FormCheck

Inspiration

The inspiration behind FormCheck stems from a deep commitment to promoting safe and effective rehabilitation practices. Recognizing the critical role that proper form plays in the success of physical therapy exercises, our team sought to develop a solution that empowers individuals to perform their exercises with precision and confidence. Drawing upon our collective expertise in physiotherapy and technology, we envisioned a tool that not only guides users through correct form but also educates and motivates them to maintain optimal technique throughout their rehabilitation journey. Inspired by the transformative potential of combining technology with therapeutic principles, FormCheck is driven by a passion for helping individuals recover from injury and achieve their wellness goals with precision and care.

What it does

FormCheck is an innovative tool designed to assist individuals in performing physical therapy exercises with proper form and technique. Utilizing advanced technology, FormCheck provides real-time feedback and guidance to users, ensuring that each movement is executed correctly and safely. Through interactive visual cues and personalized coaching, FormCheck helps users maintain optimal alignment, range of motion, and muscle activation during their rehabilitation exercises. By promoting correct form and technique, FormCheck aims to enhance the effectiveness of physical therapy sessions, reduce the risk of injury, and accelerate the recovery process for individuals recovering from injury or surgery.

How we built it

We used a python library called Sklearn to train an Arduino UNO equipped with an accelerometer to monitor movements during physical therapy. This would be used to analyze movement of a joint during physical therapy to flag incorrect exercises that could result in further injury.

Challenges we ran into

It was difficult to get the Sklearn to work with the output generated by the Arduino. We also tried incorporating wireless capabilities but were unable to add this feature due to faulty Bluetooth modulators.

Accomplishments that we're proud of

We're proud of our use of Sklearn to build a model that tracks incorrect movements during physical therapy.

What we learned

We learned how to exchange information between arduinos and python libraries.

What's next for FormCheck

Next we would like to integrate wireless capabilities and add several other accelerometers.

https://drive.google.com/file/d/1dB8irh04kNHHMEiqj5vyxwLIebx7FQEz/view

Built With

• arduino
• python
• sklearn