An IMU-to-Body Alignment Method Applied to Human Gait Analysis

Abstract

This paper presents a novel calibration procedure as a simple, yet powerful, method to place and align inertial sensors with body segments. The calibration can be easily replicated without the need of any additional tools. The proposed method is validated in three different applications: a computer mathematical simulation; a simplified joint composed of two semi-spheres interconnected by a universal goniometer; and a real gait test with five able-bodied subjects. Simulation results demonstrate that, after the calibration method is applied, the joint angles are correctly measured independently of previous sensor placement on the joint, thus validating the proposed procedure. In the cases of a simplified joint and a real gait test with human volunteers, the method also performs correctly, although secondary plane errors appear when compared with the simulation results. We believe that such errors are caused by limitations of the current inertial measurement unit (IMU) technology and fusion algorithms. In conclusion, the presented calibration procedure is an interesting option to solve the alignment problem when using IMUs for gait analysis.

Compensation technique for environmental and light source power variations applied in a polymer optical fiber curvature sensor for wearable devices

“…In addition, the errors are similar to or lower than the ones obtained in and Leal-Junior et al (2017b). Comparing with commercially available devices, the proposed POF curvature sensor presents errors lower than electrogoniometers (Piriyaprasarth and Morris, 2007) and IMUs (Vargas-Valencia et al, 2016). The authors emphasize that such errors can be further reduced with the analysis and compensation of the polymer viscoelastic response, which will be explored on future works by means of an analysis of the POF material properties and characterization of the viscoelastic response that leads to errors on the POF curvature sensor measurements (Leal-Junior et al, 2017a), where such reduction of the errors can provide a more reliable sensor for human movement analysis and wearable robots applications.…”

“…This value is lower than the errors obtained with IMUs (Vargas-Valencia et al, 2016) with the additional advantages of low cost, easiness of implementation and greater simplicity on signal processing. Comparing with other POF-based curvature sensors based on the same operation principle, the sensor with the developed compensation technique present smaller errors than the ones found in Bilro et al (2011), Donno et al (2008 and Stupar et al (2012), which are 2°, 1.36° and 2.5°, respectively.…”

A Quality of Experience assessment of haptic and augmented reality feedback modalities in a gait analysis system

“…This complex number representation defines any spatial rotation around a fixed point or coordinate system. A quaternion q = [q0q1q2q3] was used to calculate an angle θ about a fixed Euler axis [46,47]. To get the angle between two joints with IMU, quaternion matrices were obtained by fusion of the 3 internal modules (Acc xyz , Gyro xyz , Mag xyz ) using a Madgwick-based orientation filter [48].…”

Wearable Biofeedback Suit to Promote and Monitor Aquatic Exercises: A Feasibility Study

“…It was in fact decided to acquire and process data using quaternions, which allows us, on the one hand, to improve computational efficiency, something crucial for real-time applications and, on the other hand, to avoid singularities [27], [28], especially in body motion estimation [11], [30]. Inertial-based human joint angle tracking has been already investigated in the literature (e.g., [23], [31]). However, a key aspect that is usually not addressed in motion tracking estimation using inertial sensors is the independence of the joints angle estimation from precise sensor placement.…”