A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring...

Area of Science:

• Biomechanics
• Wearable Technology
• Sensor Technology

Background:

• Traditional motion capture systems are expensive and restrictive.
• There is a need for affordable, accessible wearable technology to monitor biomechanical data.
• Wearable sensors offer a promising solution for in-situ biomechanical analysis.

Purpose of the Study:

• To develop and validate a 3D printed knee wearable goniometer.
• To assess the accuracy of the wearable in measuring knee flexion angle.
• To evaluate an algorithm for real-time activity detection (standing, sitting, walking) using wearable sensor data.

Main Methods:

• Development of a 3D printed knee goniometer with a Hall-effect sensor.
• Integration with a mobile application for real-time data display.
• Validation against a commercial goniometer and an inertial sensor-based motion capture system (Aktos-t).
• Algorithm development using knee angle and angular speeds for activity recognition.

Main Results:

• The wearable goniometer demonstrated a mean absolute error between 2.46 and 12.49 when compared to commercial systems across different gait speeds.
• The activity detection algorithm achieved an average accuracy of 94.66% in detecting gait cycles during offline testing.
• The system successfully predicted user activities (standing, sitting, walking) in real-time during online testing.

Conclusions:

• The developed 3D printed knee wearable goniometer is a viable and accurate tool for measuring knee flexion.
• The integrated activity detection algorithm shows high efficacy in distinguishing between common daily activities.
• This wearable technology presents a cost-effective and practical solution for biomechanical monitoring outside laboratory settings.