• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.10
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• pp.843-849
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• 2016

It has been shown that the adaptive grasp feature can be implemented by underactuated robotic hands with a minimal number of actuators. Following this approach, a new design of prosthetic hand is presented. A method is proposed for evaluating grasp performance using cylinders, spheres, and square bars of various sizes. The effects of the major design parameters were investigated experimentally and an improved design is proposed.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.11
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• pp.36-42
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• 2000

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.1-7
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• 2024

This paper presents an under-actuated robotic hand inspired by the ligamentous structure of the human hand for a prosthetic application. The joint mechanisms are based on the concept of a tensegrity structure formed by elastic strings. These rigid bodies and elastic strings in the mechanism emulate the phalanx bones and primary ligaments found in human finger joints. As a result, the proposed hand inherently possesses compliant characteristics, ensuring robust adaptability during grasping and when interacting with physical environments. For the practical implementation of the tensegrity-based joint mechanism, we detail the installation of the strings and the routing of the driving tendon, which are related to extension and flexion, respectively. Additionally, we have designed the palm structure of the proposed hand to facilitate opposition and tripod grips between the fingers and thumb, taking into account the transverse arch of the human palm. In conclusion, we tested a prototype of the proposed hand to evaluate its motion and grasping capabilities.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.19-23
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• 2024

Patients with amputations of both hands need prosthetic hands that serve both cosmetic and functional purposes, and research on prosthetic hands using electromyography of remaining muscles is active, but there is still the problem of high cost. In this study, an artificial prosthetic hand was manufactured and its performance was evaluated using low-cost parts and software such as a surface electromyography sensor, machine learning software Edge Impulse, Arduino Nano 33 BLE, and 3D printing. Using signals acquired with surface electromyography sensors and subjected to digital signal processing through Edge Impulse, the flexing movement signals of each finger were transmitted to the fingers of the prosthetic hand model through training to determine the type of finger movement using machine learning. When the digital signal processing conditions were set to a notch filter of 60 Hz, a bandpass filter of 10-300 Hz, and a sampling frequency of 1,000 Hz, the accuracy of machine learning was the highest at 82.1%. The possibility of being confused between each finger flexion movement was highest for the ring finger, with a 44.7% chance of being confused with the movement of the index finger. More research is needed to successfully develop a low-cost prosthetic hand.

• Archives of Plastic Surgery
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• v.46 no.4
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• pp.303-310
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• 2019

Background Prosthetic hands with a myoelectric interface have recently received interest within the broader category of hand prostheses, but their high cost is a major barrier to use. Modern three-dimensional (3D) printing technology has enabled more widespread development and cost-effectiveness in the field of prostheses. The objective of the present study was to evaluate the clinical impact of a low-cost 3D-printed myoelectric-interface prosthetic hand on patients' daily life. Methods A prospective review of all upper-arm transradial amputation amputees who used 3D-printed myoelectric interface prostheses (Mark V) between January 2016 and August 2017 was conducted. The functional outcomes of prosthesis usage over a 3-month follow-up period were measured using a validated method (Orthotics Prosthetics User Survey-Upper Extremity Functional Status [OPUS-UEFS]). In addition, the correlation between the length of the amputated radius and changes in OPUS-UEFS scores was analyzed. Results Ten patients were included in the study. After use of the 3D-printed myoelectric single electromyography channel prosthesis for 3 months, the average OPUS-UEFS score significantly increased from 45.50 to 60.10. The Spearman correlation coefficient (r) of the correlation between radius length and OPUS-UEFS at the 3rd month of prosthetic use was 0.815. Conclusions This low-cost 3D-printed myoelectric-interface prosthetic hand with a single reliable myoelectrical signal shows the potential to positively impact amputees' quality of life through daily usage. The emergence of a low-cost 3D-printed myoelectric prosthesis could lead to new market trends, with such a device gaining popularity via reduced production costs and increased market demand.

• Physical Therapy Korea
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• v.2 no.1
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• pp.80-87
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• 1995

The purpose of this case study was to introduce a myoelectric hand prosthesis for upper extremity amputee and prosthetic training program. Limb loss can result from disease, injury, or congenital causes. Trauma has been increasingly important role as the cause of amputaion in young, vigorous, and otherwise healthy individuals. The higher the level of amputation the greater the functional loss of the part, and the more the amputee must depend on the prostheis for fuction and cosmesis. Myoelectrical control of prostheses is a recent development and has been steadily gaining in clinical use over the past 20 years. Such a prosthesis uses signals from muscle contraction within the stump to activate a battery driven moter that operates specific component fuctions of the prosthesis. This twenty years old male case was operated a right above-elbow amputation due to tracffic accident and admitted to Yonsei Rehabilitaion hospital for the preprosthetic and prosthetic training. The case was able to successfully complete his myoelectric hand prosthesis training in the February of 1995.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.415-420
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• 2017

Underactuated prosthetic hands are relatively light and economical. In this work, an economical grasping force control system is proposed for underactuated prosthetic hands with adaptive grasp capability. The prosthetic hand is driven by a main cable based on a set of electromyography sensors on the forearm of a user. Part of the main cable tension related to grasping force is fed back to the user by a skin-mounted vibrator. The proper relationship between the grasping force and the vibrator drive voltage was established and prototype tests were performed on a group of users. Relatively accurate grasping force control was achieved with minimal training of users.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.265-271
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• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).

• Journal of IKEEE
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• v.24 no.4
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• pp.1039-1049
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• 2020

Neural decoding is important to recognize the user's intention for controlling a neuro-prosthetic hand. This paper proposes a real-time decoding method for multi-channel peripheral neural activity. Peripheral nerve signals were measured from the median and radial nerves, and motion artifacts were removed based on locally fitted polynomials. Action potentials were then classified using a k-means algorithm. The firing rate of action potentials was extracted as a feature vector and its dimensionality was reduced by a self-organizing feature map. Finally, a multi-layer perceptron was used to classify hand motions. In monkey experiments, all processes were completed within a real-time constrain, and the hand motions were recognized with a high success rate.