Movement-controlled video games have potential to promote home-based practice of therapy activities. The success of therapy gaming interventions depends on the quality of the technology used and the presence of effective support structures. This study assesses the feasibility of a novel intervention that combines a co-created gaming technology integrating evidence-based biofeedback and solution-focused coaching (SFC) strategies to support therapy engagement and efficacy at home. Following feasibility and single-case reporting standards (CONSORT and SCRIBE), this was a non-blind, randomized, multiple-baseline, AB, design. Nineteen (19) young people with cerebral palsy (8–18 years old) completed the 4-week home-based intervention in France and Canada. Participant motivations, personalized practice goals, and relevance of the intervention to daily activities were discussed in a Solution Focused Coaching-style conversation pre-, post-intervention and during weekly check-ins. Participants controlled a video game by completing therapeutic gestures (wrist extension, pinching) detected via electromyography and inertial sensors on the forearm (Myo Armband and custom software). Process feasibility success criteria for recruitment response, completion and adherence rates, and frequency of technical issues were established a priori. Scientific feasibility, effect size estimates and variance were determined for Body Function outcome measures: active wrist extension, grip strength and Box and Blocks Test; and for Activities and Participation measures: Assisting Hand Assessment (AHA), Canadian Occupational Performance Measure (COPM) and Self-Reported Experiences of Activity Settings (SEAS). Recruitment response (31%) and assessment completion (84%) rates were good and 74% of participants reached self-identified practice goals. As 17% of technical issues required external support to resolve, the intervention was graded as feasible with modifications. No adverse events were reported. Moderate effects were observed in Body Function measures (active wrist extension: SMD = 1.82, 95%CI = 0.85–2.78; Grip Strength: SMD = 0.63, 95%CI = 0.65–1.91; Box and Blocks: Hedge’s g = 0.58, 95%CI = -0.11–1.27) and small-moderate effects in Activities and Participation measures (AHA: Hedge’s g = 0.29, 95%CI = -0.39–0.97, COPM: r = 0.60, 95%CI = 0.13–0.82, SEAS: r = 0.24, 95%CI = -0.25–0.61). A definitive RCT to investigate the effectiveness of this novel intervention is warranted. Combining SFC-style coaching with high-quality biofeedback may positively engage youth in home rehabilitation to complement traditional therapy. ClinicalTrials.gov, U.S. National Library of Medicine: NCT03677193.

Over the past few years, there has been notable advancement in the field of Quantified Gait Analysis (QGA), thanks to machine learning techniques. QGA and gait prediction are areas where Deep learning (DL) techniques are gaining popularity. There has been a significant amount of attention from the scientific community on the application of gait analysis in various fields. Based on our understanding, there is a noticeable absence of a comprehensive review and current understanding of gait analysis utilizing DL and Multi-task learning (MTL) models. Therefore, this paper provides a comprehensive assessment of the current application of DL algorithms for QGA. The study takes a systematic approach to explore this topic in depth. We conducted a thorough search of three databases, namely Web of Science, IEEEXplore, and Scopus, to identify relevant papers published from 1989 to October 2023. A total of 55 papers were considered eligible and included in this review. Approximately 46% of the studies that were identified utilized classification models to categorize gait phases and locomotion modes. Additionally, a significant portion of the studies (45%) utilized regression models to estimate and predict various kinematic and kinetic parameters, including joint angles, trajectories, moments, and torques. Interestingly, a notable 9% of the studies employed the use of MTL techniques in the realm of DL for gait analysis. We have also provided information on the most commonly utilized datasets for QGA.

This study addresses the characterization of normal gait and pathological deviations induced by neurological diseases, considering knee angular kinematics in the sagittal plane. We propose an unsupervised approach based on Dynamic Time Warping (DTW) to identify different normal gait profiles (NGPs) corresponding to real cycles representing the overall behavior of healthy subjects, instead of considering an average reference, as done in the literature. The obtained NGPs are then used to measure the deviations of pathological gait cycles from normal gait with DTW. Hierarchical Clustering is applied to stratify deviations into clusters. Results show that three NGPs are necessary to finely characterize the heterogeneity of normal gait and accurately quantify pathological deviations. In particular, we automatically identify which lower limb is affected for Hemiplegic patients and characterize the severity of motor impairment for Paraplegic patients. Concerning Tetraplegic patients, different profiles appear in terms of impairment severity. These promising results are obtained by considering the raw description of gait signals. Indeed, we have shown that normalizing signals removes the temporal properties of signals, inducing a loss of dynamic information that is crucial for accurately measuring pathological deviations. Our methodology could be exploited to quantify the impact of therapies on gait rehabilitation.

Autonomous driving is achieved by controlling the coupled nonlinear longitudinal and lateral vehicle dynamics. Longitudinal control greatly affects lateral dynamics and must preserve lateral stability conditions, while lateral controllers must take into account actuator limits and ride comfort. This work deals with the coordinated longitudinal and lateral control for autonomous driving. An improved particle swarm optimized PID (PSO-PID) is proposed to handle the task of speed tracking based on nonlinear longitudinal dynamics. An enhanced linear parameter varying model predictive controller (LPV-MPC) is also designed to control lateral dynamics, the latter is formulated with an adaptive LPV model in which the tire cornering stiffness coefficients are estimated by a recursive estimator. The proposed LPV-MPC is enhanced with an improved cost function to provide better performance and stability. Matlab/Carsim co-simulations are carried out to validate the proposed controllers.