Step by Step: Enhancing Gait Analysis with Sensor-Equipped Robotic Platforms

Neurodegenerative diseases often result in pathological gait patterns, reducing mobility, stability, and overall functional capabilities. Given their impact on older adults' quality of life, early and accurate diagnosis is crucial for timely intervention. Traditional gait assessment technologies present some limitations related to low portability levels and user comfort. In this context, Socially Assistive Robots (SARs) offer an alternative by enabling non-intrusive gait monitoring while also supporting professional caregivers with objective measurements of users' motor performance. This study investigates the feasibility of using a mobile robotic platform to extract and analyze digital biomarkers related to gait activity. A novel pipeline was developed to automatically detect gait parameters from laser sensor data, segment the gait cycle, and compare these measurements against inertial measurement unit (IMU) data, which is the widely used approach. Results demonstrate a strong correlation (CI > 0.7) between laser-derived and IMU-based temporal gait parameters. However, discrepancies in step length measurements suggest that laser-based tracking provides more precise spatial information than IMU estimations. Additionally, this study explores the influence of the robotic platform on gait performance. Findings indicate that users walk faster when the robot is absent, despite its position behind them and out of sight. This suggests an unconscious adaptation to the robot's presence, aligning with previous studies on human-robot interaction.