For the past two years I've been using hacked generic activity trackers as a rapid prototyping platform for wearable devices - primarily gesture recognition biofeedback and position tracking devices at the CMI MATTER Lab, a research institute focused on children's mental health.
My goal is to present generic nRF52832 and nRF51822 ARM Cortex activity trackers, in particular the X9 Pro, as a full fledged platform for prototyping low power devices accessible to anyone familiar with Arduino.
The $35 X9 Pro Activity Tracker is equivalent to ALL these Adafruit Products COMBINED:
- Adafruit Feather nRF52 Bluefruit LE ($24.95)
- Adafruit Vibrating Mini Motor ($1.95)
- Adafruit Pulse Sensor Amped ($24.95)
- Adafruit Triple-Axis MMA8451 Accelerometer ($7.95)
- Adafruit Lithium Ion Polymer Battery - 3.7v 100mAh ($5.95)
- Adafruit OLED Breakout Board - 16-bit Color 0.96" ($29.95)
TOTAL: $95.70 not including brushed steel enclosure, silicone strap and general miniaturization
Devices That Use This Platform (see Project Logs):
- "Tingle", a gesture recognition and biofeedback device for compulsive mental health disorders like trichotillomania and excoration.
- "Thermo", a hand position tracking device that does not require an independent camera or other external reference data (think mobile Oculus and HTC Vive controllers).
- Intraoral Respiration Monitor for Overdose Detection - a computer (hacked smartwatch) worn entirely inside your mouth with air pressure, humidity and temperature sensors.
Tutorials and Example Code:
- Blink, Button, OLED, Web Bluetooth (GATT Notifications) and Bluetooth Serial debugging Arduino example sketches that work with the X9 activity trackers (and most other activity trackers mentioned in this project with slight modification)
- Use of neural networks (LSTM MLP by way of synaptic.js) for gesture recognition using accelerometer data from the X9 streamed into a web browser over Web Bluetooth
- Accessing additional GPIO ("pins") so you can customize your device and add additional components
Neural Network Gesture Recognition with Web Bluetooth Tutorial
Stream sensor data from your hacked activity tracker into a web browser using the experimental HTML5 Web Bluetooth API. Sample data in different positions and train a neural network to distinguish position ie recognize gestures.
This is a GitHub site so all you have to do is fork the GitHub repository and you can create your own version of this site in seconds. Customize and hack it! (info on Web Bluetooth)
X9 Pro Activity Tracker Components
- nRF52832 ARM Cortex M4 SoC/MCU
- vibration motor
- 96x64 Color OLED display with SSD1331 controller IC
- Kionic KX126 Accelerometer with interrupt...