IMU-Based Joint Angle Measurement for Gait Analysis

By Thomas Seel, Jörg Raisch, Thomas Schauer. Italian National Conference on Sensors, 2014

851 citations recorded by the source corpus.

This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.

Subfields: Aerospace Engineering, Biomedical Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3908e3aa7bb24b0f73693c7b5b84e8bc4971a0dc

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Citations in the corpus

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion (2017, methodology): “In some cases it has been used for gait analysis [36,37,79,93], in other cases only parts of the lower limbs were targeted, mainly for monitoring purposes in medical settings [94,96] or rehabilitation [44,59].”
Using Inertial Sensors for Position and Orientation Estimation (2017, neutral): “This is specifically of concern in motion capture applications, since the magnetic field measured at the different sensor locations is typically different [87, 25, 35, 133].”
Inertial Sensor-Based Gait Recognition: A Review (2015, methodology): “…gait parameters, i.e. , spatio-temporal parameters (cadence, step length, gait symmetry, inner-foot distance, etc. ) [15], detection of gait phases [5] or kinematic parameters (joint angle measurement) [4,16] either expressed implicitly by applying special feature extraction techniques.”