An Open-Source Wearable System for Real-Time Human Biomechanical Analysis

Abstract

The advancement of inertial measurement unit (IMU) technology has opened new opportunities for motion analysis, yet its widespread adoption in clinical practice remains constrained by the high costs of proprietary systems, lengthy setup procedures, and the need for specialized expertise. To address these challenges, we present a multi-IMU system designed with streamlined calibration, efficient data processing, and a focus on accessibility for patient-facing applications. Although initially developed for human gait analysis, the modular design of this system enables adaptability across diverse motion tracking scenarios. This work outlines the system's technical framework, including protocols for data acquisition, derivation of gait variables, and considerations for user-friendly software deployment. We further illustrate its utility by measuring lower-limb gait kinematics in near-real time and providing stride-to-stride biofeedback using a single sensor. These initial results underscore the potential of this system for both laboratory-based gait assessment and rehabilitation interventions in clinical environments and future work will assess validation against traditional optical motion capture methods.

Keywords: gait rehabilitation; inertial measurement unit; motion analysis; wearable sensors.

Figure 1

Kinematic components used in the calculation of the LLTE.

Figure 2

IMU Placement. IMU-fixed coordinate systems are shown, with x-axis (green), y-axis (blue), and z-axis (red). Sternum (a): at or slightly superior to the xiphoid process; sacrum (b): superior to the midpoint between the posterior superior iliac spine; thigh (c): anterior, approximately 15 cm proximal to the knee joint center defined by the midpoint between the femoral condyles; shank (d): anterior, approximately 15 cm distal to the knee joint center; foot (e): dorsal aspect halfway between the metatarsophalangeal joint and the ankle joint.

Figure 3

Illustration of corrections to the knee joint S/I position during stance phase for different gait presentations, namely, a stiff or flexed knee gait. The different colors in each of the panels indicate the same time point for each potential walking condition, Orange: Initial Contact, Green: Mid-stance, Blue: Terminal Stance.

Figure 4

Block diagram of ReBAIT system components and workflow.

Figure 5

Ensemble averages of time normalized stance phase lower extremity joint angles across participants during normal walking on a treadmill at four different speeds (0.80 m s⁻¹ panels (A–C), 1.00 m s⁻¹, panels (D–F), 1.25 m s⁻¹, panels (G–I) and 1.50 m s⁻¹, panels (J–L)). The solid lines and transparent error bands denote the mean and one standard deviation, respectively.

Figure 6

Time normalized A/P knee position (panel (A)), S/I knee position (panel (B)), and sagittal-plane shank angle (panel (C)) trajectories across stance that are components of the LLTE for each walking speed. The solid lines and transparent error bands denote the mean and one standard deviation, respectively.

Figure 6

Time normalized A/P knee position (panel (A)), S/I knee position (panel (B)), and sagittal-plane shank angle (panel (C)) trajectories across stance that are components of the LLTE for each walking speed. The solid lines and transparent error bands denote the mean and one standard deviation, respectively.

Figure 7

Values of the LLTE and its constituent components across walking speeds when compared to the aggregate 0.80 ms⁻¹ across participants as the target trajectory.