Biological Systems Engineering Lab
Living organisms developed in nature through the evolution process are equipped with supremely skilled and sophisticated biological functions that cannot be realized with current engineering techniques. Analysis of these mechanisms may lead to not only elucidation of biological functions but also development of a wide variety of novel engineering systems.
From the viewpoint of a scientist approaching the secrets of living organisms and from that of an engineer developing machinery useful for human kind, the members of Biological Systems Engineering laboratory work on a wide variety of projects to analyze the characteristics of biological functions from theoretical and experimental approaches employing engineering techniques aiming to find new principles peculiar to biological systems, and develop novel medical/welfare apparatuses and industrial devices by applying the elucidated principles.
Through such research activities, the students can learn in-depth knowledge about biological systems based on electricity, electronics, systems and information engineering foundation allowing themselves to become creative engineers capable of seeking a new principle and expanding it into new fields.
As of 2017, total of 42 senior undergraduate students, graduate students and doctoral students are working in the lab. The students are involved in either of the four research groups according to their research projects, which are ME group (Medical engineering group), Kansei brain group (Former A-life Group), EMG Group (Biological signal analysis group), and Human modeling group (Former Biological Motion Analysis Group). Since the group each consists of students from all grades, the student can acquire the necessary knowledge and skills required fora researcher and a member of societyas well throughthe lab. activities that the senior teaches the junior and the juniors help the seniors. Since the number of students is quite large compared to the usual laboratory, the lab. is managed as the followings.
Regarding education, we divide the students into four research groups corresponding to each research project, andconduct education and research activities on a group-by-group basis. We promote collaboration between graduate students and undergraduate students by organize each research group with students from all grades as much as possible. We alsoencourage the students to manage the group and teach each other by their own by assigning group leaders and sub leaders.In this way, we intend not only to increase autonomy of research activities but also to cultivate leadership mindset and skills enablingsupervision of their future community.
Specific education and research guidance is conducted throughout the all-member seminar (held once a week) participated by all members including facilities and students, the group seminars (held once a week) in each group, graduation theses and master thesis presentations (held twice a year),and the workshops for respective research projects. Especially, the workshops held on a regular basis, whichis a meeting that invites the collaborators from various department, universities, public research institutions and companies, can provide valuable chances for the students tocommunicate with researchers outside the laboratory.
Students' rotating representatives participate to the staff meeting biweekly held for entire laboratory management so that they can have opportunities to acquire knowledge and experience about organization management.In addition, we always try to revitalize our laboratorythrough conducting research evaluation questionnaire between students, issue of e-mail magazine (once a week), management of lab. homepage, encouragement of presentation at academic conferences both in Japan and abroad, active participation in various exhibitions, and holding laboratory tours for visitors.
Five research themes
There are still a lot of unknown functions and mechanisms hidden in the biological system. If we can elucidate and utilize them from engineering standpoint,then there is a possibility of creating new technologies to carve out the future of the 21st century. The Biological Systems Engineering Laboratory categorizes the broad research field of biological systems into five major research themes in order to explore specific research projects under each theme, and further functionally coordinate and fuse each theme to create novel research fields.
Research Theme 1Biological signal analysis and its application to human interfaces
We develop novel signal processing algorithms that enablethe interpretation of human motions, intentions, and physiological/psychological states contained in biological signals, such as myoelectric signals, electroencephalograms, and electrocardiograms, as well as create robotic interfaces and medical welfare equipment.
Specific Research Projects
- Biological signal discrimination by the neural networks
- Skill assistance by work model
- Development of myoelectric arms/human assist robots
- Development of cybernetic interface Bio-remote/Bio-pointer/Bio-vocoder/Amusement interface/Bio-music interface/EEG interface/Cybernetic robot interface: CHRIS/Cybernetic glove box/Cybernetic rehabilitation aid
Research Theme 2Biomechanical analysis and its application to human-machine system design
We model human sensory/motor functions from electrical and electronicperspectives based on experimentally measured data, and develop novel movement support systems and next-generation automobile control systems by incorporating modeled human characteristics.
Specific Research Projects
- Measurement/modeling of human hand/joint impedance
- Analysis of human impedance perception characteristics
- Analysis of human hand trajectory generation mechanism
- Manipulability analysis
- Impedance matching of human and machine
- Development of virtual sports rehabilitation aid
- Human-automotive system analysis
Research Theme 3Statistical structure of neural networks based on novel machine learning algorithms
We propose new machine learning algorithms and neural networks based on probabilistic statistical theory and applythese to the development oflearning and control technologiesfor robots, medical welfare equipment, and medical data classificationtechnology.
