Model-Based Approach for Posture and Movement Classification in Working Environments


In this paper, we present an approach for model-based movement and posture classification in working environments. The approach presented here is designed for long-term in-situ observations of and by workers in their workplaces. The proposed model is adaptable to different input data, eg, skeleton data from either an Inertial Measurement Unit (IMU) or a skeleton derived from an optical sensor such as Kinect. We present a preliminary design of the model and suggest algorithms suitable for real-time usage of the model in an IMU-based motion capture suite. In an experiment we measured the weight on the knee while performing different kneeing postures to show the dependence of posture angles on the knee load.

Ambient Assisted Living