Shingo Shimoda

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by gait impairments that affect the regulation of whole-body movement, which can be quantified through metrics such as vertical center of mass (vCoM). In healthy gait, vCoM oscillations contribute to metabolic efficiency by facilitating the exchange between kinetic and potential energy; however, this mechanism is often disrupted in PD. Although levodopa improves motor impairments, its impact on vCoM and neuromuscular control remains poorly understood. This study investigated levodopa effects on vCoM and neuromuscular coordination in fifteen patients with PD (Hoehn & Yahr 2–3) during PD OFF (before levodopa) and PD ON (after levodopa) and compared to nine healthy older adults (HOA). Kinematic and electromyography data were collected during an overground walk to quantify vCoM and muscle synergies. Here we show that levodopa significantly increased maximum vCoM displacement (p = 0.002), with PD OFF showing significantly reduced displacement compared to HOA (p < 0.001) while PD ON did not differ significantly, indicating a shift in vCoM displacement towards healthy patterns. Temporal patterns of muscle synergies showed that PD OFF exhibited prolonged activation duration and slower slope-to-peak in Synergy 2 (push-off) compared to HOA while spatial patterns of Synergy 2 showed an increase in gastrocnemius muscle contribution in PD ON compared to PD OFF (p = 0.002). Together these findings demonstrate that levodopa might partially restore whole-body gait mechanics through a unique reorganization of muscle synergy structure, pointing to a possible functional link between neuromuscular coordination and gait dynamics in PD.

Microfluidic bioprinter demonstrates significant advantage in controlling the generation of hydrogel microfibers as bioinks, however, precisely controlling the deposition position of the resulting microfibers remains challenging, since a lag is always generated in microfluidic jets, inducing spatial deviation between the laydown pattern of the microfibers and the predefined printing trajectory. This article presents a microscopic vision-based method for automatically compensating for such deviations. In particular, a magnet-embedded reservoir is constructed initially in order to ensure a stable printing process in a solution. Subsequently, an underwater fluorescent imaging method was developed for the purpose of quantitatively investigating the influence of the reservoir movement on the length of jet lag. L. Based on the jet lag feedback, a new microfluidic printing strategy is developed by integrating model predictive control into nonuniform rational B-splines curve-based small-line segment interpolation to control the L-compensated movement of the reservoir. Furthermore, curvature-related velocity planning is utilized in the controlled movement to optimize printing efficiency. The experimental results demonstrate that the printing accuracy is augmented by over 45% and 11% for regular and irregular curly curves, respectively.

The coordination of muscles in human locomotion is commonly understood as the integration of motor modules known as muscle synergies. Recent research has delved into the adaptation of muscle synergies during the acquisition of new motor skills. However, the precise interplay between modulated muscle synergies during movement according to motion requirements remains unclear. Here, we aim to elucidate the alterations in locomotor synergies across various lower-limb motion strategies and motor tasks. Our findings reveal consistent weights of muscles in muscle synergies alongside varying timing activation aligned with specific motion requirements. It shows that spatial muscle synergies remain stable across different motor tasks, but humans adjusted the timing activation of these modules (temporal muscle synergies) to meet the motor requirements. To classify temporal muscle synergies and quantify connection weights for both self-connections and connections between muscle synergies, we employed a graph neural network. Our results demonstrate that muscle synergy 4, responsible for elevating the thigh to propel forward during the swing phase, experiences pronounced enhancement with changes in motion strategies. Furthermore, we observed a reduction in the self-connection of muscle synergy 2, implicated in stabilizing body posture, during motion tasks other than normal walking. Additionally, the connections between muscle synergy 2 and other synergies diminished, indicating more adaptation in muscle synergy 2 to achieve stabilization in more challenging motor tasks. The validity of these findings was verified through five-fold cross-validation, affirming the efficacy of our approach in elucidating neuro-modulation mechanisms in human locomotion. Our proposed methodology holds promising implications for the development of personalized training strategies, offering insights into the intricate interactions among different muscle synergies in accomplishing motor tasks.

