The brain is able to engage in dual tasks such as motor imagery (MI) and action observation (AO) or motor execution (ME) with action observation. In this study, we have quantitatively compared event-related desynchronization (ERD) patterns during tasks of pure MI, MI with AO (O-MI), ME, and ME with AO (O-ME) of the leg to investigate the underlying neuronal mechanisms using EEG. Subjects were instructed to imagine or perform rhythmical actions while watching a video of leg movements during O-MI and O-ME tasks; In contrast, subjects imagined and performed the leg movements without observing any video during pure MI and ME tasks. We noticed that the amplitude of ERDs from MI, O-MI, ME and O-ME sequentially increases in central regions of the brain. These quantified ERD patterns in EEG were used to study the differences of brain oscillatory changes among the four tasks. We found that ERDs in motor area were more distinct in O-MI, compared with pure MI. These results suggest that O-MI produced stronger motor activations than MI. Plus, O-ME showed significantly greater activations than ME in the beta band. O-ME has produced stronger neurophysiological effects than MI, and stronger behavioral effects than ME. These empirical results do provide convincing evidence of the dual tasks such combined MI or ME with action observation on brain pattern changes. The video of the goal-directed leg movements is most likely able to improve the ability of performing or imagining movements. O-MI and O-ME may get better and closer therapeutic effects in leg rehabilitation and motor skill training. Furthermore, the extent analysis of ERD may provide the basis for evaluating the ability of O-MI and O-ME in leg rehabilitation and motor skill training.
We can catch all sorts of falling objects like eggs or balls. We must predict the dynamical properties of objects to interact with them, but doing so precisely is difficult. The CNS is suggested to modulate the mechanical impedance of the musculoskeletal dynamics to accomplish robust control and overcome variability in environmental dynamics. In this study, we tested the hypothesis that musculoskeletal stiffness increases as the degree of variability in the environment increase through the motor adaptation process. We conducted a ball-catching task experiment in a virtual reality system where the load force of the ball changed every trial and measured the muscle activity in the wrist to estimate its joint stiffness. We found that group level wrist stiffness after adaptation monotonically increased against load force variability. Meanwhile, some participants showed a non-monotonic relationship between wrist stiffness and load force variability. The results of the experiment and computational simulations suggest that the CNS may adapt to a stochastic environment by modulating musculoskeletal stiffness level under the trade-off between movement accuracy and energetic cost.
2020年9月23～２６日に東京都立大学で開かれたInternational Symposium on Community-centric Systems (CcS2020)において、本学大学院電子情報工学専攻ウェアラブルロボット研究室卒業し、東京工業大学大学院博士課程に進学した何梓遜君がExcellent paper賞を受賞しました。発表テーマは「A Design of Anthropomorphic Hand based on Human Finger Anatomy」です。著者は Zixun He, Rezenko Roman Yurievich, Satoru Shimizu, Masato Fukuda, Yousun Kang, Duk Shin。人間の筋肉骨格系に基づいた電動義手の開発に関する研究です。
—Prosthetic hands are artificial extensions that help people who have lost their hands or arms to regain normal activity. One of the main requirements is that it should be as close as possible to the natural hand. Human hand flexibility is largely due to our highly evolved hand structure. To achieve those structures, we have adopted a human-like design concept. We designed the prosthetic hand based on the salient features of the human anatomy, making it have the same structural features as human hands: artificial joints, ligaments and Extensor hood, that should be very similar to the real hands. We tested to control prosthetic hand based on detected coordinates by Leap Motion. Finally, we could succeed in artificial hands to achieve same flexibility as characteristics of the human hand.