Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors

Guo, Kai; Zhang, Senhao; Zhao, Shudong; Yang, Hongbo

doi:10.1155/2021/6359403

Cited by 16 publications

(9 citation statements)

References 24 publications

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“…[175] In 2021, Guo et al explored the transplantation of a flexible sensor to a data glove to track the bending changes of the knuckles for simple gesture recognition and rehabilitation training. [176] Similarly, Yang et al employed a flex sensor in a hand-sensing glove to measure the hand motions, which was designed to be attached to a compact and portable hand exoskeleton for bilateral hand training and home-based rehabilitation. [177] Although these sensing technologies are not directly used in teleoperated surgical systems, their ability to capture the motion of human limbs is high, and therefore they can be used in the future development of advanced TSRS user interfaces.…”

Section: Sensing Technologies For Advanced Master Consolesmentioning

confidence: 99%

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Nguyễn

Wong

Thai

et al. 2023

Advanced Sensor Research

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In recent years, advances in modern technology have altered the practice of surgery from open to minimally invasive surgery (MIS) aided by robots. Teleoperated surgical robotic systems (TSRSs) provide numerous significant benefits for MIS over traditional approaches, including improved safety, more efficient and precise surgery, better cosmesis, shorter recovery time, and reduced postoperative pain. Existing TSRSs' master consoles, with improvements in vision systems, designs, and control methods, have significantly enhanced human-robot interactions, resulting in safer and more accurate medical intervention operations. Despite advances, haptic technologies, including sensors, machine assistance, and intuitive devices for user interfaces, are still limited, resulting in less effective usage of TSRSs for surgical operations. This review presents a summary of the emerging TSRSs with a focus on their user interfaces. In addition, advanced sensing, haptic, smart garments, and medical image artificial intelligence (AI) assistance technologies are shown with their potential for use in master consoles of the TSRSs are shown. Finally, a discussion on the need for a smart human-robot interface for TSRSs is given.

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Section: Sensing Technologies For Advanced Master Consolesmentioning

confidence: 99%

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Nguyễn

Wong

Thai

et al. 2023

Advanced Sensor Research

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Section: Scenario Setting Of Muscle Strength Prediction Experiments A...mentioning

confidence: 99%

“…Since the grip gesture is mainly related to finger movements and its function is mainly controlled by the forearm muscle groups [28], the flexible sEMG collection device was placed centrally on the forearm with the subject's forearm flat on the table. Prior to testing, 75% alcohol pads were gently rubbed on the skin of selected muscles to enhance conductivity [67]. After the experiment was prepared, the subjects grasped the force acquisition blocks according to six different levels of force (5 kg, 10 kg, 15 kg, 20 kg, 25 kg, and 30 kg) and the approximate constant force grip strength lasted for 20 s according to the value on the display of the electronic counting grip, with 30 sets of each applied force size.…”

Section: Scenario Setting Of Muscle Strength Prediction Experiments A...mentioning

confidence: 99%

Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation

Liu

Li²,

Zhang³

et al. 2022

Micromachines

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Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.

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“…Inertial sensors have high measurement accuracy but also have drift problems. As inertial sensors have limited adaptability to the palm [11], they are more often applied as supplementary ones to detect the motion of large joints.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Based Hand Contralateral Controlled Functional Electrical Stimulation System

Huang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Contralateral controlled functional electrical stimulation (CCFES) can induce simultaneous movements in patients' bilateral hands. It has been clinically proven to be effective in improving hand motor control and dexterity. sEMG and bending sensor-based data gloves for detecting patients' motor intent have been developed with limitations. sEMG sensor signals are unstable and susceptible to noise. Data gloves composed of bending sensors require complicated calibration and tend to have data drift. In this paper, a LiDAR-based system for hand CCFES is proposed. The method utilized LiDAR to detect the patient's motion intention without contact in CCFES systems. It has been clinically proven that LiDARs can effectively distinguish the different motion amplitudes of hand gestures as quantitative evaluation sensors of functional electrical stimulation (FES). Training data for classifiers were collected from 9 healthy individuals and 15 stroke patients performing 4 gestures, including hand opening, fist clenching, wrist extension, and wrist flexion. The support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (kNN) were verified for their classification performance in offline hand gesture recognition tests. Experiments were also conducted on 6 stroke volunteers to evaluate gestures triggered by FES. The SVM classifier showed excellent classification performance for four hand gestures, with an average F1-score of 0.97±0.05 in offline tests. As for online gesture recognition, an average F1-score of 0.92±0.09 was obtained.In the evaluation experiments, between data from 50% and 100% movement amplitude, paired t-tests showed significant differences. The experimental results indicated that the proposed system showed promise for hand rehabilitation.

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Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors

Cited by 16 publications

References 24 publications

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation

LiDAR-Based Hand Contralateral Controlled Functional Electrical Stimulation System

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