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

NeuroMuscular Disorders (NMDs) may induce difficulties to perform daily life activities in autonomy. For people with NMDs affecting the upper-limb mobility, Dynamic Arm Supports (DASs) turn out to be relevant assistive devices. In particular, active DASs benefit from an external power source to support severely impaired people. However, commercially available active devices are controlled with push buttons, which add cognitive load and discomfort. To alleviate this issue, we propose a new force-based assistive control framework. In this preliminary work, we focus on the computation of a feedforward force to compensate upper-limb gravity. Four strategies based on a biomechanical model of the upper limb, tuned using anthropometric measurements, are proposed and evaluated. The first one is based on the potential energy of the upper-limb, the second one makes a compromise between the shoulder and elbow torques, the third one minimizes the sum of the squared user joint torques and the last one uses a probabilistic approach to minimize the expected torque norm in the presence of model uncertainties. These strategies have been evaluated quantitatively through an experiment including nine participants with an active DAS prototype. The activity of six muscles was measured and used to compute the Mean Effort Index (MEI) which represents the global effort required to maintain the pose. A statistical analysis shows that the four strategies significantly lower the MEI (p-value < 0.001).

This work proposes two myoelectric control maps based on a DoF-wise synergy algorithm, inspired by human motor control studies. One map, called intuitive, matches control outputs with body movement directions. The second one, named non-intuitive, takes advantage of different synergies contribution to each DoF, without specific correlation to body movement directions. The effectiveness and learning process for the two maps is evaluated through performance metrics in ten able-bodied individuals. The analysis was conducted using a 2-DoFs center-reach-out task and a survey. Results showed equivalent performance and perception for both mappings. However, learning is only visible in subjects that performed better in non-intuitive mapping, that required some familiarization to then exploit its features. Most of the myoelectric control designs use intuitive mappings. Nevertheless, non-intuitive mapping could provide more design flexibility, which can be especially interesting for patients with motor disabilities.

The grip force dynamics during grasping and lifting of diversely weighted objects are highly informative about an individual's level of sensorimotor control and potential neurological condition. Therefore, grip force profiles might be used for assessment and bio-feedback training during neurorehabilitation therapy. Modern neurorehabilitation methods, such as exoskeleton-assisted grasping and virtual-reality-based hand function training, strongly differ from classical grasp-and-lift experiments which might influence the sensorimotor control of grasping and thus the characteristics of grip force profiles. In this feasibility study with six healthy participants, we investigated the changes in grip force profiles during exoskeleton-assisted grasping and grasping of virtual objects. Our results show that a light-weight and highly compliant hand exoskeleton is able to assist users during grasping while not removing the core characteristics of their grip force dynamics. Furthermore, we show that when participants grasp objects with virtual weights, they adapt quickly to unknown virtual weights and choose efficient grip forces. Moreover, predictive overshoot forces are produced that match inertial forces which would originate from a physical object of the same weight. In summary, these results suggest that users of advanced neurorehabilitation methods employ and adapt their prior internal forward models for sensorimotor control of grasping. Incorporating such insights about the grip force dynamics of human grasping in the design of neurorehabilitation methods, such as hand exoskeletons, might improve their usability and rehabilitative function.

The complexity of the human upper limb makes replicating it in a prosthetic device a significant challenge. With advancements in mechatronic developments involving the addition of a large number of degrees of freedom, novel control strategies are required. To accommodate this need, this study aims at developing an IMU-based control for the HannesARM upper-limb prosthetic device, as a proof-of-concept for new control strategies integrating data-fusion approaches. The natural human control of the upper-limb is based on different inputs that allow adaptive control. To mimic this in prostheses, the implementation of IMUs provides kinematic information of both the stump and the prosthesis to enrich the EMG control. The principle of operation is to decode upper limb movements by using a custom-made system and to replicate them in prosthetic arms improving the control algorithms. To evaluate the system's effectiveness, the custom algorithm's motion extraction was compared to a motion capture system using fifteen able-bodied subjects. The results showed that this system scored 0.16 ± 0.04 and 0.81 ± 0.12 in Root Mean Squared Error and Cross-Correlation compared to the motion capture system. Experimental results demonstrate how this work can extract valuable kinematic information necessary for new and improved control strategies, such as intention detection or pattern recognition, to allow users to perform a broader range of tasks and enhancing in turn their quality of life.

