Conditions such as Parkinson’s disease and essential tremors often present themselves as uncontrollable movements or spasms, especially near the hands. By recognizing when these troubling symptoms appear, earlier treatments can be provided and improve the prognosis for the patient compared to later detection. Nick Bild had the idea to create a small wearable band called “Introvention” that could sense when smaller tremors occur in hopes of catching them sooner.

An Arduino Nano 33 IoT was used to both capture the data and send it to a web server since it contains an onboard accelerometer and has WiFi support. At first, Bild collected many samples of typical activities using the Edge Impulse Studio and fed them into a K-means clustering algorithm which detects when a movement is outside of the “normal” range. Once deployed to the Arduino, the edge machine learning model can run entirely on the board without the need for an external service.

If anomalous movements are detected by the model, a web request gets sent to a custom web API running on the Flask framework where it’s then stored in a database. A dashboard shows a chart that plots the number of events over time for easily seeing trends.

To read more about Bild’s project, check out its write-up here on Hackster.io.