IEEE ... International Conference on Rehabilitation Robotics : [proceedings]最新文献

Measuring static postural sway outside of the clinic could provide clinicians with long-term, continuous data on patient balance, offering a comprehensive view beyond infrequent in-clinic assessments. This paper presents a novel method to quantify balance ability through a regression algorithm that predicts postural sway velocity using only motion and force sensors. Data is acquired through sensors onboard an instrumented cane. The prediction algorithm's validity was demonstrated in a study of eight young unimpaired subjects and eight adults over 65. The subjects' balance was challenged with different stance widths and sight conditions while using an instrumented cane. In the younger subject cohort, balance was further challenged through an unstable platform. Together, these conditions allowed for variation of the tasks' difficulty levels and thus the range of measured sway velocity. Across subjects, sway velocity was demonstrated to be highly predictable (Younger Subjects $R^{2}=0.73$, Older Subjects $R^{2}= 0.47$) using just the sensors onboard the instrumented cane. In particular, hand motion was shown to be important in predicting sway velocity. We also demonstrated the use of data features to estimate Romberg quotients of the older participants, suggesting the method's potential to track proprioceptive function over time (Correlation $mathbf{r}=0.82$). This method offers a promising approach to continuous patient monitoring and could provide a long-term, quantitative assessment of balance ability.

The growing prevalence of chronic health conditions in aging populations highlights the need for innovative solutions in rehabilitation and long-term care. We propose a multimodal system designed to automatically classify Activities of Daily Living (ADLs) and, in the future, support the prevention of secondary health conditions in institutionalized elderly individuals. This system continuously integrates six commercially available wearable and nearable sensors to monitor ADLs over two weeks, ensuring data completeness and maintaining high data quality throughout the trial. In this study, we present pilot data from two residents of a Japanese elderly care facility, demonstrating the proposed system's feasibility and usability. The collected data was comprehensive and robust, with residents showing strong acceptance of the wearable and nearable technologies for long-term use. These findings underscore the potential of multimodal sensory systems to enhance rehabilitation strategies by enabling continuous health monitoring. Integrating ADLs monitoring into rehabilitation programs may facilitate early detection of health changes and support personalized interventions, building on the success of digital health tracking in other clinical domains.

Enhancing the walking economy is a primary goal in the application of exoskeletons for gait assistance. However, traditional exoskeletons often face challenges due to rigid designs and the additional distal mass they introduce, limiting their effectiveness. In this study, inspired by the muscle-tendon complex of the human calf, we present a cable-driven ankle exoskeleton designed to provide targeted assistance during plantarflexion movements at the ankle joint. The proposed exoskeleton integrates a compact, lightweight actuation unit with a flexible fabric shank sleeve, ensuring efficient torque transmission from the motor to the ankle joint. A feedback-based cascaded repetitive control system, combined with a multi-sensor fusion communication framework, was developed to achieve precise force control. The system's actuation performance was evaluated through benchtop experiments, demonstrating a bandwidth of approximately 13.5 Hz and a force tracking error of 5 % under position disturbances. Treadmill experiments further validated the effectiveness of the exoskeleton, showing a 7.53 % improvement in walking economy compared to no-assistance conditions. These findings highlight the potential of the proposed design to advance the development of cable-driven exoskeletons for improved gait assistance.

Stroke is a common healthcare problem, leading to disability and significantly impacting individuals' quality of life. A customised device was suggested to be used for roboticassisted therapy to meet different stroke survivors' needs. However, most existing upper limb rehabilitation robots only offer a generic handgrip, which fails to meet the various needs of stroke survivors with different spastic patterns and levels of upper limb weakness. This study investigates the technical and clinical feasibility of customised handgrips to meet stroke survivors' needs through an online questionnaire with 25 therapists and parametric handgrip designs. One wrist support and two different handgrips were designed, and their size (e.g., length and width) can be adapted based on the individual anthropology data. By using the additive manufacturing method, the manufacturing cost of bespoke handgrip is reduced by up to 15 %. Additionally, more than 90 % of professionals $(mathrm{n}=23)$ stated the positive impact of customised handgrip on rehabilitation outcomes and $72 %(mathrm{n}=18)$ would like to use it with stroke survivors due to its high accessibility and variability. The results indicate that a customised handgrip has the potential to benefit the stroke rehabilitation from both technical and clinical perspectives.

Movement coordination disorders can originate from congenital abnormalities, traumatic injuries, or severe infections. These conditions can manifest in various ways, such as hypertonia, hypotonia, and involuntary movements, significantly impairing an individual's ability to perform daily tasks. This research focuses on the development of a soft exoskeleton designed to enhance the quality of life for individuals with cerebral palsy, particularly those affected by athetosis - a condition marked by involuntary, fluctuating muscle tone and associated balance challenges. The exoskeleton aims to provide conductive education (also known as the Petó method) for maintaining motor functions and facilitating precise movements, thereby contributing to the social and professional integration of individuals living with mobility disorders. This manuscript emphasizes the simulation phase of the exoskeleton development, which supports the physical design process through numerous iterative cycles. During the research, we utilized three different movement data sources, we developed a whole and a partial simulated model (reduced degree-offreedoms) of our real exoskeleton, and investigated two different control methods in simulation. At the end of the day we achieved a qualitatively similar behavior in simulation what we produced with the physical exoskeleton at its current development stage.

