Portable Device False Wake Detection via Double Tap Motion Analysis
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Solution Overview
Problem
Existing electronic devices face challenges in accurately distinguishing between user input and false wake conditions, such as those caused by being in a pocket or car cradle, due to the inability of accelerometers to differentiate between finger taps and periodic motions from rough roads.
Innovation Solution
A portable electronic device that collects and analyzes motion data following a double tap event, using a sensor circuit with a motion sensor and sensor hub to determine if the motion data meets specific criteria, thereby distinguishing between user input and non-user input conditions, and delaying the wake-up of the display accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If an accelerometer is used to detect double tap for waking up the device, then the device can be activated by user input, but false wake conditions occur when the device is in pocket or car cradle due to periodic motion from rough roads
Solution Approach 1:
The detection process is segmented into multiple independent analysis stages: initial double tap detection, followed by separate analysis of motion patterns, periodicity detection, and amplitude verification. Each stage independently evaluates specific characteristics to collectively distinguish genuine user input from false wake conditions caused by periodic road motion.
Solution Approach 2:
The system dynamically adjusts its detection criteria based on real-time sensor data characteristics. It adapts by analyzing the temporal patterns, frequency content, and amplitude profiles of detected motions, enabling the device to dynamically differentiate between the sharp, impulsive nature of finger taps and the periodic, rhythmic patterns of road-induced motion.
2Measurement precision
If the accelerometer detects motion in certain conditions, then the device wakes up, but it cannot distinguish between finger tap and periodic motion generated from a rough road
Solution Approach 1:
The detection system extends analysis beyond simple acceleration magnitude by incorporating temporal dimension (time-based patterns), frequency dimension (periodicity analysis), and amplitude dimension (strength of motion). This multi-dimensional approach enables precise differentiation between finger taps and road motion without requiring overly complex algorithms.
Solution Approach 2:
The system implements feedback mechanisms where the results of initial motion detection trigger subsequent verification analyses. The output from each detection stage feeds into the next stage, creating a feedback loop that progressively refines the classification of detected motion, improving measurement precision while managing complexity through staged processing.
Data Source
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AI summary
There is described a portable electronic device (101) capable of detecting false wake conditions, and a method thereof. The portable electronic device (101) comprises a sensor circuit (219) and a display (207). The sensor circuit (219) detects (403) double tap data associated with user input and motion data subsequent to the double tap data within a predetermined time period. The sensor circuit (219) also determines (409) whether the motion data corresponds to at least one criterion associated with non-user input. The display (207) wakes (411) from a sleep state in response to the sensor circuit determining that the motion data corresponds to the at least one criterion. For some embodiments, the sensor circuit (219) includes a motion sensor (221) to detect the motion data and a sensor hub (223) to determine whether the motion data corresponds to the at least one criterion.