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

Assist-as-needed (AAN) is a paradigm in rehabilitation robotics based on the fact that more active participation from human users promotes faster recovery of motor functions. Moreover, the patients and public engaged and involved in our research design stressed that in order to provide safe and patient-friendly assistance, rehabilitation robotics should be equipped with different constraints while giving minimal assistance where required. Most of the current constraint-based AAN methods are only capable of providing position or velocity constraints which limit the quality of assistance that the robotic systems could provide. In this paper, we propose a multi-objective optimization (MOO) based controller which can implement both linear and non-linear constraints to improve the quality of assistance. This MOO-based proposed controller includes not only position and velocity constraints but also a vibration constraint to subside the tremors common in rehabilitation patients. The performance of this controller is compared with a Barrier Lyapunov Function (BLF) based controller with task-space constraints in a simulation. The results indicate that the MOO-based controller behaves similarly to the BLF-based controller in terms of position constraints. It also shows that the MOO-based controller can improve the quality of assistance by constraining the velocity and subsiding the simulated tremors.

Subject-specific musculoskeletal models generate more accurate joint torque estimates from electromyography (EMG) inputs in relation to experimentally obtained torques. Similarly, reflex Neuromuscular Models (NMMs) that employ COM states in addition to musculotendon information generate muscle activations to musculoskeletal models that better predict ankle torques during perturbed gait. In this study, the reflex NMM of locomotion of one subject is identified by employing an EMG-calibrated musculoskeletal model in unperturbed and perturbed gait. A COM acceleration-enhanced reflex NMM is identified. Subject-specific musculoskeletal models improve torque tracking of the ankle joint in unperturbed and perturbed conditions. COM acceleration-enhanced reflex NMM improves ankle torque tracking especially in early stance and during backward perturbation. Results found herein can guide the implementation of reflex controllers in active prosthetic and orthotic devices.

Body-Machine Interfaces (BoMIs) are promising assistive and rehabilitative tools for helping individuals with impaired motor abilities regain independence. When operating a BoMI, the user has to learn a novel sensorimotor transformation between the movement of certain body parts and the output of the device. In this study, we investigated how different feedback modalities impacted learning to operate a BoMI. Forty-seven able-bodied participants learned to control the velocity of a 1D cursor using the 3D rotation of their dominant wrist to reach as many targets as possible in a given amount of time. The map was designed to maximize cursor speed for movements around a predefined axis of wrist rotation. We compared the user's performance and control efficiency under three feedback modalities: i) visual feedback of the cursor position, ii) proprioceptive feedback of the cursor position delivered by a wrist manipulandum, iii) both i) and ii). We found that visual feedback led to a greater number of targets reached than proprioceptive feedback alone. Conversely, proprioceptive feedback yielded greater alignment between the axis of rotation of the wrist and the optimal axis represented by the map. These results suggest that proprioceptive feedback may be preferable over visual feedback when information about intrinsic task components, i.e. joint configurations, is of interest as in rehabilitative interventions aiming to promote more effective learning strategies.

Usability testing is important for the effective translation of neurorehabilitation technologies but is often overlooked and under-reported. The aim of this paper is to present a method of collecting and analyzing usability data, using a think-aloud and semi-structured interview protocol and qualitative analysis techniques. We present a worked case study of this method with a novel neurorehabilitation system that utilizes thought-controlled robotics to partially restore lower-limb function of people with spinal cord injury (SCI). Five male participants (mean age = 32.6 years) with SCI who identified as users of related neurorehabilitation technologies completed the usability study. Video-recorded usability sessions utilized a combination of concurrent and retrospective think-aloud methods as well as semi-structured interviews. Recordings were analyzed to identify verbal and behavioral feedback from participants regarding system performance and acceptability. In total, 538 data points were logged, which were aggregated into 60 usability issues, 44 positive evaluations, and 31 strategies for improvement. The approach undertaken was novel in that we sought to not only capture usability issues but also system elements that were positively evaluated by intended users and strategies for improvement from the perspective of intended users. These observations will be used to inform the further development of the neurorehabilitation system.

Lower limb assistive technology (e.g. exoskeletons) can benefit significantly from higher resolution information related to physiological state. High-density electromyography (HD-EMG) grids offer valuable spatial information on muscle activity; however their hardware is impractical, and bipolar electrodes remain the standard in practice. Exploiting information rich HD-EMG datasets to train machine learning models could help overcome the spatial limitations of bipolar electrodes. Unfortunately, differences in signal characteristics across acquisition systems prevent the direct transfer of models without a drop in performance. This study investigated Domain Adaptation (DA) to render EMG-based models invariant to different acquisition systems. This approach was evaluated using a Temporal Convolutional Network (TCN) that mapped EMG signals to the subject's knee angle, using HD-EMG as source data and Delsys bipolar EMG as target data. Furthermore, the feature extraction learnt by the TCN was also applied across muscle groups, evaluating the transferability of the sensor agnostic features. The DA implementation shows promise in both scenarios, with an average increase in accuracy (angular error normalised by the range of motion) of 7.36% for the Rectus Femoris, Biceps Femoris and Tibialis Anterior, as well as a cross-muscle performance increase of up to 10.80%. However, when the domain discrepancy is severe, the model is currently unable to generate a reliable walking trajectory due to inherent limitations related to the applied regression scheme and the chosen Mean Squared Error loss function. Therefore, future research should focus on exploring advanced loss functions and classification-based DA models that prioritise restoring key features of the gait.

