Smartphone Sensor Data Collection for Personal Activity Scorecards
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Solution Overview
Problem
Current smartphones lack an efficient method to automatically collect and analyze user activity data from various sensors to provide personalized insights and comparisons, limiting their ability to promote lifestyle adjustments and social interactions based on activity levels.
Innovation Solution
A processor-based personal electronic device is programmed to collect data from onboard sensors and remote sensors, generating an activity 'scorecard' that allows users to adjust their behavior, compare activity levels with groups, and retrieve data from remote servers for statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If smartphones collect and analyze user activity data from various sensors, then personalized insights and lifestyle monitoring capabilities are improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The patent introduces a server as an intermediary to handle complex data processing, statistical analysis, and scorecard generation. The smartphone collects sensor data and transmits it to the server, which performs the computationally intensive tasks of analyzing activity patterns, comparing user data with group statistics, and generating personalized insights. This mediator approach allows the smartphone to provide personalized capabilities without bearing the full complexity burden locally.
Solution Approach 2:
The system segments the data processing functionality between the smartphone and the server. The smartphone handles data collection from sensors and basic transmission, while the server handles advanced analytics, statistical comparisons, and insight generation. This segmentation distributes the complexity across multiple components, allowing each to specialize in specific tasks without overwhelming a single device.
2Measurement precision
If smartphones automatically collect data from onboard and remote sensors, then user activity characterization is improved, but energy consumption worsens
Solution Approach 1:
The system employs periodic data collection and transmission rather than continuous operation. The smartphone collects sensor data over time periods and transmits batches to the server at intervals, rather than maintaining constant communication and processing. This periodic approach allows accurate activity characterization through accumulated data while significantly reducing energy consumption compared to continuous monitoring and transmission.
3Ease of operation
If smartphones provide detailed activity scorecards and group comparisons, then user engagement and lifestyle adjustment capabilities are improved, but information processing requirements worsen
Solution Approach 1:
The system implements feedback mechanisms where the server analyzes collected data and returns personalized scorecards, activity trends, and group comparison results to the smartphone for user display. This feedback loop provides users with actionable insights and recommendations based on their activity patterns, making lifestyle monitoring easy to operate while distributing the information processing burden to the server that handles the heavy analytical workload.
Data Source
AI summary
A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. A wireless communication link may be used by the device to obtain data from remote sensors which may be worn by the user. A personal “scorecard” may be generated from the raw data and from data concerning other users. Personal, raw characterization data may be computed into personal statistical data by averaging over time. Then, it may be sent (anonymously) to a server that receives such data from many (or all) users. The server may return personal statistical positioning to enable comparison of the user to other participants. In certain embodiments, the generation of a personal scorecard from the personal position in the group statistics may be performed in the user's device.