Sensor Tags for Personal Activity Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems fail to effectively monitor and enhance personal activities such as exercise and diet, as they lack comprehensive recognition, recording, and analysis capabilities, leading to inadequate adherence to health recommendations and insufficient compliance with medical regimens.
Innovation Solution
A system utilizing tag- and thermal data-assisted object recognition, combined with location-specific information sharing, and environmental beacons to track and analyze personal activities, providing enhanced data gathering and analysis within a variably defined personal activity space, including positional, movement, and acceleration data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive sensor systems are implemented to monitor personal activities, then activity recognition precision and health monitoring reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (accelerometers, gyroscopes, magnetometers, barometers, GPS receivers, cameras, microphones) into an integrated sensor system that functions as a unified monitoring device. This merging approach enables comprehensive activity recognition while managing system complexity through integrated architecture rather than separate devices.
Solution Approach 2:
The sensor system is designed to perform multiple functions simultaneously: detecting motion, determining location, capturing audio-visual data, and monitoring physiological parameters. This multi-functionality allows a single system to address various health monitoring needs without requiring separate specialized devices for each function.
2Reliability
If continuous monitoring of personal activities is implemented, then health regimen compliance is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of sensor data rather than continuous monitoring at maximum resolution. Sensors are activated at intervals or triggered by specific events, allowing the system to maintain reliable compliance monitoring while reducing overall energy consumption through duty-cycled operation.
Solution Approach 2:
The sensor system automatically processes and analyzes data locally using onboard processors and algorithms, enabling self-service operation without requiring constant external processing. This reduces the energy burden of data transmission and external computation, allowing continuous monitoring with optimized power usage.
3Loss of information
If detailed activity data is collected and analyzed, then health insights and adherence monitoring are improved, but data processing complexity and time increase
Solution Approach 1:
The system performs preliminary processing of sensor data onboard, including filtering, feature extraction, and initial pattern recognition, before data leaves the device. This preliminary action reduces the volume and complexity of data requiring further processing, maintaining information completeness while reducing overall processing time.
Solution Approach 2:
The system implements feedback loops where processed activity data is immediately used to adjust monitoring parameters, provide real-time feedback to users, and refine analysis algorithms. This feedback mechanism enables efficient use of processed information, reducing the need for redundant processing and minimizing time loss.
Data Source
AI summary
New activity recognition, recording, analysis and control techniques, systems and sensors are provided. In one embodiment, multiple sensory tags with unique identification and data transfer attributes, create positional, movement, orientation and acceleration data and supply it to a control system. The tags may be placed at location(s) on the user's body, clothing, personal effects, exercise equipment and other activity-relevant locations, to enhance activity recognition and mapping. The system may define a personal activity space, sample data preferentially from that space, and perform a simplified form of object-recognition to determine, record and analyze user activities.