Accurate and timely movement intention detection can facilitate exoskeleton control during transitions between different locomotion modes. Detecting movement intentions in real environments remains a challenge due to unavoidable environmental uncertainties. False movement intention detection may also induce risks of falling and general danger for exoskeleton users. To this end, in this study, we developed a method for detecting human movement intentions in real environments. The proposed method is capable of online self-correcting by implementing a decision fusion layer. Gaze data from an eye tracker and inertial measurement unit (IMU) signals were fused at the feature extraction level and used to predict movement intentions using 2 different methods. Images from the scene camera embedded on the eye tracker were used to identify terrains using a convolutional neural network. The decision fusion was made based on the predicted movement intentions and identified terrains. Four able-bodied participants wearing the eye tracker and 7 IMU sensors took part in the experiments to complete the tasks of level ground walking, ramp ascending, ramp descending, stairs ascending, and stair descending. The recorded experimental data were used to test the feasibility of the proposed method. An overall accuracy of 93.4% was achieved when both feature fusion and decision fusion were used. Fusing gaze data with IMU signals improved the prediction accuracy.

Recent developments in soft wearable robots have shown promise for assistive and rehabilitative use-cases. For inflatable approaches, a major challenge in developing portable systems is finding a balance between portability, performance, and usability. In this paper, we present a textile-based robotic sleeve that can provide functional elbow flexion assistance and is compatible with a portable actuation unit (PAU). Flexion is driven by a curved textile actuator with internal pneumatic supports (IPS). We show that the addition of IPS improves torque generation and increases battery-powered actuations by 60%. We demonstrate that the device can provide enough torque throughout the ROM of the elbow joint for daily life assistance. Specifically, the device generates 13.5 Nm of torque at 90°. Experimental testing in five healthy individuals and two individuals with Amyotrophic Lateral Sclerosis (ALS) demonstrates its impact on wearer muscle activity and kinematics. The results with healthy subjects show that the device was able to reduce the bicep muscle activity by an average of 49.1±13.3% during static and dynamic exercises, 43.6±11.1% during simulated ADLs, and provided an assisted ROM of 134°±13°. Both ALS participants reported a reduced rate of perceived exertion during both static and dynamic tasks while wearing the device and had an average ROM of 115°±8°. Future work will explore other applications of the IPS and extend the approach to assisting multiple joints.

Mirror Therapy (MT) is an effective therapeutic method used in the rehabilitation of hemiplegics. The effectiveness of this method is improved by employing a bi-modal approach which requires the synchronous movement of the affected and unaffected arm. For this purpose, we describe the design of a wearable device using a Mechanical Metamaterial (MM) that is optimized for the specific user to provide passive assistance of wrist flexion-extension and enable synchronous motion of the affected and unaffected arm during MT.

Cerebral palsy (CP), one of the diseases that cause motor deficiency, is a childhood condition characterized by a motor disability (palsy) caused by a non-progressive static lesion in the brain (cerebral) [1], [2]. Rehabilitation of patients with CP is typically accomplished through exercises performed by a team of several specialists so that the patient can act independently or with as little reliance on third parties as possible. In CP, motor disorders are often present together with sensory, perceptual, cognitive, behavioural and communication disorders. Regarding motor disorders, the most frequent outcome is hemiplegia, although diplegic and paraplegic patients are also present [1].

Compensatory movements are commonly observed post-stroke and can negatively affect long-term motor recovery. In this context, a system that monitors movement quality and provides feedback would be beneficial. In this study, we aimed to detect compensatory movements during seated reaching using a conventional tablet camera and an open-source markerless body pose tracking algorithm called MediaPipe [1]. We annotated compensatory movements of stroke patients per frame based on the comparison between the paretic and non-paretic arms. We trained a binary classification model using the XGBoost algorithm to detect compensatory movements, which showed an average accuracy of 0.92 (SD 0.07) in leave-one-trial-out cross-validation across four participants. Although we observed good model performance, we also encountered challenges such as missing landmarks and misalignment, when using MediaPipe Pose. This study highlights the feasibility of using near real-time compensatory movement detection with a simple camera system in stroke rehabilitation. More work is necessary to assess the generalizability of our approach across diverse groups of stroke survivors and fully implement near real-time compensatory movement detection on a mobile device.