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.

Patients with hand paralysis have motor and sensory dysfunction in their hands. They can neither flex/extend their fingers nor feel gripping force or finger posture. To allow patients with hand paralysis to perform gripping motions, it is necessary to assist finger motion and provide sensory information about the gripping force and posture at the residual sensory area. We previously developed a wearable finger-motion assist system that assists finger motion and measures the gripping force necessary to present sensory information. Therefore, the purpose of this study is to propose and develop a wearable system that provides multiple types of sensory information based on gripping force and finger posture estimated by finger-motion assist system, and to confirm the basic performance of the system through experiments. We developed a band-type sensory feedback system that tightens the forearm in response to gripping force using tendon-driven wires. A vibrator is installed in it, and the intensity of vibration changes in response to finger posture. As basic experiments, identification of gripping force and finger posture using band squeezing and vibration was tested. The results showed the overall identification rate for the combination of gripping force and finger posture that were both correct was 75.6%. The identification rates for gripping force and finger posture when stimulations were presented simultaneously were 93.3 % and 80.9 %, respectively. In conclusion, we confirmed the feasibility of this system that presents multiple types of sensory information.

Patients with knee arthropathy suffer from pain owing to damage at the knee joint cartilage and experience difficulty in walking as the condition worsens. As one of treatments of knee arthropathy, an implantation of cellular cartilage has been performed. After implantation of cellular cartilage, the knee joint should avoid overweight to prevent cell damage form body weight and apply appropriate mechanical stress to articular cartilage to promote cartilage regeneration. Therefore, a weight support system which can adjust the amount of weight in everyday use is required. This study aims to develop an in-clothes body weight support system to protect the knee joint during daily walking to support knee arthropathy treatment and recovery in regenerative medicine, and confirm basic performance of the system through experiments with able-bodied person. The system had frame structure with two-node link from the groin to the foot with the user's knee joint as the center of rotation. It consisted of load-bearing seat, height adjustment mechanism, thigh cuff, mechanical knee joint, and, ground reaction force sensor shoes, and was designed to be sufficiently thin to fit inside clothes. In the 10-Meter walking test, the system could adjust the amount of supporting weight and support two-thirds, one-half, and one-third of total body weight of an able-bodied participant during walking. In conclusion, we confirmed that the developed system had basic performance of weight support.

Decreasing birth rates and an aging population in society often cause labor shortages in the manufacturing industry, making the development of methods to improve productivity based on limited human resources imperative. One way to achieve this is by enhancing product quality via the reduction of product losses and the consequent need for reassembly due to human error. Human error during assembly can arise from specific actions via the arms and fingers. We assumed that these errors can be captured based on the information on human skeletal models. The purpose of this study is to propose and develop a system that acquires information about assembly procedures by using human skeletal models, including the fingers of workers, and notifies them of skipped procedures and errors in part types. Further, we confirm the basic capability of the proposed system via experiments based on simulated assembly work. The proposed system monitors workers' motion based on color and depth images captured by a single RGB-D camera. In addition, we developed a function to detect the process and to point out errors with audible and visual feedbacks when errors were made by workers in assembly processes. In the experiment using simulated assembly work, the proposed system exhibited an accuracy rate of 98.3% with respect to acquisition of assembly processes. In the case of an error in an assembly process, the system was able to point out the error correctly and provide feedback to the worker before he had finished picking up the wrong part. In conclusion, we confirmed that the developed system exhibited the basic capability to acquire work procedures and efficiently point out errors in real time.

Patients with hand paralysis often suffer from motor and sensory dysfunction. In many cases, they can neither use their fingers nor feel a gripping force. Thus, they find it difficult to perform certain tasks, such as lifting small objects. It is important for patients with hand paralysis to receive assisted flexion and extension motions for their fingers and gripping-force feedback by leveraging body parts or organs where sensation remains. The purposes of this study are to develop a wearable cybernic finger to assist the gripping force needed to handle objects and to provide gripping-force feedback to patients; the feasibility of the device is confirmed through basic experiments. For the basic experiments, the device was evaluated by a model hand and able-bodied persons. The system successfully assisted the model hand’s fingers to grip, lift, hold, put down, and release cylindrical objects representative of necessary daily objects. Furthermore, six states of gripping force were identified with an average identification rate of 95.6% using vibration feedback. To summarize, a wearable hand-assist unit with sensory feedback has proven to be feasible.

