Wearable Sensing

An understanding of local walking context plays an important role in the analysis of gait in humans and in the high level control systems of robotic prostheses. Laboratory analysis on its own can constrain the ability of researchers to properly assess clinical gait in patients and robotic prostheses to function well in many contexts, therefore study in diverse walking environments is warranted. A ground-truth understanding of the walking terrain is traditionally identified from simple visual data.

Wearable devices, such as data gloves and electronic skins, can perceive human hand's actions, behaviors and even emotions with the help of knowledge learning and inference. Curvature or magnetism sensing in such devices often lacks comprehensive gesture interactive information, meanwhile, the limited computing power of wearable applications restricts the multi-mode fusion of different sensing data and the deployment of deep learning networks.

We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate.

Human-robot interaction remains a significant challenge for mobile robots performing as social robots, which are expected to coexist, collaborate, and be cognitive with persons to enhance efficiency and reduce labor strength. Typically, escort robots can be widely employed for accompanying elderly and youth outdoors, guiding visitors in exhibition halls and museums, and sorting goods in warehouses.