These intelligent slippers sense regular activities and falls using machine learning
When it comes to activity monitors such as smartwatches, rings, and pendants, they are often considered cumbersome or too difficult to keep track of, especially for the elderly with memory or dexterity problems. This is why the team of Jure Špeh, Jan Adamic, Luka Mali, and Blaz Ardaljon Mataln Smehov decided to create the SmartSlippers project, which is a far more integrated method for detecting steps and falls.
The hardware portion of the SmartSlippers prototype is just a Nano 33 BLE Sense board due to its onboard inertial measurement unit (IMU) and Bluetooth® Low Energy capability. At first, the team collected 14 minutes of five different types of movements: walking, running, stairs, falling, and idle within the Edge Impulse Studio. From here, they trained a neural network on these samples, which resulted in an accuracy of around 84%.
With the Nano now able to detect motion, the next step was to get the board to talk with a phone so that emergency services could be called in the event of a fall. Their firmware sets up a BLE device and adds a characteristic that sends data to the connected phone when an event occurs. And finally, their custom Android mobile app displays the current status of the SmartSlippers and can even call someone if a fall is detected.
To read more about the project, you can visit its write-up here on Hackster.io.