Force-Sensing Wake-Up Circuit for Low-Power Gesture Detection
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Solution Overview
Problem
Computing devices with low-cost sensors face power consumption issues due to constant monitoring and false alarms caused by sensor drift, leading to reduced battery life, as they frequently wake up to check for critical events.
Innovation Solution
Implementing an ultra-low power mode where an analog front-end autonomously evaluates sensor signals, waking a microcontroller only when a threshold level of confidence in an 'interesting' event is reached, and subsequently waking a high-power application processor for confirmed human gestures, thereby minimizing unnecessary wake-ups and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the low-power core regularly wakes to read sensor data at a periodic rate (e.g., 50 Hz), then the device can detect critical events, but power consumption increases and battery life diminishes
Solution Approach 1:
The system segments the monitoring task across three hierarchical levels: (1) analog front-end circuitry that continuously monitors sensor signals and generates interrupt requests only when thresholds are exceeded, (2) low-power microcontroller that evaluates interrupt causes and determines whether to wake the application processor, and (3) high-power application processor that handles confirmed gestures. This segmentation eliminates the need for periodic sampling by all processors, reducing power consumption while maintaining event detection reliability.
Solution Approach 2:
The patent introduces an intermediary evaluation layer between the sensor and the application processor. The low-power microcontroller acts as an intermediary that receives interrupt requests from the analog front-end, analyzes the interrupt cause using stored historical data, and selectively wakes the application processor only when a genuine gesture is detected. This intermediary prevents false alarms from unnecessarily activating high-power components.
2Ease of manufacture
If the device uses low-cost sensors, then manufacturing cost is reduced, but sensor drift causes false alarms requiring frequent supervision
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data in the analog front-end (filtering, threshold comparison) and pre-evaluating interrupt causes in the low-power microcontroller using historical data from a ring buffer. This preliminary processing identifies and eliminates false alarms caused by sensor drift before they can trigger expensive wake-up events, allowing low-cost sensors to achieve reliable performance.
Solution Approach 2:
The patent implements feedback mechanisms where the low-power microcontroller continuously monitors sensor data and compares current readings against historical patterns stored in the ring buffer. When drift causes threshold violations, the feedback analysis distinguishes between genuine gestures and drift-induced false alarms, adjusting the system's response accordingly. This feedback loop maintains reliability despite using low-cost drift-prone sensors.
3Reliability
If the microcontroller analyzes sensor data in detail to distinguish genuine gestures from false alarms, then false positives are reduced, but power consumption increases
Solution Approach 1:
The system applies partial action by having the low-power microcontroller perform only essential analysis functions: evaluating interrupt causes, comparing current readings with historical data, and making go/no-go decisions about waking the application processor. The microcontroller does not perform exhaustive gesture analysis; instead, it performs just enough analysis to filter obvious false alarms, leaving detailed processing to the application processor only when necessary. This partial action significantly reduces microcontroller power consumption while maintaining adequate filtering.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach extends battery life by reducing unnecessary wake-ups and power consumption, as the microcontroller and application processor are woken only when a human gesture is confidently detected, reducing false positives and accounting for sensor drift.
Implementation Method 1
an analog front-end, implemented in hardware and configured to convert an analog signal generated by a force sensor into a digital signal
Implementation Method 2
a differentiator (e.g., high-pass filter) is used to evaluate sensor signals in a time domain to provide an estimate of a rate of change of the signal
Data Source
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AI summary
Techniques and apparatuses are described that provide an ultra-low power mode for a low-cost force-sensing device. These techniques extend battery life of the device by minimizing power consumption for potential wake-up events. To do this, a high-pass filter (e.g., differentiator) is used to evaluate sensor signals in a time domain to provide an estimate of a rate of change of the signal. When the rate of change of the signal deviates from a baseline value by a threshold amount, then a microcontroller is woken to evaluate a large number of historical samples, such as 200 or more milliseconds worth of historical data. If a human gesture is not recognized, then the microcontroller returns to an idle state, but if a human gesture is recognized, then a high-power application processor is woken to execute an application configured to perform an operation mapped to the human gesture.