Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons.

Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system.

Inverse Dynamics Modeling of Paralympic Wheelchair Curling.

Paralympic wheelchair curling is an adapted version of Olympic curling played by individuals with spinal cord injuries, cerebral palsy, multiple sclerosis, and lower extremity amputations. To the best of the authors' knowledge, there has been no experimental or computational research published regarding the biomechanics of wheelchair curling. Accordingly, the objective of the present research was to quantify the angular joint kinematics and dynamics of a Paralympic wheelchair curler throughout the delivery.

Modelling the deflection of rowing oar shafts.

The deflection of rowing oar shafts subjected to a static load was investigated. Two sets of sculling oars of different design stiffness were tested at three different lengths from 2.66 to 2.