Gait Cycle Duration Analysis in Lower Limb Amputees Using an IoT-Based Photonic Wearable Sensor: A Preliminary...

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Sensor Systems

Background:

• Objective gait analysis is crucial for diagnosing and monitoring neurological and musculoskeletal conditions.
• Current methods often rely on complex laboratory setups or expensive equipment, limiting accessibility.
• There is a need for low-cost, portable, and real-time gait assessment solutions.

Purpose of the Study:

• To demonstrate the feasibility of a single-point LiDAR sensor for wearable gait analysis.
• To develop and validate a low-cost wearable system for real-time assessment of Gait Cycle Duration (GCD) and gait symmetry.
• To explore the potential of IoT connectivity for remote gait data visualization.

Main Methods:

• A wearable sensor system integrating a single-point LiDAR module and IoT connectivity was developed.
• The sensor was positioned on the medial calf to detect contralateral limb crossing as a proxy for mid-stance.
• Gait Cycle Duration (GCD), Symmetry Ratio, and Symmetry Index were computed in real-time during simulated walking at various cadences.
• Sensor-based GCD was compared against visual annotation using Kinovea®.

Main Results:

• The wearable LiDAR system successfully estimated GCD and captured gait symmetry trends comparable to visual annotation.
• Most cycle-wise relative differences between the sensor and Kinovea® were below 13%.
• The system demonstrated reliable operation across different walking speeds with a material cost under 100 €.

Conclusions:

• A single-point LiDAR sensor integrated into a wearable, IoT-enabled system is feasible for portable and objective gait analysis.
• The low-cost nature and real-time capabilities support its potential for widespread clinical utility.
• Further validation with larger cohorts and gold-standard references is warranted to quantify accuracy and repeatability.