Specific Research Projects
- Log-linearized Gaussian Mixture Network(LLGMN)
- Recurrent LLGMN
- Hierarchical LLGMN
- Reduced-dimensional LLGMN
- Deep probabilistic neural networks
- Terminal learning algorithm
- Unsupervised learning algorithm
- Biomimetic control through machine learning
- Neural chips
Research Theme 4Brain function/neural network modeling and artificial life models
Focusing on functions such as locomotion generation, sensation, perception, learning, and judgment, we model brain functions from an engineering viewpoint using artificial neural networks. Ultimately, we aim to model and analyze higher brain functions, especially social brain functions that understand the minds of others and live harmoniously, and Kansei that involves nonverbal, unconscious, and intuitive sensibilities. We also develop artificial life form models based on biological knowledge using the constructed brain models.
Specific Research Projects
- Mouseolfactory model and odor classification system
- Modeling the human olfactory system and estimation of brain function andKansei
- Bioelectrical signal measurement and emotion estimation of small fishes
- Dynamical model analysis of small fishes and muscle activity/neural activity estimation
- Biomimetic control ofCaenorhabditis elegans robot
- Virtual bacteria
- Virtual Paramecium
- Virtual Caenorhabditis elegans
Research Theme 5Biometric information mining technology and medical support systems
We are engaged in the research and development of novel medical support systems and medical devices through medicine-engineering collaborations by utilizing electric and electronic systems and information engineering technologies, such as biomechanical analysis technology, biological signal analysis technology, machine learning technology, and biological simulation technology that were developed in the Biological Systems Engineering laboratory.
Specific Research Projects
- Measurement/modeling of arterial impedance characteristics
- Vascular viscoelastic index estimation using ultrasonic image
- Non-invasive biological signal measurement and continuous blood pressure measurement
- Autonomic nerve activity evaluation and surgical operation support system
- Finger-tapping motion analysis and higher brain function evaluation
- Diagnosis support system for Parkinson's disease
- Newborn infant motion evaluation
- Quantitative pain evaluation and fMRI measurement
Medical engineering group
Adopting the latest engineering technology is requisite for the medical research of the 21st century. Medical engineering is a field in which medical and engineering are integrated, and medical engineering (ME) group has been engaged in biomechanical analysis technology, biological signal analysis technology, machine learning technology, biological simulation technology to develop novel medical support systems and medical devices by exploiting electric and electronic information system technology.
- Vascular inpedance characteristics measurement and modeling
- Vascular viscoelatic index estimation using ultrasonographic image
- Non-ivasive biological signal measurement and blood pressure measurement
- Autonomic nerve activity evaluation and surgical operation support system
- Finger tap motion analysis and higher brain function evaluation
- Development of diagnostic support system for Parkinson's disease
- Development of a newborn infant motor evaluation method
- Development of pain quantitative evaluation method and fMRI measurement
Kansei brain group
Focusing on functions such as locomotion, sensation, perception, learning and judgment of the brain, Kansei Brain group tries to model and simulate its function by artificial neural network models from the viewpoint of engineering. This group ultimately aims to model and clarify nonverbal, unconscious, and intuitive ""Kansei", as well as the social brain functions that enable us to understand and cooperate with others. Employing the constracted brain models, we also develop artificial life models based on biological insights.
- Olfactory model of mice and odor identification system
- Modeling human olfactory system and estimating cerebral function and Kansei
- Bioelectrical signal measurement and emotion estimation of a small fish
- Dynamical model analysis for estimating the muscle activities and neural activity of a small fish
- Biomimetic control of Caenorhabditis elegans robot
- Development of virtual bacteria
- Development of virtual paramecium
- Development of virtual Caenorhabditis elegans
Human modeling group
In the human modeling group, through technologies that extend human exercise and sensation, we aim to realize an excellent human mechanical system. In order to enjoy the daily life even at the age, we think that it is important to maintain the feeling that I am moving my body freely by myself, and feel a sense of feeling various things by myself.
- Human motion impedance characteristics
- Human impedance perception
- Operability analysis and impedance adjustment
- Human hand trajectory generation mechanism
- Impedance training
- Virtual sports training
- Sports motion analysis
- Analysis of automobile operability
- Development of a driving seat design support system
- Powered suit without electricity: Unplugged powered suit
- Sensorimotor-enhancing suit (SEnS)
- Prediction of tactile sensibility from touch surface changes
Biological signal analysis group
When trying to produce muscle force, electrical impulses are transmitted from the brain through nerves, and the muscles discharge electricity. The measured electrical signal is called an electromyogram (EMG). There are various bioelectric signals that can be measured from the human body, such as electroencephalograms and electrocardiograms. The biological signal analysis group is engaged in the development of proprietary signal processing algorithms to identify motion intentions and the physiological/psychological states of human beings contained in their measured bioelectric signals. In addition, we have proposed novel robot interfaces and medical welfare devices using biosignals as input.
