Loss of locomotion is one major problem faced by the elderly currently. Various rehabilitation technologies have been developed to assist them in recovering walking capability. Gait monitoring is an important aspect of lower-limb function rehabilitation. By observing the walking behaviour of a patient, the stability and recovery progress can be evaluated. Despite gait is often measured by motion capture and force-based measurement, these methods are costly and non-portable, whereas inertial measurement units (IMUs) require the attachment of sensors onto the subject’s body. A few contactless measurements have been proposed, however, none of them views the feet from the back, making it non-trivial to transfer the method to overground rehabilitation robots. This paper proposes a method to track the poses of the feet in real time using a novel deep neural network, termed SDF-Net, that models the signed distance function (SDF) of an object. Independent of the viewing angle, the algorithm receives the colour and depth images of the feet as input and computes the pose of the feet. The tracking accuracy is evaluated by having a subject to perform various actions with the feet; the dynamic errors are found to be less than 9 mm and 8 degrees for position and orientation errors respectively, which are better than the state-of-arts reviewed.

In the past few years, rehabilitation technology has been one of the emerging fields in the robotics area. H-Man, a 2D planar robot, was developed for upper extremities neurorehabilitation. The current design allows elbow and shoulder motor function recovery. To further expand its use to distal components of upper limb physiotherapy, a modular end-effector capable of measuring grip force and vertical displacement is developed to be implemented on the manipulandum. The grip force measurement yields results with less than 1N (3%) mean error and 0.6N (2%) standard deviation, demonstrating the feasibility of the end effector.