Falls during daily ambulation activities are a leading cause of injury in older adults due to delayed physiological responses to disturbances of balance. Lower-limb exoskeletons have the potential to mitigate fall incidents by detecting and reacting to perturbations before the user. Although commonly used, the standard metric for perturbation detection, whole-body angular momentum, is poorly suited for exoskeleton applications due to computational delays and additional tunings. To address this, we developed a novel ground perturbation detector using lower-limb kinematic states during locomotion. To identify perturbations, we tracked deviations in the kinematic states from their nominal steady-state trajectories. Using a data-driven approach, we optimized our detector with an open-source ground perturbation biomechanics dataset. A nine-subject cross-validation demonstrated that our model distinguished perturbed from unperturbed gait cycles with 95.5% accuracy and only a delay of 33.1% within the gait cycle, outperforming the benchmark by 49.4% in detection accuracy. The results of our study offer exciting promise for our detector and its potential utility to enhance the controllability of robotic assistive exoskeletons.

Neurological disorders, such as stroke, can affect the ability to walk and balance. Robotic rehabilitation assists in training walking and balance capabilities of patients with neurological disorders. However, not all participants are good responders when using exoskeletons. This study aims to cluster gait patterns in stroke patients to provide insights into the pathology of stroke patients. Joint angles of the affected lower limb of 45 sub-acute stroke patients from an open access database were clustered based on a principal component analyses, followed by a k-means cluster analysis. A total of eight gait pattern clusters were retrieved and clinically rearranged into four categories. The results can be used in the field of robotic devices as well as a more clinical setting. Future research should focus on validating and using the retrieved clusters as clinical indicators for selecting suitable treatments, such as robotic devices or exoskeletons, to personalize rehabilitation.

Ankle injuries and musculoskeletal disorders often necessitate long-term rehabilitation to restore mobility and balance. The BALANSens robotic platform, designed within the Integrated Balance Rehabilitation (I-BAR) framework, combines ankle, balance, and stepping therapies into a cohesive rehabilitation process. This paper focuses on implementing impedance control on the BALANSens platform, which regulates the dynamic interaction between human limbs and the robotic system, making it suitable for active rehabilitation exercises. Traditional impedance control methods rely on costly six-axis force sensors to measure applied torques. However, this work proposes an affordable alternative using multiple load cells placed under the foot sole. These load cells not only measure ankle joint torques but also capture foot pressure distribution data, a key factor in the later stages of rehabilitation. The paper presents the derivation of joint space impedance control for the platform, with force measurements using load cells, and evaluates the system in a real hardware environment. The results underscore the promise of this approach for active rehabilitation, offering a cost-effective solution that simultaneously enriches the scope of patient assessment data.

Action Observation (AO) therapy leverages the mirror neuron system (MNS) and may support motor recovery in neurorehabilitation. In this study, we integrated Flo v2, a humanoid robot equipped with grippers and object detection system, into an AO therapy paradigm with electroencephalography (EEG) monitoring. Flo v2's enhanced design enables the execution of upper-limb actions, either transitive (involving object interaction such as grasping a cup) or intransitive (gesture-based without object manipulation such as waving). The robot control is synchronized with EEG recording to facilitate the investigation of cortical responses during AO tasks. We also conducted a case study to assess of the upgraded robot system's feasibility. Three healthy participants observed and imitated robot-performed actions, where the robot actor was in person or on videos. Exploratory analyses of EEG signals examined sensorimotor mu event-related desynchronization (ERD) during video-based and in-person AO tasks. Results indicated stronger responses during bimanual and transitive AO in the in-person settings. However, individual variability in cortical responses was evident, with one subject showing less pronounced ERD patterns, and that comparisons of mu ERDs across different types of action in video-based AO settings were inconsistent among subjects. Flo v2's enhancements demonstrated its feasibility as a tool for robot-mediated AOE therapy and highlighted potential for further neurorehabilitation research.

Current upper limb prostheses aim to enhance user independence in daily activities by incorporating basic motor functions. However, they fall short of replicating the natural movement and interaction capabilities of the human arm. In contrast, human limbs leverage intrinsic compliance and actively modulate joint stiffness, enabling adaptive responses to varying tasks, impact absorption, and efficient energy transfer during dynamic actions. Inspired by this adaptability, we developed a transhumeral prosthesis with Variable Stiffness Actuators (VSAs) to replicate the controllable compliance found in biological joints. The proposed prosthesis features a modular design, allowing customization for different residual limb shapes and accommodating a range of independent control signals derived from users' biological cues. Integrated elastic elements passively support more natural movements, facilitate safe interactions with the environment, and adapt to diverse task requirements. This paper presents a comprehensive overview of the platform and its functionalities, highlighting its potential applications in the field of prosthetics.