Smartphone Stress Detection Using Touch and Motion Signals
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Solution Overview
Problem
Existing methods for determining stress levels on smartphones require additional sensors or wearables, limiting their usability and effectiveness in providing real-time feedback for stress reduction techniques.
Innovation Solution
A method utilizing a smartphone app that analyzes touch and motion data to determine stress levels through logistic regression, integrating touch feature values and motion feature values without requiring external sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors or wearables are used to measure stress parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The smartphone is made to serve multiple functions: it acts as both the user interface and the sensing device. The existing smartphone sensors (accelerometer, gyroscope, touchscreen controller) are repurposed to measure stress-related parameters such as touch pressure, touch duration, device movement, and orientation, eliminating the need for dedicated stress measurement sensors or wearables.
Solution Approach 2:
The system uses the smartphone's own built-in sensors to measure stress parameters. The accelerometer, gyroscope, and touchscreen controller that are already present in the smartphone are utilized to capture motion data and touch interaction data, making the system self-sufficient without requiring external sensing devices.
2Measurement precision
If multiple sensors are integrated to capture comprehensive stress data, then measurement precision improves, but ease of operation deteriorates
Solution Approach 1:
The smartphone performs multiple roles including serving as the user interface, motion sensor platform, and data processing unit. By utilizing the smartphone's existing capabilities for touch interaction and motion detection, the system eliminates the need for users to carry or set up additional sensing devices, thereby maintaining ease of operation while achieving comprehensive stress measurement.
3Measurement precision
If touch data and motion data are both analyzed, then stress level determination accuracy improves, but computational requirements increase
Solution Approach 1:
The system analyzes touch data and motion data at different levels of detail. Touch interaction data (pressure, duration, position) and motion sensor data (acceleration, orientation) are collected and processed to generate stress feature values. The regression model uses these features to compute stress scores, providing accurate stress determination while managing computational load by processing data in discrete time intervals and using normalized feature values.
Data Source
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AI summary
A method for determining a user's stress level is performed by a smartphone app. Touch and motion feature values are generated, the feature values are weighted by regression parameters, and a stress score is generated based on the weighted touch and motion feature values. The touch feature values indicate how the user's finger moves over the smartphone and are generated from touch data points including X positions, Y positions and associated touch timestamp values. The motion feature values indicate movement of the smartphone and are generated from motion data points including X movements, Y movements, Z movements and associated motion timestamp values. The regression parameters are generated using touch and motion data identified by other users as being acquired while those other users were experiencing various perceived levels of stress. The app indicates to the user whether the stress score is higher or lower than a previously generated stress score.