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ML4QS is a complete wrist sensor data pipeline project that uses Phyphox to collect accelerometer and gyroscope data, and identifies five daily activities (smoking, typing, idling, cooking, and exercising) via machine learning classifiers.
Section 01
ML4QS is a complete wrist sensor data pipeline project that uses Phyphox to collect accelerometer and gyroscope data, and identifies five daily activities (smoking, typing, idling, cooking, and exercising) via machine learning classifiers.
Section 02
Section 03
The Quantified Self movement advocates using data tracking to understand personal behavior and health status. The ML4QS project explores a core question: Can we infer a person's current activity solely from wrist movement data?
This project uses a wrist-worn smartphone as the sensor platform, collects accelerometer, gyroscope, and linear accelerometer data via the Phyphox app, and builds a complete machine learning pipeline from data collection to activity classification.
Section 04
The project selected five representative daily activities, covering the behavioral spectrum from rest to intense exercise:
Smoking
Characterized by repeated hand-to-mouth movements, showing slow, periodic arc motions. This activity has a unique movement pattern that is distinctly different from typical hand activities.
Typing + Mouse Operation
Represents the desktop office scenario, characterized by fast, low-amplitude wrist vibrations mainly from keyboard typing and mouse movements.
Idle
A state of sitting and resting, with hands not participating in any activity, and the signal is close to the zero baseline. Used as a control group to evaluate the system's noise level.
Cooking
Includes actions like stirring, chopping vegetables, and fetching items, showing free-form arm movements with high variance. This activity has strong randomness, posing a challenge to the classifier.
Exercising
Brisk walking or jogging, characterized by strong periodic whole-body movements. The signal has large amplitude and obvious frequency characteristics.
Section 05
This design ensures data diversity and statistical significance, while enhancing the model's generalization ability through multi-participant collection.
Section 06
The project uses three types of sensors to fully capture the wrist's movement state:
Accelerometer
Measures linear motion and gravity components, providing information about the device's position and movement in space. The data includes three axes: acc_x, acc_y, acc_z.
Gyroscope
Measures rotational angular velocity, capturing the wrist's turning movements. The data includes three axes: gyr_x, gyr_y, gyr_z.
Linear Accelerometer
Measures pure motion acceleration after removing gravity, used to distinguish between static tilt caused by gravity and actual movement. The data includes three axes: lin_x, lin_y, lin_z.
Section 07
To convert continuous time-series data into a format suitable for machine learning processing, the project uses a sliding window method:
The use of overlapping windows increases the number of training samples while maintaining temporal continuity.
Section 08
The project extracts rich time-domain and frequency-domain features from each window to fully characterize the statistical properties of movement:
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