Digital Magnetic Tensor Sensor With ANN Measurement Model for Human Joint Motion Sensing in Sagittal Plane

Abstract

Motivated by the increased needs of home-based rehabilitation for stroke patients, more and more interest has been drawn towards developing body-fixed sensors for monitoring affected joint motions. Although relatively accurate bone geometries can be obtained by scanning technologies, most human joints are approximated by simple circles and spheres to reduce the highly nonlinear kinematics to a tractable form for motion studies; many human joint-motion sensing challenges remain open. This article presents a novel magnetic tensor sensor (MTS) for noncontact tracing a human joint trajectory and a physics-based measurement model implemented on an artificial neural network (ANN) to account for un-modeled factors. The effects of input configurations and datatypes on measurement accuracy of an MTS/ANN have been numerically investigated with published data and experimentally evaluated on a prototype pantographic exoskeleton worn on a human shank/foot. As demonstrated experimentally, the MTS/ANN system calibrates the sensor intrinsic parameters, accounts for the environmental magnetic effects on the measurements, and can be trained with both offline and user-specific data.