In the rehabilitation of upper limb function impaired by stroke, facilitating the coordinated activation of multiple muscles is desirable. This study aims to analyze the coordination patterns of the tonic and phasic components of EMG during a reaching task and to investigate how the phasic component changes in relation to reaching speed. The analysis focused on the shoulder and elbow joints. EMG was recorded at five different speeds, with the slowest speed selected to represent the tonic component. The tonic component was then removed from the total EMG at the other four speeds to extract the phasic component. Correlation coefficients were calculated between the tonic component and joint angles, as well as between the phasic component and joint angular accelerations. For the tonic component, as joint angle increased during reaching, muscle activation also increased to counteract gravitational moments and enhance joint stiffness. For the phasic component, as reaching speed increased, the correlation between acceleration-deceleration patterns and muscle activation also increased. This suggests a greater synergistic contraction for enhanced acceleration and deceleration, as well as increased antagonistic contraction to ensure dynamic stability during faster movements
Quick picking and heavy lifting are the most common problems in current workplaces. They can cause lumbar muscle damage. The operator then must spend energy, time, and money for recovery or rehabilitation. To solve this problem, we developed a passive-type assistive suit using air mesh material, elastic band, and wire. To determine the strength support effect of the passive-type assistive suit, electromyography (EMG) was performed for eight muscles and the maximum voluntary contraction (MVC) was analyzed when lifting weights of 0%, 15%, and 30% of the subject’s weight in a Semisquat motion. Results showed that MVC increased as the weight of the heavy object increased. However, its increase was not proportional to the decrease in MVC according to the presence or absence of assistive suits or the weight of the heavy object. The highest MVC was observed for the erector spinae muscle under all conditions. The greatest decrease in MVC according to working clothes was measured for the vastus lateralis muscle (lifting: 17.7±2.95%, lowering: 18.3±0.55%). These results show that lifting work performed while wearing a passive-type assistive suit using wires and elastic bands is effective in assisting muscle activity.
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EMG and Usability Assessment of Adjustable Stiffness Passive Waist-Assist Exoskeletons for Construction Workers Jung Sun Kang, Bo Ra Jeong, Eung-Pyo Hong, Bok Man Lim, Byung June Choi, Youn Baek Lee, Yun Hee Chang International Journal of Precision Engineering and Manufacturing.2025; 26(1): 227. CrossRef
Carrying heavy objects in agricultural and industrial sites is the most basic labor, which requires a lot of energy. Many equipment such as crane, chain block, elevator, and forklift truck has been developed to reduce human power. Nevertheless, many tasks require human labor. In addition, rapid aging is increasing musculoskeletal diseases in industrial workers. Consequently, various muscle auxiliary wear robots and devices are being developed. In this study; a passive upper limbs exoskeleton (H-Frame) was developed to help carry over 20 kg of weight in industrial and agricultural sites. For the functional test of the developed H-Frame, tests were carried out for 20, 30, and 40 kg of each box. To measure the objective and numerical data of the H-Frame, various sensor values such as EMG (Electromyography), harness compression force sensor, and load cell value of side support and rope were measured. EMG and metabolic experiments were also performed on 8 subjects before and after wearing the device. The average value of the upper extremity muscle showed a 44% reduction effect after wearing. The device helped the wearer when carrying heavy objects. It could help prevent musculoskeletal diseases in industrial and agricultural fields.
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Comparative Analysis between IMU Signal-based Neural Network Models for Energy Expenditure Estimation Chang June Lee, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2024; 41(3): 191. CrossRef
Evaluation of local muscle fatigue has been conducted over past decades to investigate the process of fatigue accumulation and to reduce effect of fatigue in EMG field. The purpose of this study was to investigate fatigue in isotonic contractions, which can inflict the same fatigue on the subject during dynamic contractions. Local muscle fatigue was measured by changing the load level and exercise time in dumbbell curl comprising isotonic contractions through power spectrum changes. Five healthy males and five healthy females performed dumbbell curls with 1 kg load for two minutes, and on other days when no fatigue occurred due to the previous exercise, performed for one minute with a 2 kg load. The muscle fatigue was estimated by median frequency before and after fatigue, the decrease was greater than in the trial wherein a load of 2 ㎏ was applied for one minute than in the trial where a load of 1 kg was applied for two minutes for the females. The decrease in the median frequency is quantitative data indicated by the slowing of the motor unit actional potential (MUAP), suggesting exercise intensity is more sensitive to the slowing of the MUAP than the exercise duration.
Many of the workers are exposed to work that burdens the musculoskeletal system, and musculoskeletal diseases, such as low back pain, are increasing every year. Various muscle support systems, such as wearable robots, have been developed to prevent musculoskeletal diseases at industrial sites, but the system is bulky. Therefore, the total weight is high, it is inconvenient to wear, and the wearer cannot freely perform the activities when power is not supplied. In this paper, in order to compensate for the shortcomings of the hard-type wearable robot system, a soft-type wearable suit using an elastic band was manufactured so that it is light and portable, as it does not require an actuator. The experiment was conducted to verify the effect of muscle strength assistance through an experiment (Measurement of Maximum Waist Torque and Measurement of the Approximate Dose) on the effect of the soft wearable suit. In addition, by making two different types of elastic bands in the wearable suit, it was possible to classify the more effective types for the waist and lower extremities according to the elasticity by comparing the muscle strength assisting effect according to the elastic band.
