Paolo Bonato

Existing clinical assessments for upper-limb motor rehabilitation poststroke pose limitations as end points for efficient clinical trials. This study aimed to develop a digital metric for assessing motor recovery using accelerometer data collected in naturalistic environments. We constructed the digital arm performance scale (DAPS) by analyzing ∼23,000 hours of data from 215 participants, including healthy individuals and subacute and chronic stroke survivors. We decomposed continuous upper-limb accelerometer data into lower-level movement segments, from which key features were extracted and aggregated using a linear mixed-effects model to produce an interpretable digital biomarker. DAPS demonstrated excellent reliability, sensitivity, concurrent validity, known-groups validity, discriminant validity, and responsiveness. Power analysis indicated that DAPS could reduce the required sample size for clinical trials with upper-limb motor recovery end points by more than 60% compared with traditional assessments. These findings highlight the potential of DAPS as a low-burden, scalable assessment tool for upper-limb motor recovery, with potential applications in both clinical trials and practice.

Measuring neuromotor control after stroke is crucial for identifying the mechanisms underlying asymmetrical walking and guiding rehabilitation. The lower extremity portion of the Fugl-Meyer (FM-LE) and the number of muscle synergies are commonly used measures, but have important limitations. The dynamic motor control index has emerged as a complementary metric, yet its relationship to established clinical measures (i.e., FM-LE), muscle synergy number, and gait biomechanics remains unclear. This study evaluated the ability of the dynamic motor control index to quantify post-stroke neuromotor impairment relative to FM-LE and muscle synergy number and examined its relationship with propulsion asymmetry. Electromyography data from 22 individuals post-stroke and 31 neurotypical controls were analyzed using non-negative matrix factorization. The dynamic motor control index and not the muscle synergy number differentiated paretic, non-paretic, and neurotypical limbs (χ2(2) = 27.57, p < .001). It also differed significantly between less and more impaired individuals classified by FM-LE ( p = .05) and demonstrated good discriminative performance between these groups (AUC: 0.777, p = .017). The index also moderated the relationship between FM-LE and propulsion asymmetry (ΔR ² = 0.223, p = .007). These findings support the dynamic motor control index as a clinically relevant msarker of post-stroke neuromotor impairment and recovery.

Lower-limb exoskeletons (LLE) have proven to be effective tools in rehabilitation for diminishing gait impairments. However, modern LLE control systems based on the assistance-as needed concept face challenges in providing appropriate interaction torques to the user. This is especially true when it is necessary to make the exoskeleton transparent, i.e. with zero user robot interaction torques (τUR), even during walking speed variations, when no assistance is required. To address these limitations, in this work we designed a novel transparent control system that utilizes a baseline torque controller, which generates a torque profile based on τUR from previous strides, and drives the actuators, leading to a lower τUR, along with a synchronization layer comprising a pool of adaptive oscillators (AOs) to estimate the user's gait phase and adapt the controller to different walking speeds. Additionally, a zero-torque controller was used in parallel to the main torque controller to enhance users' volitional control and balance. The proposed controller was experimentally tested on a six-DoF LLE with eight healthy participants who walked for 200 gait strides at three walking speeds. The baseline torque controller was shown to adapt to different walking speeds and successfully improve the exoskeleton transparency in terms of user-robot interaction, reducing the average τUR by 40% of the entire exoskeleton and 70% of the hip joint at 0.8 m/s, and gait kinematics compared to our prior controller based on a zero impedance model.

When human instructors guide learners through motor tasks, they seamlessly coordinate physical touch with verbal explanations - a dance teacher positions a student's arms while describing the movement, a therapist supports a patient's limb while offering encouragement. In contrast, a robot applying physical forces without verbal context can feel invasive or unsettling to humans. We present a robot guidance controller that learns to coordinate physical and verbal guidance as human instructors naturally do. Our system adaptively balances these modalities based on real-time estimation of human compliance: when learners struggle, it provides firmer physical corrections with explicit instructions; as they improve, it transitions to lighter touch with encouraging phrases. Our method comprises three components: (1) an estimator that infers physical and verbal compliance from tracking errors, (2) an optimization method that dynamically allocates guidance between force and language, and (3) a force-to-language model that generates contextually appropriate utterances. User studies (N=12) demonstrate that adaptive coordination of guidance significantly outperforms single-modality guidance and fixed-combination baselines: up to 50% reduction in tracking error, 39% improvement in movement smoothness, and 27% faster task completion. While validated in rehabilitation therapy, our approach generalizes to any human-robot collaborative learning scenario.

