Wearable Data Labeling via Automated Event Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for labeling data collected from individuals, such as physiological data, are often manual, prone to errors, and lack scalability, leading to inaccuracies and missed data points.
Innovation Solution
A method and system for generating a dataset of labelled data points using a device that records data, receives and assigns labels, and determines events, enabling accurate and scalable data collection through user interfaces and machine learning algorithms, including the use of micro electro-mechanical systems (MEMS) and encryption for secure transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data labeling methods are used, then implementation simplicity is maintained, but accuracy and reliability deteriorate due to human error and missed data points
Solution Approach 1:
The system enables self-service data labeling by automatically detecting events using sensor data and generating candidate labels without requiring manual intervention for every data point. The wearable device autonomously processes sensor inputs, identifies physiological events, and creates structured data points with timestamps and preliminary labels, significantly reducing human error while maintaining systematic complexity through automated algorithms.
2Productivity
If manual data labeling methods are used, then implementation simplicity is maintained, but productivity deteriorates due to limited scalability
Solution Approach 1:
The system replaces the mechanical manual labeling process with an automated electronic system. Sensors continuously collect physiological data, algorithms automatically detect events and generate labels, and the system structures data points with timestamps and categories without human intervention. This substitution dramatically increases data labeling throughput and scalability while managing complexity through integrated hardware-software architecture.
3Reliability
If automated event detection is implemented, then data labeling accuracy is improved, but ease of operation deteriorates due to additional system components
Solution Approach 1:
The system merges multiple functions into an integrated wearable device: sensors collect physiological data, processors detect events automatically, and the system generates and stores labeled data points all within a single device. This consolidation improves reliability by ensuring consistent automated operation while simplifying user interaction, as the device requires minimal manual input beyond initial setup and consent.
Data Source
AI summary
Systems, methods, and computer program products for generating a dataset of labelled physiological data points. Data corresponding to an individual is recorded. Input may be received indicating a label. A data point may be generated. The data point may include a timestamp when the input was received or the event is triggered, the label, and a portion of the data corresponding to the time when the input was received. The data point may be stored in a dataset of labelled data points. Labels might be predicted by the system using machine learning algorithm.