- Development of new probability neural network: Log-linearized Gaussian Mixture Network (LLGMN), Recurrent LLGMN, Hierarchical LLGMN, Reduced-dimensional LLGMN, Deep probability neural network
- Development of terminal learning algorithm
- Biosignal classification using neural networks
- Skill assistance using a work model
- Development of cybernetic interface: Bio-Remote, Bio-Pointer, Bio-Vocoder, Bio-Music, electroencephalogram interface, cybernetic robot interface (CHRIS), cybernetics glove box, cybernetic rehabilitation aid
- Development of multi-functional myoelectric prosthesis
- Learning-type biomimetic control
Our research results have been published in scientific journals, books, conference proceedings, patent, etc.. The numbers of publications the lab produced are shown as follows
(as of October 25, 2021):
Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
Kazuma Sakamoto, Zu Soh, Michiyo Suzuki, Yuichi Iino, and Toshio Tsuji
Scientific Reports, volume 11, Article number: 13737, doi.org/10.1038/s41598-021-92690-2, Published online: 02 July 2021. (SCI, IF=4.379)
The right hemisphere is important for driving-related cognitive function after stroke
Koji Shimonaga, Seiji Hama, Toshio Tsuji, Kazumasa Yoshimura, Shinya Nishino, Akiko Yanagawa, Zu Soh, Toshinori Matsushige, Tatsuya Mizoue, Keiichi Onoda, Hidehisa Yamashita, Shigeto Yamawaki, and Kaoru Kurisu
Neurosurgical Review, vol. 44, pp.977-985, doi.org/10.1007/s10143-020-01272-9, Published online: 11 March, 2020, 2021 (SCI, IF=2.654)
Prediction of blood pressure change during surgical incision under opioid analgesia using sympathetic response evoking threshold
Satoshi Kamiya, Ryuji Nakamura, Noboru Saeki, Takashi Kondo, Hirotsugu Miyoshi, Soushi Narasaki, Atsushi Morio, Masashi Kawamoto, Harutoyo Hirano, Toshio Tsuji, and Yasuo M Tsutsumi
Scientific Reports, volume 11, Article number: 9558, doi.org/10.1038/s41598-021-87636-7, Published online: 5 May 2021. (SCI, IF=3.998)
Cardiorespiratory Synchronisation and Systolic Blood Pressure Correlation of Peripheral Arterial Stiffness During Endoscopic Thoracic Sympathectomy
Toshifumi Muneyasu, Harutoyo Hirano, Akira Furui, Zu Soh, Ryuji Nakamura, Noboru Saeki, Yoshiyuki Okada, Masashi Kawamoto, Masao Yoshizumi, Atsuo Yoshino, Takafumi Sasaoka, Shigeto Yamawaki, and Toshio Tsuji
Scientific Reports, volume 11, Article number: 5966, doi.org/10.1038/s41598-021-85299-y, Published online: 16 March 2021. (SCI, IF=3.998)
Peripheral Arterial Stiffness During Electrocutaneous Stimulation is Positively Correlated with Pain-related Brain Activity and Subjective Pain Intensity: An fMRI Study
Toshio Tsuji, Fumiya Arikuni, Takafumi Sasaoka, Shin Suyama, Takashi Akiyoshi, Zu Soh, Harutoyo Hirano, Ryuji Nakamura, Noboru Saeki, Masashi Kawamoto, Masao Yoshizumi, Atsuo Yoshino, and Shigeto Yamawaki
Scientific Reports, volume 11, Article number: 4425, doi.org/10.1038/s41598-021-83833-6, Published online: 24 February 2021. (SCI, IF=3.998)
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures
Akira Furui, Ryota Onishi, Akihito Takeuchi, Tomoyuki Akiyama, and Toshio Tsuji
IEEE Transactions on Biomedical Engineering, Volume: 68, Issue: 2, pp. 515-525, Digital Object Identifier: 10.1109/TBME.2020.3006246, Publication Date: FEBRUARY 2021 (SCI, IF = 4.424)
Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies
Chiara Tacchino, Martina Impagliazzo, Erika Maggi, Marta Bertamino, Isa Blanchi, Francesca Campone, Paola Durand, Marco Fato, Psiche Giannoni, Riccardo Iandolo, Massimiliano Izzo, Pietro Morasso, Paolo Moretti, Luca Ramenghi, Keisuke Shima, Koji Shimatani, Toshio Tsuji, Sara Uccella, Nicolo Zanardi, and Maura Casadio
Computer Methods and Programs in Biomedicine, Volume 199, 105838, pp.1-17, Available online 21 November 2020, February 2021 (SCI, IF=3.632)
Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
Satoshi Miyamoto, Zu Soh, Shigeyuki Okahara, Akira Furui, Taiichi Takasaki, Keijiro Katayama, Shinya Takahashi, and Toshio Tsuji
Scientific Reports, volume 11, Article number: 549, doi.org/10.1038/s41598-020-80810-3, Published online: 12 January 2021. (SCI, IF=3.998)
Human Hand Impedance Characteristics during Maintained Posture in Multi-Joint Arm Movements
T. Tsuji, P. Morasso, K. Goto, and K. Ito
Biological Cybernetics, Vol.72, pp.475-485, 1995.