Continuous, objective, and precise upper-limb motor assessments are essential for realizing the vision of precision rehabilitation for stroke survivors. Wearable inertial sensors have emerged as a promising solution, enabling the analysis of motor performance in real-world settings. Recent studies have introduced two movement segmentation methods—anatomical segmentation and linear segmentation—for processing wearable inertial data to monitor post-stroke upper-limb motor recovery, each grounded in distinct theories of motor control and behavior. These methods differ in their practical implications for clinical use: linear segmentation requires only a single wearable device on the stroke-affected wrist, while anatomical segmentation necessitates an additional sensor on the sternum. This study seeks to systematically compare the clinimetric performance of these two approaches, taking into account their differences in practicality, to provide insights into their effective integration into clinical practice. 17 stroke survivors were equipped with inertial sensors on the trunk and the stroke-affected wrist while performing activities of daily living in a simulated apartment setting. Acceleration time-series from wrist movements were decomposed into movement segments using each movement segmentation approach. Reliable features were extracted from the movement segments, and supervised regression models were trained to establish concurrent validity against existing clinical measures. Anatomical segmentation demonstrated strong concurrent validity against existing clinical measures but may face challenges for continuous use due to the need for multiple sensors. Linear segmentation, on the other hand, provided slightly reduced but acceptable performance in motor deficit assessment while offering the advantage of requiring only a single wrist-worn sensor.

The Heat Distribution Index (HDI) has been used to capture the abnormal variance in knee temperature distribution as a proxy for inflammation in osteoarthritis. HDI has been derived from thermal camera (TC) data, but its use has been limited to static conditions, as TCs are impractical for dynamic assessments. This study evaluates the feasibility of using a wearable array of thermistors to assess HDI changes in response to exercise, using TC data as reference. Ten healthy participants were enrolled, and knee skin temperature was measured pre-, post-exercise, and during a cooldown period of approximately 30 min. The thermistor-based system detected a significant HDI increase post-exercise (ΔHDI = 0.76°C, p < 0.001) consistent with the TC results (ΔHDI = 0.71°C, p < 0.001), with no statistical difference detected between the two methods (p > 0.05). During cooldown, both systems tracked recovery trends without detectable differences (p > 0.05). The mean HDI error for the thermistor array was 0.20 ± 0.36°C. Simulations assessing performance and sources of error showed negligible bias in HDI estimates regardless of the number of thermistors, while variance decreased as thermistor count increased. A more uniform sensor distribution across the region of interest improved accuracy over clustering near its edges, while misplacement had minimal impact except for rare outliers. These findings support the feasibility of a wearable array of thermistors for assessing exercise-induced HDI changes. Improvements in sensor placement to maximize skin contact while allowing for perspiration may enhance reliability. This work lays the foundation for real-time thermal monitoring and may aid in developing wearable tools to monitor patients with knee osteoarthritis.

Background: Research over the past twenty years has shown that gait outcomes have a high sensitivity for diagnosing Parkinson's disease (PD), for detecting the effects of interventions, and for monitoring disease progression, even in early disease. However, the lack of standardization in protocols and reported gait measures is impeding data aggregation across study sites and contributes to heterogeneity in the results, thus limiting the adoption of gait outcomes in clinical trials. Objective: To provide recommendations for a minimum set of gait measures to be adopted in projects evaluating people with PD to enhance standardization across the field. Methods: The Gait Advisors Leading Outcomes for Parkinson's (GALOP) committee is an advisory committee for the MJFF. Based on a five-step approach, GALOP generated recommendations for standardizing protocols that assess quantitative gait measures, following expert consensus on best practices. Results: Built on the literature and consensus amongst experts, we recommend a minimum set of meta-data to accompany gait protocols and a minimum gait assessment protocol to be performed at a comfortable speed. Suggestions on challenging testing are provided. Conclusions: To support and empower the scientific community, we have generated recommendations to collect and share gait data gathered from people with PD using an open data repository. Standardizing gait protocols and outcomes in PD has the potential of accelerating research and clinical trials, harmonizing protocols across study sites, fostering collaborations, and in the long run, improving patient care and quality of life.

