Impact Detection Device Using Dynamic Sensor Sampling Modes
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Solution Overview
Problem
Existing impact detection systems face challenges with battery life and user activation requirements, particularly in applications where continuous monitoring is impractical due to limited power availability and the need for frequent battery changes or recharging.
Innovation Solution
The system employs a sealed apparatus with a Bluetooth Low Energy (BLE) System on Chip, incorporating ultra-low power sensors and a protective package that switches between active, sleep, and deep sleep modes based on user activity, allowing for low-power impact detection without user intervention and extending battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the device continuously monitors impact using sensors, then impact detection reliability is improved, but battery life deteriorates
Solution Approach 1:
The system dynamically adjusts sensor operation modes based on detected activity. The processor transitions between active mode (continuous monitoring at high sampling rate), sleep mode (reduced monitoring), and deep sleep mode (minimal monitoring), allowing the device to maintain reliability during actual use while conserving battery life during idle periods
Solution Approach 2:
The system implements periodic sampling of sensor data at different rates depending on the operational state. During active mode, sensors sample at a first sampling rate; during sleep mode, at a second sampling rate; and during deep sleep mode, at a third sampling rate. This periodic action with variable rates maintains detection capability while managing power consumption
2Measurement precision
If the device operates in active mode continuously, then measurement precision is improved, but use of energy deteriorates
Solution Approach 1:
The sampling rate is dynamically adjusted based on the operational mode. During active mode, the system uses a higher first sampling rate for precise motion data collection. During sleep and deep sleep modes, it transitions to lower second and third sampling rates respectively, reducing energy consumption while maintaining adequate monitoring capability
Solution Approach 2:
The system changes the sampling rate parameter according to operational needs. By switching between first sampling rate (active), second sampling rate (sleep), and third sampling rate (deep sleep), the system optimizes the balance between measurement precision and energy consumption based on current operational state
3Ease of operation
If the device requires user activation for each activity, then ease of operation deteriorates, but extent of automation improves
Solution Approach 1:
The device automatically detects user presence and activity state through sensor data analysis, and self-adjusts its operational mode without requiring user intervention. The processor monitors sensor inputs to determine whether the user is wearing the device and automatically transitions between active, sleep, and deep sleep modes, making the system self-regulating
4Reliability
If the device uses multiple sensors for comprehensive monitoring, then reliability is improved, but device complexity deteriorates
Solution Approach 1:
The system dynamically manages multiple sensors by transitioning them between different operational states. The processor activates sensors as needed based on detected conditions, turning sensors on during active mode and putting them into lower power states during sleep and deep sleep modes, thereby managing complexity through dynamic control
Data Source
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AI summary
Measures, including apparatus, methods, circuitry and computer program products for use in impact detection. Example apparatus comprises a plurality of sensors. The apparatus is configured to determine whether a user is wearing an item in which the apparatus is comprised, and in response to a positive determination, operate the apparatus in an active operating mode. The active operating mode comprises operating one or more sensors in the plurality to generate data associated with motion of the user at a first sampling rate.