assistive Robotic Manipulatorsmentioning

confidence: 99%

Human-Robot Interaction in Rehabilitation and Assistance: a Review

Mohebbi

2020

Curr Robot Rep

View full text Add to dashboard Cite

Purpose of Review Research in assistive and rehabilitation robotics is a growing, promising, and challenging field emerged due to various social and medical needs such as aging populations, neuromuscular, and musculoskeletal disorders. Such robots can be used in various day-today scenarios or to support motor functionality, training, and rehabilitation. This paper reflects on the human-robot interaction perspective in rehabilitation and assistive robotics and reports on current issues and developments in the field. Recent Findings The survey on the literature reveals that new efforts are put on utilizing machine learning approaches alongside novel developments in sensing technology to adapt the systems with user routines in terms of activities for assistive systems and exercises for rehabilitation devices to fit each user's need and maximize their effectiveness. Summary A review of recent research and development efforts on human-robot interaction in assistive and rehabilitation robotics is presented in this paper. First, different subdomains in assistive and rehabilitation robotic research are identified, and accordingly, a survey on the background and trends of such developments is provided.

show abstract

“…The existing human-machine collaboration technology is usually implemented by using a series of equipment to detect the signals issued by the human body, such as the muscle electrical signals [3], or the coordination of camera and machine vision. These methods have high accuracy [4], but a lot of computing resources are consumed, and there are other problems such as low sampling rate, complex configuration, long calibration time and so on.…”

Section: Introductionmentioning

confidence: 99%

An IMU-based robotic manipulator for versatile applications

Pang

2023

ACE

View full text Add to dashboard Cite

With the development of sensory technologies, the inertial measurement unit (IMU) has been emerging widely. In this paper, the IMU sensor is used to facilitate the versatile applications of robotic manipulators. The Kalman filter fuses the raw measurements of an IMU mounted on human hands to obtain the real-time attitude information. The contribution is to manipulate the robotic arm dexterously by the motion guidance of human hands, to achieve human-robot interaction. The experimental results evaluate the attitude preciseness and associated manipulators locomotion. In the future work, the proposed interaction method will be applied in manipulation tasks such as grasping, inspection and so on.

show abstract

Integration of Forearm sEMG Signals with IMU Sensors for Trajectory Planning and Control of Assistive Robotic Arm

Cited by 6 publications

References 11 publications

Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Human-Robot Interaction in Rehabilitation and Assistance: a Review

An IMU-based robotic manipulator for versatile applications

Contact Info

Product

Resources

About