Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms

Latent Ergonomics Maps: Real-Time Visualization of Estimated Ergonomics of Human Movements

Improving the ergonomy of working environments is essential to reducing work-related musculo-skeletal disorders. We consider real-time ergonomic feedback a key technology for achieving such improvements. To this end, we present supportive tools for online evaluation and visualization of strenuous efforts and postures of a worker, also when physically interacting with a robot. A digital human model is used to estimate human kinematics and dynamics and visualize non-ergonomic joint angles, based on the on-line data acquired from a wearable motion tracking device.

Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.

Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques

The paper presents a stochastic methodology for the simultaneous floating-base estimation of the human whole-body kinematics and dynamics (i.e., joint torques, internal and external forces). The paper builds upon our former work where a fixed-base formulation had been developed for the human estimation problem. The presented approach is validated by presenting experimental results of a health subject equipped with a wearable motion tracking system and a pair of shoes sensorized with force/torque sensors while performing different motion tasks, e.g., walking on a treadmill. The results show that joint torque estimates obtained by using floating-base and fixed-base approaches match satisfactorily, thus validating the present approach.