This study presents a novel wearable solution integrating Polymer Optical Fiber (POF) sensors into a knee sleeve to monitor knee flexion/extension (F/E) patterns during walking. POF sensors offer advantages such as flexibility, light weight, and robustness to electromagnetic interference, making them ideal for wearable applications. However, when one integrates these sensors into a knee sleeve, they exhibit non-linearities, including hysteresis and mode coupling, which complicate signal interpretation. To address this issue, a Long Short-Term Memory (LSTM) network was implemented to model temporal dependencies in sensor output, hence providing accurate knee angle estimates. Data were collected from 31 participants walking at different speeds on a treadmill, using a camera-based motion capture system for validation. Configurations with multiple (up to five) sensors were considered. The best performance was achieved using three sensors, yielding a median root mean square error (RMSE) of 3.41° (interquartile range: 2.50° – 5.19°). Whereas using multiple sensors generally improved robustness, the inclusion of data from sub-optimally placed sensors negatively affected performance. The technology holds potential for clinical application in knee osteoarthritis (OA) management. Future work should focus on optimizing signal calibration and expanding the dataset to facilitate accounting for the different ways in which the knee sleeve conforms to the anatomy of different individuals.

Children with cerebral palsy (CP) often experience gait impairments. Robot-assisted gait training (RGT) has been shown to have beneficial effects in this patient population. However, clinical outcomes of RGT vary substantially from patient to patient. This study explored the hypothesis that clinical outcomes are associated with changes in muscle synergies in response to RGT. Thirteen children with CP and Gross Motor Function Classification Scale (GMFCS) levels I–IV were recruited in the study. Children participated in a 6 week-RGT intervention and underwent clinical evaluations and gait studies—with focus on the analysis of electromyographic (EMG) data—pre- and post-training. Lower-limb muscle synergies were derived from the EMG recordings. Pre- vs. post-RGT clinical outcomes and muscle synergies were compared to explore potential relationships. Three and, less often, two muscle synergies were detected in study participants pre-RGT. Linear mixed effect models showed that composition of the muscle synergies and their temporal activation coefficients present deviations from normative data proportional to the severity of functional limitations (i.e., GMFCS levels, p < 0.01). At a group level, changes in muscle synergies pre- vs. post-RGT did not significantly correlate with changes in clinical outcomes (p > 0.05). However, it was observed that participants who displayed prominent changes in muscle synergies also displayed large improvements in clinical scores. Gait impairments in children with CP were associated with muscle synergies that deviated from normative. Participants who demonstrated the most substantial improvements in clinical scores following RGT exhibited multiple changes in the muscle synergies. However, no statistically significant correlations were identified at the group level. Future studies relying on larger datasets are needed to further investigate this observation and potential underlying mechanisms.

Existing clinical assessments for upper-limb motor rehabilitation post-stroke pose limitations as endpoints for clinical trials. This study aims to develop a wearable-based digital biomarker for assessing motor recovery using accelerometer data collected in naturalistic environments. The study analyzed approximately 23,000 hours of data from 215 participants, including subacute and chronic stroke survivors and healthy individuals. A novel analytical approach decomposed continuous accelerometer data into a lower-level unit of motor behaviors called movement segments, from which key features were extracted and aggregated using a linear mixed-effects model to produce a composite biomarker. The resulting digital biomarker demonstrated excellent interpretability, reliability, concurrent validity, discriminant validity, known-group validity, and responsiveness, enabling a nearly 66% reduction in the required sample size for clinical trials compared to traditional measures. These findings highlight its potential as a low-burden, scalable assessment tool for upper-limb motor recovery, with applications in both clinical trials and routine clinical practice.