A Log-Linearized Gaussian Mixture Network and Its Application to EEG Pattern Classification
T. Tsuji, O. Fukuda, H. Ichinobe, and M. Kaneko
IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 29, No. 1, pp. 60-72, February 1999.
A Recurrent Log-linearized Gaussian Mixture Network
T. Tsuji, N. Bu, M. Kaneko, and O. Fukuda
IEEE Transactions on Neural Networks, Vol.14, No.2, pp.304-316, March 2003.
A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions
O. Fukuda, T. Tsuji, M. Kaneko and A. Otsuka
IEEE Transactions on Robotics and Automation, Vol.19, No.2, pp.210-222, April 2003.
Quantitative Evaluation of Pain during Electrocutaneous Stimulation using a Log-Linearized Peripheral Arterial Viscoelastic Model
H. Matsubara, H. Hirano, H. Hirano, Z. Soh, R. Nakamura, N. Saeki, M. Kawamoto, M. Yoshizumi, A. Yoshino, T. Sasaoka, S. Yamawaki, and T. Tsuji
Scientific Reports, volume 8, Article number: 3091, doi:10.1038/s41598-018-21223-11, Published online: 15 February 2018.
Continuous Blood Viscosity Monitoring System for Cardiopulmonary Bypass Applications
S. Okahara, Z. Soh, S. Miyamoto, H. Takahashi, S. Takahashi, T. Sueda, and T. Tsuji
IEEE Transactions on Biomedical Engineering, Vol.64, No.7, pp. 1503-1512, DOI:10.1109/TBME.2016.2610968, JULY 2017.
Assessment of Lower-limb Vascular Endothelial Function Based on Enclosed Zone Flow-mediated Dilation
H. Hirano, R. Takama, R. Matsumoto, H. Tanaka, H. Hirano, Z. Soh, T. Ukawa, T. Takayanagi, H. Morimoto, R. Nakamura, N. Saeki, H. Hashimoto, S. Matsui, S. Kishimoto, N. Oda, M. Kajikawa, T. Maruhashi, M. Kawamoto, M. Yoshizumi, Y. Higashi, and T. Tsuji
Scientific Reports, volume 8, Article number: 9263, doi:10.1038/s41598-018-27392-3, Published online: 18 June 2018.
A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans
Z. Soh, K. Sakamoto , M. Suzuki , Y. Iino, and T. Tsuji
Scientific Reports, volume 8, Article number: 17190, doi:10.1038/s41598-018-35157-1, Published online: 21 November 2018.
A Scale Mixture-based Stochastic Model of Surface EMG Signals with Variable Variances
A. Furui, H. Hayashi, and T. Tsuji
IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2019.2895683, Date of Publication: 28 January 2019.
A myoelectric prosthetic hand with muscle synergy-based motion determination and impedance model-based biomimetic control
A. Furui, S. Eto, K. Nakagaki, K. Shimada, G. Nakamura, A. Masuda, T. Chin, and T. Tsuji
Science Robotics, Vol. 4, Issue 31, eaaw6339, DOI: 10.1126/scirobotics.eaaw6339, 26 June 2019.
Markerless Measurement and Evaluation of General Movements in Infants
T. Tsuji, S. Nakashima, H. Hayashi, Z. Soh, A. Furui, T. Shibanoki, K. Shima, and K. Shimatani
Scientific Reports, volume 10, Article number: 1422, doi:10.1038/s41598-020-57580-z, Published online: 29 January 2020.