• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.194-199
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• 2016

EMG has specific information which is related to movements according to the activities of muscles. Therefore, users can intuitively control a prosthesis. For this reason, biosignals are very useful and convenient in this kind of application. Bioimpednace also provides specific information about movements like EMG. In this study, we used both EMG and bioimpedance to classify the typical hand gestures such as hand open, hand close, no motion (rest), supination, and pronation. Nine able-bodied subjects and one amputee were used as experimental data set. The accuracy was $98{\pm}1.9%$ when 2 bio-impedance and 8 EMG channels were used together for normal subjects. The number of EMG channels affected the accuracy, but it was stable when more than 5 channels were used. For the amputee, the accuracy is higher when we use both of them than when using only EMG. Therefore, accurate and stable hand motion estimation is possible by adding bioimepedance which shows structural information and EMG together.

• 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.

• The Journal of Korean Academy of Prosthodontics
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• v.45 no.6
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• pp.717-723
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• 2007

Statement of problem. Unreasonable distal cantilevered implant-supported prosthesis can mask functional problems of reconstruction temporarily, but it can cause serious strain and stress around its supported implant and surrounding alveolar bone. Purpose. The purpose of this study was to evaluate strain of implants supporting distal cantilevered fixed prosthesis with two different cantilevered length under distal cantilevered static load. Material and methods. A partially edentulous mandibular test model was fabricated with auto-polymerizing resin (POLYUROCK; Metalor technologies, Stuttgart, Swiss) and artificial denture teeth (Endura; Shofu inc., Kyoto, Japan). Two implants-supported 5-unit screw-retained cantilevered fixed prosthesis was made using standard methods with Type III gold alloy (Harmony C&B55; Ivoclar-vivadent, Liechtenstein, Germany) for superstructure and reinforced hard resin (Tescera; Ivoclar-vivadent, Liechtenstein, Germany) for occlusal material. Two strain gauges (KFG-1-120-C1-11L1M2R; KYOWA electronic instruments, Tokyo, Japan) were then attached to the mesial and the distal surface of each standard abutment with adhesive (M-bond 200; Tokuyama, Tokyo, Japan). Total four strain gauges were attached to test model and connected to dynamic signal conditioning strain amplifier (CTA1000; Curiotech inc., Paju, Korea). The stepped $20{\sim}100$ N in 25 N increments, cantilevered static load 8mm apart (Group I) or 16mm apart (Group II), were applied using digital push-pull gauge (Push-Pull Scale & Digital Force Gauge, Axis inc., Seoul, Korea). Each step was performed ten times and every strain signal was monitored and recorded. Results. In case of Group I, the strain values were surveyed by $80.7{\sim}353.8{\mu}m$ in Ch1, $7.5{\sim}47.9{\mu}m/m$ in Ch2, $45.7{\sim}278.6{\mu}m/m$ in Ch3 and $-212.2{\sim}718.7{\mu}m/m$ in Ch4 depending on increasing cantilevered static load. On the other hand, the strain values of Group II were surveyed by $149.9{\sim}612.8{\mu}m/m$ in Ch1, $26.0{\sim}168.5{\mu}m/m$ in Ch2, $114.3{\sim}632.3{\mu}m/m$ in Ch3, and $-323.2{\sim}-894.7{\mu}m/m$ in Ch4. Conclusion. A comparative statistical analysis using paired sample t-test about Group I Vs Group II under distal cantilevered load shows that there are statistical significant differences for all 4 channels (P<0.05).

• 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%).