Create a device that keeps track of the kettlebell excersices automatically. The device should be integrated with existing health/fitness apps like (strava, trainingpeaks, fitbit, google fit,apple health).
Main puspose of the project is to have a simplified domain of "tracking kettlebell excersices" in order to learn and understand how to use accelerometer and gyro sensors.
- Brush-up on some Math.
- Evaluation and comparison of different accelerometer and gyro sensors.
- Theory and practical application of the different filters.
- FreeCAD - 3D modellign for the product prototypes.
- PCB design
- Bluthooth BLE, ANT+, MQTT
- Evaluation of batteries for portable projects.
References to the material that has been very usefull
- TinyML - Nice walkthrough of how to get started with Tensorflow Lite and succesfully deploy the model to a microcontroller. The book has examples of gesture detection based on the accelerometer and gyroscope data, that are inspired from the Jennifer Wang's presentation on gesture detection
- Vibration Analysis: FFT, PSD, and Spectrogram Basics
- SignSpeaker: A Real-time, High-Precision SmartWatch-based Sign Language Translator
- Classifying physical activity from smartphone data
- Computer science, meet exercise: How I built a push-up pedometer at a three day hackathon
- 1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jeff Roach
- Who's singing? Automatic bird sound recognition with machine learning - Dan Stowell
- Activity Recognition with Wearable Accelerometers Using Deep Convolutional Neural Network and the Effect of Sensor Placement - Jeremy Kulchyk, Ali Etemad - Shows the importance of the sensor placement.
- Audio Classification with Machine Learning (EuroPython 2019)
- Deep Learning for Speech Recognition - Adam Coates, Baidu
- Convolution in the time domain - Mike X Cohen
- Convolution in the frequency domain - Mike X Cohen
- How to inspect time-frequency results - Mike X Cohen