Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Abstract

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.

Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions

“…In this line, the study reported by Almeida [ 15 ] contributed to our work reported in this article. The authors developed a wearable acquisition system capable of collecting data from the IMU sensor and transporting the data collected via Wi-Fi to a client system developed in Python.…”

“…Complementary filters can be applied whenever there are two or more sources of the same information. For example, Almeida [ 15 ] used the angle calculated through the magnetometer sensor to compose the CF as a third source.…”