Mobile Device State Transition via Sensor Fusion and Neural Networks
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Solution Overview
Problem
Mobile devices require multiple actions to transition from power-saving to normal operation states, which can be inconvenient for users, especially when they need to quickly access the device after it has been stored away.
Innovation Solution
Incorporating various sensors such as touch, accelerometer, and light sensors to detect user intentions, with the aid of neural networks to analyze sensor data and automatically adjust the device's state, such as powering up and unlocking the screen, based on user interactions and environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the mobile device enters power-saving state to decrease power-consumption level and prolong battery life, then energy efficiency is improved, but the time and effort required for users to access the device increases
Solution Approach 1:
The system performs preliminary actions by detecting user approach (via sensors like proximity or touch) before the user actually interacts with the device. When a user approaches, the system pre-wakes the processor and displays in advance, so that by the time the user touches the device, it is already in the process of waking up or fully awake, eliminating the need for the user to wait for the device to respond after they interact with it.
Solution Approach 2:
The system uses feedback from sensors (touch, accelerometer, light, proximity) to continuously monitor user interaction and environmental conditions. This feedback loop allows the device to dynamically adjust its power state based on real-time detection, transitioning smoothly between power-saving and active states without requiring manual user intervention or creating delays.
2Reliability
If the mobile device requires multiple actions to transition from power-saving to normal operation states, then security and stability are improved, but the ease of operation deteriorates
Solution Approach 1:
The device performs self-service by automatically detecting user presence and intention through multiple sensors and autonomously transitioning from power-saving to active state without requiring explicit user commands. The system monitors touch, accelerometer, light, and proximity sensors to self-determine when to wake and unlock, eliminating the need for users to manually press buttons or enter codes multiple times.
Solution Approach 2:
The system merges multiple sensor functions (touch, accelerometer, light, proximity) into a unified detection mechanism that works together to determine user presence and intent. By combining the outputs of these sensors, the device can more accurately distinguish between legitimate user access attempts and other conditions, enabling secure automatic waking without requiring multiple separate user actions.
Data Source
AI summary
In one embodiment, a method includes receiving data associated with multiple first client devices, the data corresponding to a first set of sensor values and physical-state indicators of the first client devices; receiving data associated with a second client device corresponding to a second set of sensor values, the second client device including a first and a second set of sensors; generating functions that each determine a probability that the second client device is in a particular physical state, wherein the determination includes correlating the second set of sensor values with a corresponding physical state of the second client device based on the data associated with the multiple first client devices, and wherein a first function is based on values of the first set of sensors and a second function is based on values of the second set of sensors; and sending the generated functions to the second client device.