Improving Patient Knee Rehabilitation Using Smart Orthotics

We, Team VIBRANT, aim to facilitate the at-home knee rehabilitation process for both patient and clinician by integrating existing sensors into a wearable device capable of collecting and converting real-time data onto a user interface designed to improve the evaluation of the patient’s progression to recovery.

Check Out Our Pitch

Needs Statement

Frequent physical activity is urged by doctors in order to maintain a healthy heart and body. However, injuries are a prevalent part of physical activity, with the knee being one of the most commonly injured joints on the body at a rate of 2.29 knee injuries per 1000 people. Once the knee is injured, it is difficult to resume daily routines immediately due to the long recovery period these injuries entail; rehabilitation and recovery time can take anywhere from 1 week to a year based on the severity of the injury. During a lengthy rehabilitation period, it can become difficult to track the patient’s progress, especially in combination with the need to incessantly meet with his/her clinician. Therefore, a remote method of treatment is beneficial in order to increase the overall effectiveness of the rehabilitation process.

What Is Our Device?

Our device is a knee sleeve with integrated sensors which will measure muscle strength and range of motion data. This data will then be transformed onto a readable user interface which will help physical therapists and patients in the recovery process.

Who Are We?

A team of senior Materials Science Engineering and Biomedical Engineering students enrolled in the BioENGINE program at the University of California Irvine.

Our Solution

In order to solve the problem of compliance and limited visits, our device records relevant data and sends it to the mobile application. This allows the patient to view their progress and be more compelled to work harder and the clinician to track the patient remotely.

Elements of the Device

How Data Metrics Are Being Measured

Range of Motion Sensing Approach

For our device, we plan on using a strain sensor from Dr. Michelle Khine’s lab. The strain sensor will be placed over the knee joint and range of motion can be detected from the amount of strain picked up from the sensor.

Muscle Strength Sensing Approach

The block diagram above shows how the EMG sensor would collect muscle data. Electrical activity in the muscle will be collected by electrodes placed onto the surface of the skin.

Myoware Muscle Sensor

The Myoware sensor is a surface EMG sensor which will be connected to an Arduino to collect muscle activity data. There will be two Myoware on the knee sleeve: one for picking up knee extensor data, and one for knee flexor data.

Product Development So Far

Project Progression

Current Prototype

The current prototype is an old knee sleeve with an Arduino and Myoware sensor sewn on. The Arduino is connected to a computer in order to collect data. Electrodes are also connected to the myoware which captures data from the vastus lateralis quadriceps muscle.

Computer-Aided Design

This image shows a model of a load cell fixture we will be using to test our EMG data against force measured by a load cell. Force measurements will be taken during exercises as well as during maximal contraction of the muscle.

User Interface

There will be both a patient user interface and a clinician user interface. The patient UI will show the patients exercise protocol and progress. The clinician interface will be more detailed with qualitative data about the patient’s progress.

Mobile App Example

Possible iteration of how the mobile app accompanying the device will look.

Major Tasks Set By the Team

project objectives

Team Members:
Sydney Ishikawa
Rochelle Lucero

Data Acquisition From the Sensor Utilize algorithms and software to ensure the sensors acquire and process measured data

Team Members:
Harsharan Chahal
Bryan Huynh

Integrated Wearable Device Design Develop a sleeve design that integrates sensors into the wearable device

Team Members:
Mohammad Talah Anwar
Anthony Nguyen

Data Conversion Into User Interface Convert acquired data into an intelligible, presentable format for patients and clinicians