Zobrazeno 1 - 10
of 616
pro vyhledávání: '"Tsuji, Toshio"'
Autor:
Hashimoto, Yuki, Furui, Akira, Shimatani, Koji, Casadio, Maura, Moretti, Paolo, Morasso, Pietro, Tsuji, Toshio
The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. However, its evaluation in clinical practice relies on visual inspection by experts, and an automated solution is eagerly aw
Externí odkaz:
http://arxiv.org/abs/2207.03344
In this paper, we propose a time-series stochastic model based on a scale mixture distribution with Markov transitions to detect epileptic seizures in electroencephalography (EEG). In the proposed model, an EEG signal at each time point is assumed to
Externí odkaz:
http://arxiv.org/abs/2111.06526
Autor:
Kubo, Kouki, Hama, Seiji, Furui, Akira, Mizuguchi, Tomohiko, Soh, Zu, Yanagawa, Akiko, Kandori, Akihiko, Sakai, Hiroto, Morisako, Yutaro, Orino, Yuki, Hamai, Maho, Fujita, Kasumi, Yamawaki, Shigeto, Tsuji, Toshio
Publikováno v:
In Heliyon 15 July 2024 10(13)
EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models
Publikováno v:
Expert Systems with Applications, vol. 185, 115644, Dec 2021
Electromyogram (EMG) has been utilized to interface signals for prosthetic hands and information devices owing to its ability to reflect human motion intentions. Although various EMG classification methods have been introduced into EMG-based control
Externí odkaz:
http://arxiv.org/abs/2107.09853
Publikováno v:
Proceedings of 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 10484-10490
Myoelectric prosthetic hands are intended to replace the function of the amputee's lost arm. Therefore, developing robotic prosthetics that can mimic not only the appearance and functionality of humans but also characteristics unique to human movemen
Externí odkaz:
http://arxiv.org/abs/2105.14215
Publikováno v:
IEEE Transactions on Biomedical Engineering, vol. 68, no. 2, pp. 515-525, 2021
Objective: The detection of epileptic seizures from scalp electroencephalogram (EEG) signals can facilitate early diagnosis and treatment. Previous studies suggested that the Gaussianity of EEG distributions changes depending on the presence or absen
Externí odkaz:
http://arxiv.org/abs/2007.00898
Publikováno v:
IEEE Transactions on Biomedical Engineering, vol. 66, no. 10, pp. 2780-2788, Oct 2019
Objective: Surface electromyogram (EMG) signals have typically been assumed to follow a Gaussian distribution. However, the presence of non-Gaussian signals associated with muscle activity has been reported in recent studies, and there is no general
Externí odkaz:
http://arxiv.org/abs/1912.04580
Autor:
Miyazaki, Hironori, Nishio, Yoshifumi, Miyahara, Kohta, Furutani, Chiaki, Xu, Ziqiang, Saeki, Noboru, Tsuji, Toshio, Okada, Yoshiyuki
Publikováno v:
In Heliyon December 2023 9(12)
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No.12, pp. 3021-3033, 2015
This paper proposes a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional ti
Externí odkaz:
http://arxiv.org/abs/1911.06009
Electromyogram (EMG) classification is a key technique in EMG-based control systems. The existing EMG classification methods do not consider the characteristics of EMG features that the distribution has skewness and kurtosis, causing drawbacks such a
Externí odkaz:
http://arxiv.org/abs/1912.04218