Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation.

Background: Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outcomes. Thus, quantitative assessment of engagement towards rehabilitation using physiological data and subjective evaluations is increasingly becoming vital.

Ink-based textile electrodes for wearable functional electrical stimulation: A proof-of-concept study to evaluate comfort and efficacy.

Background: Functional Electrical Stimulation (FES) represents a promising technique for promoting functional recovery in individuals with neuromuscular diseases. Traditionally, current pulses are delivered through self-adhesive hydrogel Ag/AgCl electrodes, which allow a good contact with the skin, are easy-to-use and have a moderate cost. However, skin adherence decreases after a few uses and skin irritations can originate.

Identification of movement phenotypes from occupational gesture kinematics: Advancing individual ergonomic exposure classification and personalized training.

The identification of personalized preventive strategies plays a major role in contrasting the occurrence of work-related musculoskeletal disorders. This requires the identification of distinct movement patterns within large samples and the attribution of a proper risk level to each identified movement phenotype. We assessed the feasibility of this approach by exploiting wearable inertial measurement units to estimate the whole-body kinematics of 43 healthy participants performing 18 reach-to-manipulate movements, which differed based on the object's position in the space and the type of manipulation required.

Comparison of different mobile health applications for intervention in children and adolescent with overweight: a protocol for systematic review with meta-analysis and trial sequential analysis.

Introduction: Overweight in children is increasing worldwide. Innovative smartphone health applications (mHealth apps) have either sought to deliver single or multi-component interventions for the management of overweight in children. However, the clinical effects of these apps are poorly explored.

A Mobile Phone Intervention to Improve Obesity-Related Health Behaviors of Adolescents Across Europe: Iterative Co-Design and Feasibility Study.

Background: Promotion of physical activity, healthy eating, adequate sleep, and reduced sedentary behavior in adolescents is a major priority globally given the current increase in population health challenges of noncommunicable diseases and risk factors such as obesity. Adolescents are highly engaged with mobile technology, but the challenge is to engage them with mobile health (mHealth) technology. Recent innovations in mobile technology provide opportunities to promote a healthy lifestyle in adolescents.