Abstract
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.
Highlights
In physical human–robot interaction domains, a huge variety of applications requires robots to actively collaborate with humans
The links were modeled with simple geometric shapes whose dimensions were estimated via inertial measurement units (IMUs) readings (i.e, Xsens motion capture system provides the position of several anatomical bony landmarks w.r.t. the origin of each link)
The simultaneous estimation of the human kinematics and dynamics is performed by means of a Maximum-A-Posteriori (MAP) algorithm
Summary
In physical human–robot interaction (pHRI) domains, a huge variety of applications requires robots to actively collaborate with humans. The simultaneous whole-body estimation of the human kinematics (i.e., motion) and dynamics (i.e., joint torques and internal forces) is a crucial component for modeling, estimating and controlling the interaction. In a recent study on the human joint muscular torques estimation during gait [14], two dynamical models have been considered separately for the legs to overcome the problem of the switching contact detection and to avoid the increasing complexity of the control algorithm for pattern classification. We decided to perform a different choice: we considered the dynamics of the human as a whole (with the pelvis as a floating base) and we developed an algorithm to detect the feet contact via additional sensors readings (force/torque sensors).
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