RealRehab Capstone Project
RealRehab is a wearable-connected physical therapy platform developed as part of the Senior Capstone course (Fall 2025). The app helps patients perform rehab exercises (specifically ACL rehab exercises) correctly at home while allowing physical therapists to remotely configure, monitor, and adapt rehabilitation plans. The system combines motion sensors embedded in a knee brace with an iOS app and backend infrastructure to track movement, guide exercises in real time, and store recovery data for both patients and clinicians.
The current MVP focuses specifically on early-stage ACL knee rehabilitation, serving as a proof of concept for the broader system. RealRehab is intentionally designed to be extensible, with the potential to support additional joints and areas of the body in future iterations. The second semester of the capstone will focus on expanding beyond the initial ACL phase, building out a complete ACL rehabilitation journey that includes preset exercise protocols, deeper progression logic, and expanded customization tools for physical therapists.
Problem Statement:
At-home physical therapy lacks real-time feedback, accountability, and clinical visibility. Patients recovering from knee injuries often perform exercises incorrectly, lose motivation without structured guidance, and have limited access to consistent in-person physical therapy sessions. At the same time, physical therapists are unable to see how patients move at home, must rely heavily on self-reported progress, and have limited tools to remotely personalize and adjust rehabilitation programs.
Mission:
Our goal was to develop a rehabilitation platform that provides clarity, structure, and measurable progress for both patients and physical therapists. For patients, the app enables real-time guidance and clear visualization of whether exercises—such as a basic knee extension—are being performed correctly, while tracking improvement over time. For physical therapists, the platform supports the creation of personalized exercise programs and provides ongoing visibility into patient progress, enabling more informed adjustments throughout the rehabilitation process.
RealRehab is composed of three connected layers:
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Hardware: Knee brace with an IMU + flex sensor connected via Arduino BLE
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Mobile App (iOS): SwiftUI app for patients and physical therapists
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Backend: Supabase for authentication, rehab plans, and sensor data storage
Technical Flow
Patients:
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Perform guided rehab exercises
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Receive clear visual instructions
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Track progress without manual logging
Physical Therapists:
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Create and edit rehab plans
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Adjust exercise parameters (reps, rest, range)
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View patient details and progress remotely
Patient App Flow:
Patients log into the app and connect to their physical therapist using a secure access code, after which they are taken to a personalized dashboard displaying their assigned rehabilitation plan. They can set up a customized schedule with reminders, connect the knee brace via Bluetooth, complete a guided device calibration, begin their first exercise lesson, and then view results and progress data from that session. The flow is shown below, going from left to right:
Physical Therapist App Flow:
Physical therapists log into the app by entering their practice information, license number, and NPI number, then add patients to their account. From there, they create and customize rehabilitation plans by selecting exercises and adjusting parameters, save the plan to the patient’s profile, and monitor progress as patients complete their assigned sessions. The flow is shown below, going from left to right:
Key Features:
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Guided Calibration: Ensures accurate motion tracking per user
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Journey Map: Visual progression through rehab lessons
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Draggable Lesson Editor (PT): Configure reps, rest, and limits while being able to add custom lessons to the default plan
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Personlaized Scheduling (Patient): create a custom schedule to recieve reminders on when to complete exercises
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BLE Sensor Integration: Real-time motion data from the brace
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Automatic Data Saving: No manual tracking required
Hardware:
Arduino Nano 33 BLE Sense Rev 2
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Serves as the brain of the knee brace, handling sensor input, data processing, and Bluetooth Low Energy (BLE) communication with the mobile app
- It also utilizes a built-in IMU to support motion tracking and system responsiveness.
Quantity: 1
Supplier: Amazon
Catalogue #: ABX00070
Adafruit 9-DOF Orientation IMU Fusion Breakout – BNO085
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Measures how the leg moves using an accelerometer for direction and speed and a gyroscope for rotation, enabling accurate tracking of knee movement throughout each exercise.
Quantity: 1
Supplier: Adafruit
Catalogue #: 4754
Adafruit Long Flex Sensor (4.4″)
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Changes its resistance when it bendsThe Arduino measures this change in resistance as a changing voltage, allowing the system to detect knee flexion and extension during rehabilitation exercises.
Quantity: 1
Supplier: Adafruit
Catalogue #: 182
Champion Knee Brace
- Houses all of the hardware and circuitry
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needed to be flexible to allow for range of motion while also being tight enough to allow constant and reliable data capturing
Quantity: 1
Supplier: Amazon
Catalogue #: B00D6HDOOI
Patient completing the first lesson for knee extensions using the brace
Our “Killer Experiment”:
The killer experiment tested whether a patient could successfully complete a full rehabilitation lesson created by a physical therapist while receiving accurate, real-time feedback from the wearable sensors. This required validating that flex sensor data correctly mapped knee angle in degrees, IMU data accurately tracked leg orientation, calibration values transferred reliably from the database, and all prescribed lesson parameters and error conditions functioned as intended. Across 15 repeated trials, the system consistently displayed low-latency real-time feedback (averaging ~120 ms), correctly triggered all error messages for improper form, accurately reflected calibration data, and reliably tracked both angular movement and lateral leg drift. The experiment confirmed that the app, hardware, and backend functioned together as a stable, accurate, and repeatable MVP capable of guiding patients through a complete knee-extension lesson while meeting all core functional requirements.
Reflection:
This project was especially meaningful to me because it sits at the intersection of health, technology, and human-centered design, an area I am deeply passionate about. Building RealRehab allowed me to move beyond theoretical ideas and develop a fully functional system that integrates hardware, software, and clinical considerations into a real-world application. Professionally, the project strengthened my ability to work across disciplines, translate physiological movement into digital feedback, and design systems that prioritize both technical accuracy and user experience. It also reinforced my interest in pursuing health technology as a long-term focus, as I found the process of creating tools that directly support recovery, accessibility, and quality of life to be both challenging and deeply motivating.
To view the ideation and UI/UX choices behind the app, click the links below:
UX/UI Applications
Figma Design File + Case Study
To view our research, documentation, and the full app walkthrough, click the links below:
Research Presentation Deck Link
Full Video Walkthrough
FigJam Documentation
Research Report