Myoelectrical signals have many applications in medical, sports, wearable robotics and computing fields. Wet electrodes are widely used to acquire these signals. In contrast, dry contact electrodes and noncontact capacitive coupling electrodes have been developed. However, their use has several limitations. In this research, we developed a hybrid electrode that is capable of both capacitive and resistive recordings by optimizing the sensor input impedance value using a new electrode noise model that contained noise sources. We extend this design so that noise originated during real usage, such as motion artifacts and noise from electric motors is also measured and removed from the sensor output. In experiments, noise analysis and experiments were performed by measuring myoelectrical signals from both upper and lower limbs in realistic situations, including weight lifting, robot arm control, and walking on a treadmill. As the results, we verified that our electrodes were capable of bioelectrical measurements at noise levels comparable to wet electrodes in realistic situations and with high correlation coefficients between both types of sensors.

The safety of wearable walking assistant robots (W2ARs) is expected to be guaranteed as they are spreading. From our experience of the risk assessments on Robot Suit HAL, we assume that the mechanical angle stoppers and appropriate assembling are inherent safety measures for the W2ARs. These measures prevent the hazardous situations: excess assistance and collision with floor or wall. In this paper, we develop the testing machines to prove their strength. They have the weights imitating a leg or a whole body and simulate cyclic impulsive load during walking by exploiting free fall. We hope this paper helps to develop safer W2ARs and to establish safety standards of the W2ARs.

For assisting human motion, assistive devices working as muscles would be useful. A robot suit HAL (hybrid assistive limb) has been developed as an assistive device for lower limbs. Human can appropriately produce muscle contraction torque and control joint viscoelasticity by muscle effort such as co-contraction. Thus, to implement functions equivalent to human muscles using HAL, it is necessary to control viscoelasticity of HAL as well as to produce torque in accordance with operator's intention. Therefore the purpose of this study is to propose a control method of HAL using biological and motion information. In this method, HAL produces torque corresponding to muscle contraction torque by referring to the myoelectricity that is biological information to control operator's muscles. In addition, the viscoelasticities of HAL are adjusted in proportion to operator's viscoelasticity that is estimated from motion information by using an on-line parameter identification method. To evaluate the effectiveness of the proposed method, the method was applied to a swinging motion of a lower leg. When this method was applied, HAL could work like operator's muscles in the swinging motion, and as a consequence, the muscle activities of the operator were reduced. As a result of this experiment, we confirmed the effectiveness of the proposed method.

We have developed the exoskeletal robotics suite HAL (Hybrid Assistive Leg) which is integrated with human and assists suitable power for lower limb of people with gait disorder. This study proposes the method of assist motion and assist torque to realize a power assist corresponding to the operator's intention. In the method of assist motion, we adopted Phase Sequence control which generates a series of assist motions by transiting some simple basic motions called Phase. We used the feedback controller to adjust the assist torque to maintain myoelectricity signals which were generated while performing the power assist walking. The experiment results showed the effective power assist according to operator's intention by using these control, methods.

The purpose of this research is to propose a quantitative analysis method to analyze the relationship between the feedforward controller and feedback controller in a motion learning process, and also to assess the effectiveness of this method by experiments. To analyze the mechanisms of the motion control systems, we propose: 1) a control gain identification method, 2) a squared-error separation method, 3) a gradient separation method, and 4) a pattern correlation method. The inverted pendulum is used to evaluate the effectiveness of these methods. Experiments show that the proposed methods are an effective way to separate the motor control system into feedforward control and feedback control. They also disclose the fact that there are two learning processes; one of which mainly performs feedback control and the other mainly feedforward control.