This letter presents Switch-SLAM, switching-based LiDAR-inertial-visual SLAM for degenerate environments, designed to tackle the challenges in degenerate environments for LiDAR and visual SLAM. Switch-SLAM achieves high robustness and accuracy by utilizing a switching structure that transitions from LiDAR to visual odometry when degeneration of LiDAR odometry is detected. To efficiently detect degeneration, Switch-SLAM incorporates a non-heuristic degeneracy detection method that does not require heuristic tuning and demonstrates generalizability across various environments. Switch-SLAM is evaluated on diverse datasets containing both LiDAR and visual odometry degeneracy scenarios. The experimental results highlight the accurate and robust localization by the proposed method in multiple challenging environments with either LiDAR or visual SLAM degeneracy.

Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people (n=12) and post-stroke patients (n=33), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that post-stroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for post-stroke patients that focuses on activation timing of muscle synergies.

Exoskeleton robots need to always actively assist the user’s movements otherwise robot just becomes a heavy load for the user. However, estimating diversified movement intentions in a user’s daily life is not easy and no algorithm so far has achieved that level of estimation. In this study, we rather focus on estimating and assisting a limited number of selected movements by using an EMG-based movement classification and a newly developed lightweight exoskeleton robot. Our lightweight knee exoskeleton is composed of a carbon fiber frame and highly backdrivable joint driven by a pneumatic artificial muscle. Thus, our robot does not interfere with the user’s motions even when the actuator is not activated. As the classification method, we adopted a positive-unlabeled (PU) classifier. Since precisely labeling all the selected data from large-scale daily movements is not practical, we assumed that only part of the selected data was labeled and used a PU classifier that can handle the unlabeled data. To validate our approach, we conducted experiments with five healthy subjects to selectively assist sit-to-stand movements from four possible daily motions. We compared our approach with two classification methods that assume fully labeled data. The results showed that all subject’s movements were properly assisted.

This paper presents a comparative study of two AR training systems for Humanitude dementia care. Humanitude is a multimodal comprehensive care methodology for patients with dementia and has attracted considerable attention as a gentle and effective care technique. To provide an effective training system for Humanitude, we developed HEARTS 1, which realizes simultaneous sensing and interaction by combining a real training entity and augmented reality technology. However, training experiments using HEARTS 1 indicated that HEARTS 1 had certain weaknesses that should be addressed, namely, the usability of the AR display or the impression of the CG model. This paper presents a new prototype called HEARTS 2 consisting of Microsoft HoloLens 2 as well as realistic and animated CG models of older women. Psychological experiments were conducted to identify the difference in the usability of the AR displays, such as the weight and the field of view, and the impression of the CG models. We also conducted questionnaire surveys for beginners and medical professionals and preliminary training experiments to evaluate the usefulness of HEARTS 2. Based on the psychological experiments and questionnaire surveys, we concluded that HEARTS 2 was more comfortable and suitable for a training system for dementia care. GRAPHICAL ABSTRACT

Sit-to-stand (STS) motion is an important daily activity and many post-stroke patients have difficulty in performing the STS motion. Post-stroke patients who can perform STS independently, still utilize four muscle synergies (synchronized muscle activation) as seen in healthy people. In addition, temporal muscle synergy features can reflect motor impairment of post-stroke patients. However, it has been unclear whether post-stroke patients improve their STS movements in short-term rehabilitation and which muscle synergy features can estimate this improvement. Here, we demonstrate that temporal features of muscle synergies which contribute to body extension and balance maintenance can estimate the effect of short-term rehabilitation based on machine learning methods. By analyzing muscle synergies of post-stroke patients (n = 33) before and with the intervention of physical therapists, we found that about half of the patients who were severely impaired, improved activation timing of muscle synergy to raise the hip with the intervention. Additionally, we identified the temporal features that can estimate whether severely impaired post-stroke patients improve. We conclude that temporal features of muscle synergies can estimate the motor recovery in short-term rehabilitation of post-stroke patients. This finding may lead to new rehabilitation strategies for post-stroke patients that focus on improving activation timing of different muscle synergies.

Patients with stroke experience declined physical functions and aim to recover by rehabilitation. It is important to improve their functions in the subacute period. Hemiplegic patients who can stand exhibit differing characteristics such as the center of mass and muscle synergy compared to healthy subjects. However, the characteristics to be achieved by the sit-to-stand motion in the subacute period is unknown. This study investigated the sit-to-stand motions of hemiplegic patients (n = 21) in three months of stroke onset. The patients were partitioned into two groups (‘mild’ and ‘severe’), based on the functional independence score at the time of discharge, and the patient's sit-to-stand motion in the subacute period was analyzed by muscle synergy and kinematics. The severe patients performed a biased standing on the non-paralyzed side, and the activation timing of muscle synergy that contributes to whole-body extension was earlier than that of the mild patients. This study contributes to the development of guidelines for optimal intervention for each patient. GRAPHICAL ABSTRACT