Movement Verification Through Sensor Fusion Against Device Manipulation
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Solution Overview
Problem
Existing exercise monitoring platforms face challenges in verifying genuine user movement due to potential manipulation of mobile devices, leading to decreased user trust and engagement, with existing solutions often draining device resources and failing to accurately distinguish between genuine and fraudulent activity.
Innovation Solution
A movement verification system utilizing a user mobile device with a sensor set, including an inertial measurement unit and GPS module, applies a movement verification function through a neural network trained on paired data sets to accurately verify user actions like steps or bicycle revolutions, while minimizing power consumption and incorporating a token generation system for secure transaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users are provided with rewards for exercise monitoring, then user engagement and motivation improve, but users may attempt to cheat by manipulating their devices to falsely register exercise activity
Solution Approach 1:
The system implements feedback by continuously monitoring multiple sensor parameters (accelerometer, gyroscope, GPS) and comparing them against expected exercise patterns. When anomalies are detected that suggest device manipulation rather than genuine exercise, the system adjusts rewards accordingly, creating a feedback loop that maintains data authenticity while preserving user engagement
Solution Approach 2:
The patent introduces an intermediary verification layer between the raw sensor data and the reward system. This intermediary analyzes sensor patterns, detects manipulation attempts, and determines genuine exercise activity before rewards are issued, thereby preventing cheating while maintaining user motivation
2Measurement precision
If complex verification algorithms are applied to detect genuine exercise, then accuracy in distinguishing fraudulent activity improves, but device battery power and computational resources are drained
Solution Approach 1:
The system applies partial verification by selecting and analyzing only the most critical sensor parameters and exercise patterns relevant to detecting manipulation. Rather than continuously processing all available data at maximum intensity, the system performs targeted analysis that achieves sufficient verification accuracy while consuming acceptable battery power
Solution Approach 2:
The verification process operates periodically rather than continuously, analyzing sensor data at intervals sufficient to detect exercise patterns and manipulation attempts while allowing the device to enter lower-power states between analysis cycles, thereby balancing accuracy with energy conservation
3Reliability
If multiple sensor parameters are monitored to verify exercise authenticity, then reliability of movement detection improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent merges multiple sensor inputs (accelerometer, gyroscope, GPS) into a unified exercise verification model. By combining these sensors and analyzing their correlated outputs together rather than separately, the system achieves enhanced reliability in detecting genuine exercise while managing processing requirements through integrated analysis
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively verifies user movement with high accuracy and reliability, reducing false positives and negatives, promoting user trust and engagement by ensuring fair reward distribution based on genuine exercise, while conserving device battery life.
Implementation Method 1
The sensor set may comprise at least one inertial measurement unit, such as an accelerometer or a gyroscope
Implementation Method 2
The sensor set may comprise at least one reference-based positioning module, such as a GPS module
Data Source
AI summary
Movement verification methods and systems are disclosed. These methods and systems are configured to determine user movement that is characterised by a sequence of repeated user actions—such as steps or bicycle crank revolutions. A user mobile device is positioned in proximity to a user so as to register the movement of that user. The user mobile device comprising a sensor set and is configured to generate from that sensor set an unverified set of movement data resulting from user movement. A movement verifier is in communication with the mobile user device. The movement verifier is configured to receive the unverified set of movement data from the user mobile device and apply a movement verification function that compares the unverified set of movement data against a model so as to verify user movement that is characterised by a sequence of repeated user actions, such as steps.