Parkinson’s disease causes various gait disturbances due to dopamine deficiency in the basal ganglia, significantly reducing patients’ ability to perform activities of daily living and diminishing their quality of life. Previous studies have demonstrated that the wearable cyborg Hybrid Assistive Limb (HAL) trunk unit, assisted lateral movement during walking by providing lateral sway, thereby improving gait disturbances. We have developed a hybrid control method for HAL that assists appropriately based on the wearer’s stride time stability and intentional stride time changes, using their biometric data, thus expanding on existing control method. However, HAL controlled by the hybrid control method has not yet been applied to patients, and it is necessary to verify the feasibility of voluntary control that assists in synchronization with the patient’s lateral movement based on motion intention estimated from biometric information. Additionally, the current power transmission link to HAL trunk lacks a rotation mechanism, preventing accommodation of natural trunk rotation during walking and thereby restricting this motion. To address this limitation, we developed a HAL that adapts to the wearer’s trunk rotation during walking. We confirmed through gait experiments on an able-bodied participant that this new mechanism did not inhibit trunk rotation during walking. Furthermore, we confirmed that the developed HAL with voluntary control could assist lateral movement in synchronization with a patient with Parkinson’s disease through the gait experiment.

Locomotive syndrome is a condition in which motor function during walking deteriorates due to motor unit disorders, increasing the risk of requiring nursing care. This is a serious problem that reduces the quality of life. In this study, we focused on cybernics treatment, a method in which a wearable cyborg facilitates voluntary movement in paralyzed body parts, thereby providing sensory feedback from the paralyzed periphery to induce neural plasticity. Facilitating movements that activate the muscles along the spiral line—a myofascial chain that spirals across the body—is expected to improve coordination between the lower limbs and the trunk in patients with impaired locomotion, thereby enhancing their walking function. This study aimed to develop a method for activating the muscle groups along the spiral line responsible for coordinated hip-trunk motion using a wearable cyborg hybrid assistive limbs (HAL) through cybernics treatment, and to confirm the feasibility of this method in assisting twisting movements through coordinated hip-trunk motion in a fundamental experiment. The system consists of a trunk twisting unit and a hip flexion unit. By mechanically linking these components and implementing a control system that synchronizes trunk twisting and hip flexion based on the wearer’s intended movement, the system enables coordinated hip-trunk motion assistance. We conducted a fundamental experiment on an able-bodied adult male. Our results confirmed the presence of assistive torque during trunk twisting and the synchronization between trunk rotation and lateral bending, thereby confirming the feasibility of assisting hip flexion and trunk twisting in accordance with the wearer’s movement intention.

Cybernics treatment using the Hybrid Assistive Limb (HAL) can improve gait abilities. Contrary to conventional evaluations that compare 2-min walk distances without wearing HAL between pre- and post-intervention, we assess gait data measured by HAL during gait assistance, which enables the observation of gait changes that accompany the intervention. To establish this novel evaluation approach, it is essential to examine the relationship between changes in gait ability without HAL and gait patterns during HAL-assisted walking. Focusing on one individual with a neuromuscular disease, this study analyzed and evaluated the relationship between changes in the 2-min walk distance and changes in gait patterns during HAL-assisted walking. Principal component analysis (PCA) was employed to characterize the changes in gait patterns during HAL wear, followed by the creation of an individual model that predicts the rate of change in the 2-min walk distance without HAL from those features using the eXtreme Gradient Boosting (XGBoost). The model was then interpreted using SHapley Additive exPlanations (SHAP) to analyze the contributions of each feature to the prediction. Analysis of nine HAL-assisted walking trials and corresponding 2-min walk distance measurements revealed that changes in the 2-min walk distance were associated with alterations in specific gait patterns during HAL-assisted walking: knee joint angles, knee joint torques generated by HAL, and trunk pitch angles representing anterior-posterior trunk tilting. These findings clarified important features related to changes in gait ability within longitudinal gait pattern changes during cybernics treatment and demonstrated the utility of our analysis method and HAL-measured data for evaluating individual gait pattern changes.

