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

Competitive settings may be promising for neurorehabilitation as they may lead to stronger patient motivation. However, the mechanisms underlying the development of an interaction in competitive settings are not well understood. We use a dyadic haptic interface to investigate the dynamics of competitive interaction. Pairs of participants engaged in a ball game (a penalty kick challenge), with an attacker and a defender. To explore how the participants adapt to each other over game iterations, we manipulated the amount of information available to each one about the location of their opponent. We found that under the different experimental conditions, the participants adjusted their behavior in response to their opponents' strategy change. These observations are consistent with simulations with a computational model assuming that during interaction the participants use optimal (Bayesian) perception and action selection (based on game theory). Taken together, experiments and simulations suggest that the participants develop and update a flexible, adaptive predictor of their opponents next action, which helps them adjust their behavior to optimize performance. These findings provide a foundation for further research into the mechanisms that drive adaptive behavior in competitive scenarios, and provide new insights into how competitive environments can be used in rehabilitation.

During daily life activities we not only gather information regarding the environment, but also regarding other humans which are performing similar actions. Even if not specifically required by the task, people affect other motion plans through different sensory modalities and may align their plans in a subtle way even if not required. This study explores the role of visual and auditory feedback in tasks with a sensory connection between partners. We used a dual robotic interface to test various visual and auditory coupling modalities, translating the spatial dynamics of the partners into distinctive feedback. Results from visual experiment showed that the explicit representation of the partner position along with own position greatly improves coordination between them. The auditory experiment emphasized the effectiveness of binaurally presented spatio-temporally discrete auditory cues. The current study provides insights relevant to the design of novel enriched rehabilitative protocols which rely on the mechanisms underlying interaction.

Metabolic rate, the amount of energy expended over time, can be influenced by passive assistive devices like specialized footwear and exoskeletons. While most assistive device research has focused on elite athletes or recreational runners at a single speed, few studies have explored how these devices might affect metabolic rate across a range of running speeds. This study investigates the effect of a passive assistive device (exotendon), which connects a runner's legs, on metabolic rate in recreational runners across multiple speeds. In contrast to previous work, only 9 of 16 participants reduced metabolic rate with the device at $2.67 ~mathrm{m} / mathrm{s}$, by an average of 7.6 %. While all participants significantly increased stride frequency - a key factor identified in previous work for its influence on metabolic rate - the group that did not reduce metabolic rate exhibited greater variability in average stride frequency for both conditions. For those who reduced metabolic rate with the exotendon at $2.67 ~mathrm{m} / mathrm{s}$, similar improvements extended across speeds from 1.68 to $4.36 ~mathrm{m} / mathrm{s}$. These results indicate that the exotendon's effect on metabolic rate varies across individuals but can extend beyond a single running speed in those who respond to the device. This suggests that passive assistive devices may benefit some runners across a range of speeds, with potential applications for training and performance as well as rehabilitation, where reducing energy demand could aid recovery and mobility.

To increase the quality of life of stroke patients, better diagnostics with the ability to identify the cause of motor impairment are needed. Robotic diagnostics increases the resolution of measurements, allows for tracking progress over a longer period, and can be used to evaluate new treatments. The Shoulder Elbow Perturbator (SEP) was developed to improve the diagnostics of post-stroke motor impairment. The SEP has already been tested on patients, showing promising results in identifying the cause of motor impairment, but no SEP system performance analysis has been published. To identify the joint properties of the elbow accurately, the SEP should have a bandwidth of at least 12 Hz. Furthermore, admittance and velocity control are required for various possible experimental tasks. This paper shows that the SEP performs adequately for the desired perturbations and experimental conditions for system identification of the human elbow. The SEP's performance is analysed with multisine signals to determine the bandwidth and endpoint dynamics. The velocity controller bandwidth is 50 Hz, and the admittance controller bandwidth is 65 Hz. Furthermore, the controller is stable. Thus, the SEP meets all the requirements and should be able to provide the desired perturbations and experimental conditions needed for system identification of the human elbow.

In rehabilitation, evidence-based practice relies on evaluating patients' standardized outcomes to manage care plans and resources; however, the selection of tools to use is globally inconsistent. Alternatively, emerging technologies can measure high-resolution biomarkers, but their implementation remains a challenge. Given the amount of measures, interest in new scoring systems of patient health synthesizing multisource data is rising. Emerging research in rehabilitation lacks standardized guidelines and psychometric validation of these scores. Hence, this review systematically examines the methods used for developing and validating patient assessment scores in rehabilitation. A systematic search included studies on the development and validation of scores to assess adult patients with central neurological disorders in rehabilitation. Nineteen studies were synthesized. In the score development, simpler methods and input data were more common (11 studies used statistics, 12 clinical data). In the validation, the heterogeneity in reference standards and tests in scores' psychometric analysis highlights the need for evidence and standardized processes in this field. Clinically relevant information is crucial for implementable scores able to guide clinical decision-making and care strategies. Future studies should adopt exploratory approaches, analyzing various input data, methods, and psychometric properties' impact on the score capability to assess patient health.

