Tomohiro Shibata

Evaluating wearable assistive robots in real-world caregiving is challenging because temporally aligned and repeatable A/B comparisons are rarely available. Conventional evaluations assume that assist-on and assist-off trials are comparable in task content, posture, and movement timing. However, caregivers adjust their posture and timing across human–human interactions and task sequences. This study proposes a causally inspired diagnostic framework based on DBN/SCM-inspired time-series modeling and movement-fixed counterfactual estimation. We represented the multimodal observations using intervention, robot state, movement context, EMG, and context variables. Node-specific relationships were approximated using Attention-based Sparse Variational Gaussian Process regressors. We evaluated the framework at three levels of environmental complexity. These levels comprised controlled trunk flexion, partially controlled bed-to-wheelchair transfer, and real-world caregiving. The proposed framework is intended as a diagnostic counterfactual evaluation tool rather than as a method for strict causal identification. Across experiments, one-step EMG prediction accuracy alone was insufficient to identify intervention-sensitive models. In controlled validation, the selected movement-decoupled robot-only model reproduced an EMG-reducing response consistent with the controlled A/B reference. When fitted to the partially controlled transfer data, the selected structural specification identified an EMG-increasing response in supported contexts. In the real-world caregiving case study, the global assist-mediated response (AMR) was near zero despite a positive pooled A/B difference. However, the stratified analysis identified localized supported responses. The near-zero AMR indicates that the pooled difference was not reproduced through the modeled assist intervention–robot state–EMG pathway under fixed movement context. This result should not be interpreted as evidence of overall device ineffectiveness. These findings suggest that context-fixed counterfactual diagnosis can help interpret assistive responses under increasing environmental complexity.

Objective In late-stage Parkinson’s disease (PD), wheelchair mobility becomes essential, yet little is known about the endurance and efficiency of wheelchairs. For individuals who struggle with manual wheelchair (MW) propulsion, a cycling wheelchair (CW) may provide an alternative. In this study, endurance and efficiency were compared between MWs and CWs during continuous driving, including turning tasks, in late-stage PD.Methods Nine participants with late-stage PD performed the 6-minute push test using both MWs and CWs. Total distance, average speed, and the Physiological Cost Index (PCI) were measured. The PCI was calculated from the pre- and post-driving heart rates.Results Compared with the use of MWs, the use of CWs resulted in a significantly greater total distance and a lower PCI, and similar patterns were observed among participants at Hoehn and Yahr stage V (n=6).Conclusion Compared with MW use, CW use may enable more enduring and efficient mobility in late-stage PD. Further studies are needed to validate these preliminary findings.

1. In Japan, a rapidly aging population has led to an increased demand for caregiving professionals [1]. However, this workforce often experiences significant physical strain, with improper techniq...

Knee-related conditions, such as anterior cruciate ligament injuries and meniscal ruptures, significantly affect mobility and quality of life. Although range-of-motion exercises are important for rehabilitation, interpreting electromyography signals related to these knee motions remains challenging owing to the complexity and variability of muscle activation patterns. This study investigated the classification of knee motion phases (resting, holding, and flexion/extension) by using surface electromyography signals from the biceps femoris and semitendinosus muscles in subjects with and without knee issues. Electromyography signals were preprocessed by removing outliers using the median absolute deviation and Kalman filtering. The motion phases were classified using a Support Vector Machine and Random Forest models within a subject-independent leave-one-subject-out evaluation framework. For subjects without knee issues, Random Forest performed with higher classification accuracy than Support Vector Machine for both muscles, with significant differences confirmed by McNemar’s test (p < 0.05). For subjects with knee issues, both classifiers performed with comparable mean accuracies of approximately 90% for both muscles, with no statistically significant difference between models (p > 0.05), despite the Random Forest exhibiting slightly more temporally coherent time-series segmentation. The results demonstrate that electromyography-based motion phase classification is feasible under strict subject-independent evaluation. Random Forest showed greater robustness in healthy subjects, whereas both classifiers performed equivalently under pathological conditions, enhancing their potential application in intelligent rehabilitation and clinical motion assessment systems.

Given their high sociality and close evolutionary distance to humans, monkeys are an essential animal model for unraveling the biological mechanisms underlying human social behavior and elucidating the pathogenesis of diseases exhibiting abnormal social behavior. However, behavioral analysis of naturally behaving monkeys requires manual counting of various behaviors, which has been a bottleneck due to problems in throughput and objectivity. Here, we developed a three-dimensional markerless motion capture system that used multi-view data for robust tracking of individual monkeys and accurate reconstruction of the three-dimensional poses of multiple monkeys living in groups. Validation analysis in two monkey groups revealed that the system enabled the characterization of individual social dispositions and relationships through automated detection of eight basic social events. Analyses of social looking demonstrated its potential for investigating adaptive behaviors in a social group. These results suggest that this motion capture system will greatly enhance our ability to analyze primate social behavior.