ENGG 41x0 - Design IV

Sept. 2025 - April 2026

Description

Developed a wearable knee osteoarthritis (OA) monitoring system to enable continuous, real-time tracking of joint motion and inflammation, specifically designed to support physiotherapists in remote rehabilitation monitoring and decision-making. The device integrates IMUs, pressure sensors, and temperature sensors within a knee sleeve to capture key biomechanical and physiological indicators during daily activities and prescribed exercises.

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The system architecture follows a sensor → microcontroller → wireless transmission → dashboard pipeline, where raw sensor data is processed using an Arduino Nano ESP32, converted into meaningful metrics (joint angle, pressure distribution, temperature trends), and transmitted via Wi-Fi for real-time visualization. Custom embedded code was developed for sensor interfacing, signal processing, and data acquisition, including filtering and synchronization of multi-sensor inputs to ensure reliable measurements.

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To improve measurement accuracy across users, the system incorporates individual calibration protocols, aligning sensor placement and baseline readings to each user’s anatomy and movement patterns. This enables consistent and clinically relevant tracking of joint kinematics and inflammation.

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A dual-dashboard system was developed using HTML, CSS, and JavaScript, consisting of:

• a physiotherapist dashboard for real-time data visualization, trend analysis, and report generation to support clinical decision-making

• a patient dashboard providing guided exercises, repetition tracking, and real-time feedback on joint motion

The project was conducted in compliance with Research Ethics Board (REB) requirements, ensuring ethical data collection, participant safety, and adherence to human-subject research protocols during prototype testing. The device was developed through an iterative design process involving concept evaluation, prototyping, and validation testing, resulting in a functional prototype capable of accurately measuring joint angles (0°–130° range) and detecting inflammation trends. The final system (~$218) demonstrates a cost-effective, non-invasive, and data-driven rehabilitation solution that enhances patient monitoring, reduces reliance on subjective reporting, and improves physiotherapy outcomes.