There is an increasing demand for robots capable of safely interacting with individuals in everyday scenarios. This necessitates their ability to comprehend human behavior, predict future actions, and respond accordingly. This is precisely the focus of the field known as cognitive robotics . Cognitive robots can learn from experience and others, retain and retrieve relevant knowledge and skills, and employ this knowledge to make informed decisions while considering the consequences. They can reason about their actions and modify their behavior to improve their effectiveness. In short, cognitive robots are capable of flexible, context-sensitive action, knowing what they are doing and why they are doing it [1] .

Previous studies have demonstrated the potential of surface electromyography (sEMG) spectral decomposition in evaluating muscle performance, motor learning, and early diagnosis of muscle conditions. However, decomposition techniques require large data sets and are computationally demanding, making their implementation in real-life scenarios challenging. Based on the hypothesis that spectral components will present low inter-subject variability, the present paper proposes the foundational principles for developing a real-time system for their extraction by utilizing a pre-defined library of components derived from an extensive data set to match new measurements. The model library was tailored to fulfill specific requirements for real-time system application and the challenges encountered during implementation are discussed in the paper. For system validation, four distinct data sets comprising isotonic and isometric muscle activations were utilized. The extracted during validation showed low inter-subject variability, suggesting that a wide range of physiological variations can be described with them. The adoption of the proposed system for muscle analysis could provide a deeper understanding of the underlying mechanisms governing different motor conditions and neuromuscular disorders, as it allows for the measurement of these components in various daily-life scenarios.

Embodying in vitro biological neural networks (BNNs) with robots to explore the rise of intelligence in these simpler models and to endow robots with biological intelligence has been attracting increasing attention in the fields of neuroscience and robotics. However, current research suffers from unstable sensory-motor mapping due to the random wiring of neurons seeded on multi-electrode arrays (MEAs). Therefore, here we propose a modular BNN (mBNN)-based neuro-robotic system via local chemical stimulation and calcium recording. In this system, reliable evoked sensory-motor mapping (success rate $> $ 89%) from the sensory to the motor area in the mBNN was demonstrated. It is achieved in the mBNNs by combining global chemical modulation (for suppressing spontaneous signal transmission) and local chemical stimulation (for inducing the evoked signal transmission). The neural signals of the motor area of the BNN are recorded by calcium imaging, analyzed, and decoded to control the motion state of the mobile robot in real-time. The sensory signals of the robot are encoded and transmitted to the sensory area of the BNN, closing the loop. This system presents a platform to investigate how information is processed and transmitted in mBNNs, and also to examine the influence of local and global chemical modulation on within-network signal transmission.

Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder–elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion–extension and pronation–supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson’s correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.

Objective: The challenges of an aging population worldwide are the increased number of people needing medical and nursing care and inadequate medical resources. Information and communication technologies have progressed remarkably, leading to innovations in various areas. 5G communication systems are capable of high-capacity, high-speed communication with low latency and are expected to transform medicine. We aimed to report a demonstration experiment of telerehabilitation and telemedicine using a mobile ultrasound system in a depopulated area in a mountainous terrain, where 32% of the population are 65 years or older. Methods: At the core hospital, a physician or physical therapist remotely performed ultrasonography or rehabilitation on a subject in a clinic. Five general residents participated in the telerehabilitation as subjects. The delay time and video quality transmitted with 5G and long-term evolution (LTE) communication systems were compared. The physician or physical therapist subjectively evaluated the quality and delay of the transmitted images and subject acceptability. Results: Of seven physical therapists, six and three responded that the video quality was “good” for telerehabilitation with 5G/4K resolution and LTE, respectively. Five physical therapists and one physical therapist reported that the delay time was “acceptable” with 5G/4K resolution and LTE, respectively. For telemedicine using a mobile ultrasound system, the responses for 5G were “the delay was acceptable” and “rather acceptable.” In contrast, both respondents’ responses for LTE were “not acceptable.” Conclusions: Multiple high-definition images can be transmitted with lower latency in telerehabilitation and telemedicine using mobile ultrasound imaging systems with a 5G communication system. These differences affected the subjective evaluation of the doctors and physical therapists.