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EMG and Usability Assessment of Adjustable Stiffness Passive Waist-Assist Exoskeletons for Construction Workers Jung Sun Kang, Bo Ra Jeong, Eung-Pyo Hong, Bok Man Lim, Byung June Choi, Youn Baek Lee, Yun Hee Chang International Journal of Precision Engineering and Manufacturing.2025; 26(1): 227. CrossRef
Development of lifting-assistive passive functional pants for construction works Jin Zhi Chen, Jeong Eun Yoon, Zi Ying Liu, Sung Kyu Lee, Sumin Helen Koo Textile Research Journal.2025;[Epub] CrossRef
Effects of the Wearable Assistive Suit on Muscle Activity during Lifting Tasks Kwang Hee Lee, Chul Un Hong, Mi Yu, Tae Kyu Kwon Journal of the Korean Society for Precision Engineering.2024; 41(1): 47. CrossRef
Design development and evaluation of arm movement-assistive suits for lifting and movement for industrial workers considering wearability Jiwon Chung, Jung Eun Yoon, Soah Park, Hyunbin Won, Suhyun Ha, Sumin Helen Koo International Journal of Industrial Ergonomics.2024; 103: 103616. CrossRef
Enhancing wearability: designing wearable suit platforms for industrial workers Jiwon Chung, Hyunbin Won, Hannah Lee, Soah Park, Hyewon Ahn, Suhyun Pyeon, Jeong Eun Yoon, Sumin Koo International Journal of Clothing Science and Technology.2024; 36(3): 526. CrossRef
In this paper, a prosthetic robot hand was designed and fabricated and experimental evaluation of the realization of basic gripping motions was performed. As a first step, a robot finger was designed with same structural configuration of the human hand and the movement of the finger was evaluated via kinematic analysis. Electromyogram (EMG) signals for hand motions were measured using commercial wearable EMG sensors and classification of hand motions was achieved by applying the artificial neural network (ANN) algorithm. After training and testing for three kinds of gripping motions via ANN, it was observed that high classification accuracy can be obtained. A prototype of the proposed robot hand is manufactured through 3D printing and servomotors are included for position control of fingers. It was demonstrated that effective realization of gripping motions of the proposed prosthetic robot hand can be achieved by using EMG measurement and machine learning-based classification under a real-time environment.
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Development of a Caterpillar-Type Walker for the Elderly People Yeon-Kyun Lee, Chang-Min Yang, Sol Kim, Ji-Yong Jung, Jung-Ja Kim Applied Sciences.2021; 12(1): 383. CrossRef
Remote Control of Mobile Robot Using Electromyogram-based Hand Gesture Recognition Daun Lee, Jung Woo Sohn Transactions of the Korean Society for Noise and Vibration Engineering.2020; 30(5): 497. CrossRef
This paper proposes an air pocket glove for finger rehabilitation and diagnosis of hemiplegic patients after stroke. This device consists of pneumatic actuators that expand when air is injected from a pump motor, silicone flexors that act as artificial finger tendons, film-type bending sensors, and a pressure sensor. As air enters the glove, the actuators are expanded, thus stretching out paralyzed fingers. We designed two different rehabilitation modes: continuous passive motion (CPM) mode and master-slave mode, where the motions of the unaffected fingers are duplicated in the affected fingers. We conducted an experiment to test the validity of the device for each mode. In CPM mode, the patient’s spasticity level was estimated from finger angle and air pressure. Our results showed that spasticity level decreased 13% from the initial level after rehabilitation. With the master-slave mode, EMG signals were additionally measured and compared to those found during conventional therapy, which revealed a positive effect stemming from voluntary involvement in the exercise. As a result, EMG energy was shown to increase up to 18% during master-slave mode.
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Analysis of maintaining human maximal voluntary contraction control strategies through the power grip task in isometric contraction Jinyeol Yoo, Woong Choi, Jaehyo Kim Scientific Reports.2024;[Epub] CrossRef
A soft wearable exoglove for rehabilitation assistance: a novel application of knitted shape-memory alloy as a flexible actuator Soo-Min Lee, Juyeon Park Fashion and Textiles.2024;[Epub] CrossRef
Ergonomic glove pattern drafting method for hand assistive devices: considering 3D hand dimensions and finger mobility Soo-Min Lee, Juyeon Park Fashion and Textiles.2024;[Epub] CrossRef
A Wearable Soft Robot for Stroke Patients’ Finger Occupational Therapy and Quantitative Measures on the Joint Paralysis Jihun Kim, Geonhui Lee, Hanjin Jo, Wookhyun Park, Yu Shin Jin, Ho Dong Kim, Jaehyo Kim International Journal of Precision Engineering and Manufacturing.2020; 21(12): 2419. CrossRef
Deaf people use their own national sign or finger languages for communication. They have a lot of inconvenience in both social and financial problems. In this study, a finger language recognition system using an ensemble machine learning algorithm with an armband sensor of 8 channel surface electromyography (sEMG) is introduced. The algorithm consisted of signal acquisition, digital filtering, feature vector extraction, and an ensemble classifier based on artificial neural network (EANN). It was evaluated with Korean finger language (14 consonants, 17 vowels and 7 numbers) in 20 normal subjects. EANN was categorized with the number of classifiers (1 to 10) and the size of training data (50 to 1500). Mean accuracies and standard deviations for each structure were then obtained. Results showed that, as the number of classifiers (1 to 8) and the size of training data (50 to 300) were increased, the average accuracy of the E-ANN classifier was increased while the standard deviation was decreased. Statistical analysis showed that the optimal E-ANN structure was composed with 8 classifiers and 300 training data. This study suggested that E-ANN was more accurate than the general ANN for sign/finger language recognition.
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