Mobile Device Somatic Response Measurement via Motion Sensors
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Solution Overview
Problem
Current methods for measuring human body responses to stimuli, such as galvanic skin response systems, are complex, cumbersome, and difficult to use in mobile or space-constrained environments, necessitating a more efficient and user-friendly solution.
Innovation Solution
A mobile computing device equipped with motion sensors and a processor that performs baseline calibration and classification processes using a k-means clustering algorithm to measure somatic responses through a simple triple whip gesture, allowing for precise and automated detection of psychological and physiological states without the need for large devices or dedicated power sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If galvanic skin response systems are used to measure somatic responses, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex galvanic skin response measurement systems with a mechanical motion sensing approach. The mobile computing device uses motion sensors to detect physical movements (such as eye blinking, head nodding, or finger tapping) that occur in response to stimuli, substituting the need for complex electrical measurement systems with simpler mechanical motion detection.
Solution Approach 2:
The patent captures and analyzes copies of natural physical movements rather than directly measuring physiological electrical signals. By recording motion sensor data from devices like smartphones or tablets that detect the user's physical responses, the system creates a simplified measurement model that replicates the information obtained from complex galvanic skin response systems.
2Measurement precision
If galvanic skin response systems are used to measure somatic responses, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically processes motion sensor data through baseline calibration and classification algorithms without requiring manual intervention. The mobile computing device autonomously compares detected movements against baseline data to determine somatic responses, eliminating the need for operators to manually analyze complex sensor signals or calibrate equipment.
Solution Approach 2:
The patent replaces manual operation of complex measurement equipment with automated motion detection and analysis. The system uses machine learning algorithms to automatically classify movements and determine psychological or physiological states, making the measurement process as simple as having the user perform natural gestures.
3Measurement precision
If galvanic skin response systems are used to measure somatic responses, then measurement precision is improved, but portability deteriorates
Solution Approach 1:
The patent utilizes mobile computing devices that serve multiple functions - smartphones, tablets, or wearable devices that users already carry for communication and computing purposes. These universal devices incorporate motion sensors as part of their standard functionality, eliminating the need for dedicated measurement equipment and enabling portability while maintaining measurement capabilities.
4Measurement precision
If galvanic skin response systems are used to measure somatic responses, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs baseline calibration during initial setup, storing reference data about the user's typical movement patterns. This preliminary action enables rapid classification of subsequent responses without requiring repeated calibration procedures, significantly reducing the time needed for ongoing measurements while maintaining precision.
Data Source
AI summary
A mobile computing device for measuring somatic response of a user to stimulus includes motion sensors, a volatile memory, and a processor for: executing a baseline calibration process including receiving first and second supervised data from the user, and first and second sensor data from the motion sensors, while the user performs a triple whip gesture, calculating signal strength of the first and second sensor data using a k-means clustering algorithm, and executing a classification process including reading third unsupervised data from the user and third sensor data from the motion sensors while the user performs the triple whip gesture.