Accelerometer Profile Matching for App Activation
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Solution Overview
Problem
Existing mobile device technologies lack an efficient method to automatically activate applications based on user activities, relying on manual intervention or limited contextual data, which can lead to delayed or inappropriate application usage.
Innovation Solution
A system that utilizes real-time acceleration data from a mobile device's accelerometer to compare with stored acceleration profiles, automatically activating associated applications when a match is detected, allowing for seamless integration with user activities such as jogging, commuting, or working.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used to activate applications, then user control is maintained, but application activation is delayed and requires additional user effort
Solution Approach 1:
The system performs preliminary action by detecting user activities (jogging, driving, reading) through accelerometer data and proactively activating relevant applications before the user manually opens them. The accelerometer continuously monitors motion patterns and matches them against stored profiles to trigger automated application launch, eliminating the time delay between user intent and application activation.
2Measurement precision
If limited contextual data is used for application activation, then system complexity is reduced, but activation accuracy and appropriateness deteriorate
Solution Approach 1:
The system introduces an intermediary layer consisting of pre-stored acceleration profiles that serve as a reference database for comparing real-time accelerometer data. This intermediary structure enables accurate activity recognition by matching current motion patterns against known profiles without requiring complex machine learning models, thus achieving high measurement precision while maintaining relatively simple device architecture.
3Productivity
If automated application activation based on acceleration data is implemented, then responsiveness to user activities is improved, but system complexity increases
Solution Approach 1:
The system applies preliminary action by pre-storing acceleration profiles for various activities (jogging, driving, reading, etc.) before runtime. During operation, the accelerometer simply needs to compare current data against these pre-defined profiles and trigger corresponding applications, which dramatically improves activation efficiency while keeping the processing logic relatively simple and avoiding complex real-time analysis algorithms.
Data Source
Figure 1A~1B
Figure 1C~1D
Figure 1E~1F
AI summary
In some implementations, a computer-implemented method includes storing a plurality of acceleration profiles in a mobile device; receiving accelerometer data from an accelerometer in the mobile device; correlating the accelerometer data with one accelerometer profile in the plurality of accelerometer profiles; and activating a user application of the mobile device that is associated with the correlated accelerometer profile. Each acceleration profile can correspond to a sequence of acceleration forces a mobile device would be subjected to when carried with a user during an activity that corresponds to the correlated acceleration profile.