Spinal cord injury (SCI) impairs motor function and quality of life. Transcutaneous Spinal Cord Stimulation (tSCS) is a non-invasive method to restore motor function by activating spinal circuits below the lesion. However, its effectiveness is limited by individual variability, reliance on manual electrode placement, and offline muscle analysis. To address these challenges, we developed an online method for detecting spinal reflexes and muscle responses using two automated algorithms: the Ranking-Based Approach (binary method) and the Automated Selectivity-Driven Approach (leveraging a selectivity index), both designed to optimize electrode placement and stimulation amplitude for precise activation of proximal or distal muscles. In a study with six healthy participants in the supine position, the posterior root muscle test was performed automatically using tSCS across three rostrocaudal spinal electrodes. Our findings reinforce the evidence of the rostrocaudal tSCS selectivity, with rostral electrodes activating proximal muscles and caudal ones targeting distal muscles. Both algorithms identified optimal electrode positions and stimulation amplitudes, enhancing tSCS selectivity for lower-limb muscles. Our results suggest that the Automated SelectivityDriven Approach is more appropriate for increasing selectivity for targeted muscle recruitment. These results highlight the potential of automated methods to improve tSCS selectivity for SCI rehabilitation and other conditions.

Recent advancements in technology have made possible the use of soft wearable mechatronic devices for musculoskeletal rehabilitation. Soft sensors are crucial components of these devices, as they are used for user data collection and device control. However, current fabrication methods are expensive, complex, and not available to those in low resource communities. To address this issue, this paper presents advances in low-cost textile sensors created using either crocheting or weaving. The performance of four different types of sensors was evaluated in terms of working range, linearity, hysteresis, sensitivity, and repeatability. The results showed that a fully crocheted sensor made with elastic and silver-plated conductive thread performed the best. This sensor demonstrated high repeatability, an excellent working range (49.76%), and relatively good hysteresis ($20.12 pm 19$) and linearity ($R^{2}$ of $0.7741 pm 0.03$). Overall, these results indicate that soft textile strain sensors created using simpler techniques, such as crocheting, have the potential to be used in applications requiring tracking of human motion, meaning that they could be used as integral components of soft wearable robots. This low-cost approach could make soft wearable rehabilitation technologies more accessible and affordable, paving the way for broader implementation and improved patient outcomes.

The mandates of safety standards in manual material handling tasks have spurred the development and commercialization of many back-assist exoskeletons. These devices prevent back pain injuries by redistributing the applied loads, reducing the effort and fatigue in heavy and repetitive loading tasks. The majority of them employ passive and active actuation paradigms. The passive ones are known for better transparency and energy efficiency, while the active ones provide a higher degree of assistance and quickly adapt to task severities. The present work investigates the feasibility of a hybrid actuation paradigm for load-carriage under symmetric loading conditions. The preliminary results suggest that the proposed modifications to an existing passive exoskeleton effectively economize energy expenditure and improve adaptability.

This study explores the potential of using wrist-worn inertial sensors to automate the labeling of ARAT (Action Research Arm Test) items. While the ARAT is commonly used to assess upper limb motor function, its limitations include subjectivity and time consumption of clinical staff. By using IMU (Inertial Measurement Unit) sensors and MiniROCKET as a time series classification technique, this investigation aims to classify ARAT items based on sensor recordings. We test common preprocessing strategies to efficiently leverage included information in the data. Afterward, we use the best preprocessing to improve the classification. The dataset includes recordings of 45 participants performing various ARAT items. Results show that MiniROCKET offers a fast and reliable approach for classifying ARAT domains, although challenges remain in distinguishing between individual resembling items. Future work may involve improving classification through more advanced machine-learning models and data enhancements.

This study introduces a novel approach to home rehabilitation exercise instructions, utilizing a 3D-printed robotic hand that can mimic the patient's hand movements and follow pre-programmed instructions. This robotic hand, made from inexpensive 3D printed components and hobbyist equipment, is controlled by open source computer vision tracking. Two within-subject crossover studies were conducted with 30 healthy participants. The first study assessed the participants' sense of embodiment, while the second compared their ability to remember movement exercises when instructed by the robotic hand versus video clips. The results showed that the participants experienced a higher sense of embodiment with the robotic hand in its active mode. Furthermore, they demonstrated significantly better short-term memory retention of medium and difficult finger movement sequences when instructed by the robotic hand compared to video instruction ($F_{1,56}=4.047, mathrm{p}<.05$) and ($F_{1,56}=19.463, mathrm{p}$ $<.05$). These findings suggest that this innovative method could improve remote therapy and strengthen bodily ownership, which is essential in stroke rehabilitation.