Upper-limb rehabilitation exoskeletons offer a valuable solution to support and enhance the rehabilitation path of neural-injured patients. Such devices are usually equipped with a network of sensors that can be exploited to evaluate and monitor the performances of the users. In this work, we assess the normality ranges of different motor-performance indicators on a group of 15 healthy participants, computed with the benchmark toolbox of AGREE, an upper limb motorized exoskeleton. The toolbox implements a benchmarking scheme for the evaluation of the upper limb, used to test anterior reaching at rest position height and hand-to-mouth motor skills. We selected kinematic and electromyography performance indicators to assess the different motor abilities. We performed a pilot evaluation on three neurological patients, to verify if the AGREE benchmark toolbox was able to distinguish patients from healthy subjects on the basis of the selected performance indicators. Through a comparison between results obtained by the healthy and the small group of motor-impaired users, we successfully calculated the normality ranges for the selected performance indicators, and we pilot-showed how data gathered from AGREE can be used to evaluate the current status of the patients.

This research aims to develop safe, robust, and easy-to-use adaptive technology for individuals with tetraplegia. After a debilitating spinal cord injury, clinical care focuses on improving quality of life. Participation in adaptive sports has been shown to improve several aspects of participants' well-being. The TetraSki is a power-assisted ski chair that allows individuals with tetraplegia to participate in downhill skiing by sipping and puffing air on an integrated straw to turn their skis. Here, we introduce a new intuitive and dexterous control strategy for the TetraSki using surface electromyography (sEMG) from the neck and shoulder muscles. As an initial assessment, six healthy participants completed a virtual ski racecourse using sEMG and Sip-and-Puff control. Participants also completed a detection response task of cognitive load and the NASA-TLX survey of subjective workload. No significant differences were observed between the performance of sEMG control and the performance of Sip-and-Puff control. However, sEMG control required significantly less cognitive load and subjective workload than Sip-and-Puff control. These results indicate that sEMG can effectively control the equipment and is significantly more intuitive than traditional Sip-and-Puff control. This suggests that sEMG is a promising control method for further validation with individuals with tetraplegia. Ultimately, long-term use of sEMG control may promote neuroplasticity and drive rehabilitation.

The development of humanoids with bimanual manipulator arms may facilitate assistive robots to perform physical therapy with older adults living at home. As we assume the human-human interaction to be the gold standard of physical therapy, we propose a kinematics analysis to derive guidelines for implementing physical therapy assisted by humanoids. Therefore, a pilot study was carried out involving three physical therapists and two participants acting as exemplary patients. The study analyzes the therapists' movement strategy, including the position and orientation of the therapists' bodies in relation to the participants and the placement of the therapists' hands on the upper limb segment of the participants, as well as the inter- and intravariability during the performance of a ROM (range of motion) assessment. The results demonstrate that while physical therapists exhibit variation in their interaction strategies, they still achieve a consistently low level of variability in their manipulation space.

Acquired Brain Injury (ABI) causes permanent disabilities, such as foot drop. This condition affects the gait pattern, increasing the metabolic cost and risk of falling. Robotics with serious games has shown promising results in the gait rehabilitation context. This paper aims to analyze the effects of using the T-FLEX exoskeleton with (1) Automated Therapy (AT) and (2) Serious Game Therapy (SGT) in two ABI patients. Each participant completed six assisted sessions for each strategy. Results showed that AT increases the user-robot interaction torque by 10% for the first patient and 70% for the second patient, and SGT decreases by 5% for both patients. This way, SGT required the patient to generate torque to execute the ankle movement, while AT did the opposite, resulting in greater device assistance. In the functional assessment, SGT induced variations greater than 50% for the paretic ankle and knee's range of motion (ROM), indicating a potential for motor recovery. Thus, SGT led to improved ankle control and increased gait speed compared to AT. These findings suggest that SGT may be an effective rehabilitation strategy for ABI-related foot drop patients.

Home-based rehabilitation can serve as an adjunct to in-clinic rehabilitation, encouraging users to engage in more practice. However, conventional home-based rehabilitation programs suffer from low adherence and high drop-out rates. Wearable movement sensors coupled with computer games can be more engaging, but have highly variable adherence rates. Here we examined characteristics of user adherence by analyzing unsupervised, wearable grip sensor-based home-hand rehabilitation data from 1,587 users. We defined three different classes of users based on activity level: low users (<2 days), medium users (2 - 9 days), and power users (> 9 days). The probability of using the device more than two days was positively correlated with first day game success (p = 0.91, p<. 001), and number of sessions played on the first day (p = 0.87, p<. 001) but negatively correlated with parameter exploration (total number of game adjustments / total number of sessions played) on the first day (p = - 0.31, p= 0.05). Compared to low users, power users on the first day had more game success (65.18 ± 25.76 %vs. 54.94 ± 30.31 %,p <. 001), parameter exploration (25.47 ± 22.78 % vs. 12.05 ± 20.56 %, p <. 001), and game sessions played (7.60 ± 6.59 sessions vs. 4.04 ± 3.56 sessions, p <. 001). These observations support the premise that initial game success which is modulated by strategically adjusting parameters when necessary is a key determinant of adherence to rehabilitation technology.

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.