Dynamic environments present a significant challenge for mobile robot localization, as changes in object configuration and human movement can lead to incorrect position estimates. Conventional methods struggle with these variations, often resulting in mismatches and localization failures. To address this issue, we propose a Visual SLAM-based localization method that integrates semantic information using an RGB-D camera. Our approach utilizes instance segmentation to enhance localization robustness in 3D environments. During map creation, feature points extracted from images in a static environment are assigned label IDs corresponding to object regions, embedding semantic information into the 3D map. During localization, feature matching is constrained by label consistency, ensuring that only points with identical labels are matched. Additionally, feature points associated with high-mobility objects, such as chairs or people, are excluded to prevent errors caused by environmental changes. Furthermore, during pose optimization, the information matrix is weighted according to semantic labels, giving higher importance to static objects and reducing the influence of dynamic ones. We validate the proposed method through real-world experiments in cluttered indoor environments with varying furniture arrangements and human presence. The results demonstrate that our approach significantly reduces mismatches and improves localization accuracy compared to conventional ORB-SLAM2. Unlike existing methods that update maps online, our approach maintains a fixed pre-constructed map, reducing inconsistencies over time. This study enhances localization robustness in changing environments by incorporating semantic constraints into Visual SLAM.

Freezing of Gait (FOG) is one of the typical parkinsonian gait disturbances of progressive neurological disorders such as Parkinson's disease (PD) and progressive supranuclear palsy. Previous studies showed that the wearable cyborg HAL trunk-unit improved their gait disturbances through only an autonomous sway control that provided lateral swing with constant frequency. To promote the improvement by establishing interactive Bio-Feedback loop, it is necessary to realize synchronization between the wearer's intention and gait states for stabilizing gait, i.e. reducing variation of gait cycle. In this study, we developed a voluntary control method that responded to an intentional change in gait cycle and a method that switched 2 kinds of controls including the voluntary control method and the autonomous sway control method for stabilizing gait. The voluntary swing control method synchronized the HAL's force with wearer's gait in a stable gait state less prone to FOG. The autonomous sway control method provided lateral swing with constant frequency as feedback to the patients to achieve a gait state less prone to FOG. These controls were switched according to the gait stability calculated by wearer's gait cycle. Through the gait experiments with an abled-body participant, we confirmed that the HAL's lateral cyclic sway synchronized with the participant's gait and each control switched based on a gait stability. These results showed that the proposed methods had the feasibility of stabilizing gait.

Robotic rehabilitation for paralyzed hands utilizes exoskeletons and soft gloves equipped with active mechanisms to provide support for hand motion. From a safety and control perspective, it is imperative to measure finger joint angles during motion support provided by a soft robotic wearable system. However, embedding sensors into these devices can be inconvenient as it may lead to bulkiness or structural difficulties. This study aims to develop a machine learning model for estimating finger joint angles from images utilizing data created with computer graphics (CG), and to validate the feasibility of this method through basic experiments. The three-dimensional CG (3DCG) hand model includes bones corresponding to the major joints of the fingers, and a wearable system of the index finger imported from a computer-aided design software was attached to the 3DCG hand model. After rendering the integrated motion between the hand model and the wearable system for finger flexion and extension, images in conjunction with the finger joint angles were recorded as the training data. The finger joint angle estimator was based on a pre-trained vision transformer model and was learned using the created 3DCG training dataset. The basic experiments showed that the developed machine learning model enabled the estimation of finger joint angles with reproducibility for four types of hand postures with the wearable system. Furthermore, the differences between the joint angles measured in the real world and those estimated by the developed model were smaller than those for an existing hand pose estimation model. The developed machine learning model, which utilizes 3DCG, has the potential to estimate finger joint angles with the wearable system using image videos.

People with color vision deficiency (CVD) face several difficulties in performing daily tasks because they often fall outside of the culturally, linguistically, and educationally modulated majority opinion. This study aims to develop a device that can seamlessly input/output information based on the user's handling actions and to verify the validity of the support for daily decision-making of people with CVD. In this study, the use case is set as selecting clothes in a shop; online behavior observation is then conducted to design an assistive method and a watch-type device that shows useful information, such as the adjusted color and/or text for people with CVD on a display at the wrist is developed. An online user interview is conducted using a first-person perspective and bird's-eye perspective video with three CVD participants to verify the validity of the developed device for daily support. Consequently, the accuracy and effectiveness of the watch-type devices were determined. This study presents a prototyped proof-of-concept device in a remote environment, considering the coronavirus pandemic, and discusses the daily support for people with CVD.