Biomechanical Effects of Using a Passive Exoskeleton for the Upper Limb in Industrial Manufacturing Activities: A Pilot Study.

This study investigates the biomechanical impact of a passive Arm-Support Exoskeleton (ASE) on workers in wool textile processing. Eight workers, equipped with surface electrodes for electromyography (EMG) recording, performed three industrial tasks, with and without the exoskeleton. All tasks were performed in an upright stance involving repetitive upper limbs actions and overhead work, each presenting different physical demands in terms of cycle duration, load handling and percentage of cycle time with shoulder flexion over 80°.

Biopsychosocial effects and experience of use of robotic and virtual reality devices in neuromotor rehabilitation: A study protocol.

Background: Robot-assisted therapy (RAT) and virtual reality (VR)-based neuromotor rehabilitation have shown promising evidence in terms of patient's neuromotor recovery, so far. However, still little is known on the perceived experience of use of robotic and VR devices and the related psychosocial impact. The present study outlines a study protocol aiming to investigate the biopsychosocial effects and the experience of use of robotic and non-immersive VR devices in patients undergoing neuromotor rehabilitation.

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks.

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE.

Exergame for the functional rehabilitation of adults over 55 with neurological diseases.

Rehabilitation based on Exergame is showing a rapid evolution, with interesting applications for the recovery of mobility, balance, postural control, coordination and fine motor skills, and including home-based training. At present, there are no precise indications for Exergamebased rehabilitation of people over 55 affected by stroke, Parkinson's disease, or multiple sclerosis. This review examines the proposed modalities and the effectiveness of Exergame-based rehabilitation interventions for adults over 55 with stroke, Parkinson's disease or multiple sclerosis, highlighting the limitations, advantages, controversies and impact of this approach.

Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work.

A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability.