Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors.

OBJECTIVE: Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge. METHODS: Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data. RESULTS: For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models. CONCLUSION: These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke. IMPACT: Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

AI in Rehabilitation Medicine: Opportunities and Challenges.

Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.

Decoding Upper-Limb Movement Intention Through Adaptive Dynamic Movement Primitives: A Proof-of-Concept Study with a Shoulder-Elbow Exoskeleton.

This work presents an intention decoding algorithm that can be used to control a 4 degrees-of-freedom shoulder-elbow exoskeleton in reaching tasks. The algorithm was designed to assist the movement of users with upper-limb impairments who can initiate the movement by themselves. It relies on the observation of the initial part of the user's movement through joint angle measures and aims to estimate in real-time the phase of the movement and predict the goal position of the hand in the reaching task. The algorithm is based on adaptive Dynamic Movement Primitives and Gaussian Mixture Models. The performance of the algorithm was verified in robot-assisted planar reaching movements performed by one healthy subject wearing the exoskeleton. Tests included movements of different amplitudes and orientations. Results showed that the algorithm could predict the hand's final position with an error lower than 5 cm after 0.25 s from the movement onset, and that the final position reached during the tests was on average less than 4 cm far from the target position. Finally, the effects of the assistance were observed in a reduction of the activation of the Biceps Brachii and of the time to execute the reaching tasks.

A Locomotion Mode Recognition Algorithm Using Adaptive Dynamic Movement Primitives.

Control systems of robotic prostheses should be designed to decode the users' intent to start, stop, or change locomotion; and to select the suitable control strategy, accordingly. This paper describes a locomotion mode recognition algorithm based on adaptive Dynamic Movement Primitive models used as locomotion templates. The models take foot-ground contact information and thigh roll angle, measured by an inertial measurement unit, for generating continuous model variables to extract features for a set of Support Vector Machines. The proposed algorithm was tested offline on data acquired from 10 intact subjects and 1 subject with transtibial amputation, in ground-level walking and stair ascending/descending activities. Following subject-specific training, results on intact subjects showed that the algorithm can classify initiatory and steady-state steps with up to 100.00% median accuracy medially at 28.45% and 27.40% of the swing phase, respectively. While the transitory steps were classified with up to 87.30% median accuracy medially at 90.54% of the swing phase. Results with data of the transtibial amputee showed that the algorithm classified initiatory, steady-state, and transitory steps with up to 92.59%, 100%, and 93.10% median accuracies medially at 19.48%, 51.47%, and 93.33% of the swing phase, respectively. The results support the feasibility of this approach in robotic prosthesis control.

Validity and reliability of a commercial wearable sensor system for measuring spatiotemporal gait parameters in a post-stroke population: the effects of walking speed and asymmetry.

Objective.Commercial wearable sensor systems are a promising alternative to costly laboratory equipment for clinical gait evaluation, but their accuracy for individuals with gait impairments is not well established. Therefore, we investigated the validity and reliability of the APDM Opal wearable sensor system to measure spatiotemporal gait parameters for healthy controls and individuals with chronic stroke.Approach.Participants completed the 10 m walk test over an instrumented mat three times in different speed conditions. We compared performance of Opal sensors to the mat across different walking speeds and levels of step length asymmetry in the two populations.Main results. Gait speed and stride length measures achieved excellent reliability, though they were systematically underestimated by 0.11 m s-1and 0.12 m, respectively. The stride and step time measures also achieved excellent reliability, with no significant errors (median absolute percentage error <6.00%,p> 0.05). Gait phase duration measures achieved moderate-to-excellent reliability, with relative errors ranging from 4.13%-21.59%. Across gait parameters, the relative error decreased by 0.57%-9.66% when walking faster than 1.30 m s-1; similar reductions occurred for step length symmetry indices lower than 0.10.Significance. This study supports the general use of Opal wearable sensors to obtain quantitative measures of post-stroke gait impairment. These measures should be interpreted cautiously for individuals with moderate-severe asymmetry or walking speeds slower than 0.80 m s-1.

A low-power ankle-foot prosthesis for push-off enhancement.

Passive ankle-foot prostheses are light-weighted and reliable, but they cannot generate net positive power, which is essential in restoring the natural gait pattern of amputees. Recent robotic prostheses addressed the problem by actively controlling the storage and release of energy generated during the stance phase through the mechanical deformation of elastic elements housed in the device. This study proposes an innovative low-power active prosthetic module that fits on off-the-shelf passive ankle-foot energy-storage-and-release (ESAR) prostheses. The module is placed parallel to the ESAR foot, actively augmenting the energy stored in the foot and controlling the energy return for an enhanced push-off. The parallel elastic actuation takes advantage of the amputee's natural loading action on the foot's elastic structure, retaining its deformation. The actuation unit is designed to additionally deform the foot and command the return of the total stored energy. The control strategy of the prosthesis adapts to changes in the user's cadence and loading conditions to return the energy at a desired stride phase. An early verification on two transtibial amputees during treadmill walking showed that the proposed mechanism could increase the subjects' dorsiflexion peak of 15.2% and 41.6% for subjects 1 and 2, respectively, and the cadence of about 2%. Moreover, an increase of 26% and 45% was observed in the energy return for subjects 1 and 2, respectively.

Classification of Lifting Techniques for Application of A Robotic Hip Exoskeleton.

The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users' movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class "no lift") were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement.

A Real-Time Lift Detection Strategy for a Hip Exoskeleton.

Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect user's lift movement, which limits the wide application of active exoskeletons in factories. In this paper, we propose a simple but effective lift detection strategy for exoskeleton control. This strategy uses only exoskeleton integrated sensors, without any extra sensors to capture human motion intentions. This makes the lift detection system more practical for applications in manufacturing environments. Seven healthy subjects participated in this research. Three different sessions were carried out, two for training and one for testing the algorithm. In the two training sessions, subjects were asked to wear a hip exoskeleton, controlled in transparent mode, and perform repetitive lifting and a locomotion circuit; lifting was executed with different techniques. The collected data were used to train the lift detection model. In the testing session, the exoskeleton was controlled in order to deliver torque to assist the lifting action, based on the lift detection made by the trained algorithm. The across-subject average accuracy of lift detection during online test was 97.97 ± 1.39% with subject-dependent model. Offline, the algorithm was trained with data acquired from all subjects to verify its performance for subject-independent detection, and an accuracy of 97.48 ± 1.53% was achieved. In addition, timeliness of the algorithm was quantitatively evaluated and the time delay was <160 ms across different lifting speeds. Surface electromyography was also measured to assess the efficacy of the exoskeleton in assisting subjects in performing load lifting tasks. These results validate the promise of applying the proposed lift detection strategy for exoskeleton control aiming at lift assistance.