Adaptive Sampling Smart Shoe for Battery Life
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Solution Overview
Problem
Existing patient monitoring systems are inefficient in tracking activity levels outside clinical settings, leading to incomplete data and reduced battery life due to constant high sampling rates, which are unnecessary during sedentary activities.
Innovation Solution
A smart shoe system equipped with pneumatic pressure sensors and a GPS module that employs an adaptive sampling algorithm to adjust sampling rates based on detected activities, such as walking or sitting, reducing data size and extending battery life by collecting data only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant high sampling rates are used for monitoring patient activity, then monitoring precision is improved, but data size increases and battery life decreases
Solution Approach 1:
The system dynamically adjusts the sampling rate based on detected activity levels. During sedentary periods, the sampling rate is reduced to conserve battery power, while during active rehabilitation exercises, the sampling rate increases to capture precise movement data. This dynamic adaptation resolves the contradiction between maintaining high monitoring precision and extending battery life.
Solution Approach 2:
The system changes the sampling rate parameter according to activity state. By detecting transitions between sedentary and active states, the system modifies the data acquisition frequency parameter to match current needs, thereby reducing unnecessary data collection during low-activity periods while maintaining precision during high-activity periods.
2Measurement precision
If constant high sampling rates are used for monitoring patient activity, then monitoring precision is improved, but data storage requirements increase
Solution Approach 1:
The system dynamically adjusts the sampling rate based on detected activity levels. During sedentary periods, the sampling rate is reduced to conserve battery power, while during active rehabilitation exercises, the sampling rate increases to capture precise movement data. This dynamic adaptation resolves the contradiction between maintaining high monitoring precision and extending battery life.
Solution Approach 2:
The system changes the sampling rate parameter according to activity state. By detecting transitions between sedentary and active states, the system modifies the data acquisition frequency parameter to match current needs, thereby reducing unnecessary data collection during low-activity periods while maintaining precision during high-activity periods.
3Measurement precision
If constant high sampling rates are used for monitoring patient activity, then monitoring precision is improved, but communication requirements increase
Solution Approach 1:
The system dynamically adjusts the sampling rate based on detected activity levels. During sedentary periods, the sampling rate is reduced to conserve battery power, while during active rehabilitation exercises, the sampling rate increases to capture precise movement data. This dynamic adaptation resolves the contradiction between maintaining high monitoring precision and extending battery life.
Solution Approach 2:
The system changes the sampling rate parameter according to activity state. By detecting transitions between sedentary and active states, the system modifies the data acquisition frequency parameter to match current needs, thereby reducing unnecessary data collection during low-activity periods while maintaining precision during high-activity periods.
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
The adaptive sampling algorithm achieves a 95% reduction in data size while maintaining monitoring fidelity, leading to improved data efficiency, extended battery life, and reduced storage and communication requirements, enabling effective daily health monitoring.
Implementation Method 1
a shoe having a plurality of pneumatic pressure sensors. The pressure sensors may be configured to detect pressure at a plurality of points in the sole of the shoe
Implementation Method 2
a GPS integrated circuit. The GPS integrated circuit may be for correlating position of the smart shoe system to activity data generated by the plurality of pressure sensors
Data Source
AI summary
A smart shoe, smart shoe system, and a method of a smart shoe are disclosed. A smart shoe system may be used for monitoring patient activity. The smart shoe system may include a shoe having a plurality of pneumatic pressure sensors. The pressure sensors may be configured to detect pressure at a plurality of points in the sole of the shoe. The smart shoe may also include a microprocessor coupled to the pressure sensors and a GPS integrated circuit. The GPS integrated circuit may be used for correlating position of the smart shoe system to activity data generated by the plurality of pressure sensors. Additionally, the smart shoe system may include a flash memory storage for storing data generated by the microprocessor and pressure sensors.