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Jin Su Kim, Cheol Ho Song, Jae Myung Kim, Jimin Lee, Yeong-Hyeon Byeon, Jaehyo Jung, Hyun-Sik Choi, Keun-Chang Kwak, Youn Tae Kim, EunSang Bak and Sungbum Pan
Current advancements in biosignal-based user recognition technology are paving the way for a next-generation solution that addresses the limitations of face- and fingerprint-based user recognition methods. However, existing biosignal benchmark databases ...
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Suhun Jung, Jae Hwan Bong, Seung-Jong Kim and Shinsuk Park
In this study, we proposed a novel machine-learning-based functional electrical stimulation (FES) control algorithm to enhance gait rehabilitation in post-stroke hemiplegic patients. The electrical stimulation of the muscles on the paretic side was contr...
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Chao Chen, Weiyu Guo, Chenfei Ma, Yongkui Yang, Zheng Wang and Chuang Lin
Since continuous motion control can provide a more natural, fast and accurate man?machine interface than that of discrete motion control, it has been widely used in human?robot cooperation (HRC). Among various biological signals, the surface electromyogr...
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Chang-ok Cho, Jin-Hyoung Jeong, Yun-jeong Kim, Jee Hun Jang, Sang-Sik Lee and Ki-young Lee
At relatively low effort level tasks, surface electromyogram (sEMG) spectral parameters have demonstrated an inconsistent ability to monitor localized muscle fatigue and predict endurance capacity. The main purpose of this study was to assess the potenti...
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Sherif Said, Abdullah S. Karar, Taha Beyrouthy, Samer Alkork and Amine Nait-ali
Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for use...
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Saraswati Sridhar and Vidya Manian
Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning netwo...
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Bu Il Jeon, Byung Jun Kang, Hyun Chan Cho and Jongwon Kim
An electromyogram (EMG) is a signal for muscle output that indicates the degree of muscle contraction and relaxation. For these muscle signals to be output, certain signals must be received from the brain. To analyze these relations, electroencephalogram...
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Angkoon Phinyomark and Erik Scheme
The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more ...
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Heli KOSKIMAKI,Pekka SIIRTOLA
Pág. 31 - 42
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