Foot Thermoregulation Device Using Machine Learning Control
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Solution Overview
Problem
Current technologies fail to effectively enhance physiological thermoregulation, leading to performance degradation and heat stress resilience issues due to inadequate temperature regulation, particularly affecting the nervous system.
Innovation Solution
A wearable device with a thermoelectric cooler and sensors that use machine learning to regulate foot temperature based on biometric and exercise data, optimizing heat transfer and thermoregulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If a thermoelectric cooler is used to regulate foot temperature, then temperature control capability is improved, but device complexity increases
Solution Approach 1:
The apparatus is divided into distinct functional modules: a cooling element comprising a thermoelectric cooler for active temperature regulation, and a control element with processors and sensors for monitoring and control. This segmentation allows each module to perform its specific function efficiently while maintaining overall system manageability despite the increased complexity.
2Reliability
If machine learning models are implemented for real-time temperature regulation, then thermoregulation effectiveness is improved, but computational requirements and energy consumption increase
Solution Approach 1:
The system uses sensors to continuously monitor biometric data and exercise parameters in advance, feeding this information to machine learning models that predict future temperature trends. This preliminary action allows the thermoelectric cooler to be activated proactively before critical temperature thresholds are reached, improving thermoregulation effectiveness while avoiding unnecessary continuous operation that would increase energy consumption.
Solution Approach 2:
The apparatus implements a closed-loop feedback system where sensors continuously monitor foot temperature and biometric data, the processor analyzes this information using machine learning models, and the cooling element adjusts its operation accordingly. This feedback mechanism ensures optimal thermoregulation effectiveness by maintaining temperature within target ranges while minimizing energy consumption through precise, demand-based cooling rather than continuous operation.
3Measurement precision
If multiple sensors are used to monitor biometric and exercise data, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The control element is designed as a multi-functional integrated system that handles multiple sensor inputs (temperature sensors, biometric sensors, exercise datum sensors), processes data through machine learning models, controls the thermoelectric cooler, and provides user feedback. This universal control element consolidates multiple functions into a single integrated module, improving measurement precision through comprehensive sensing while managing device complexity through functional integration rather than separate discrete components for each function.
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 solution enhances physiological thermoregulation by improving heat transfer and reducing heat stress, thereby increasing performance and resilience through targeted temperature control.
Implementation Method 1
a cooling element comprising a thermoelectric cooler, wherein the cooling element is configured to regulate a temperature of the foot of the user
Data Source
AI summary
An apparatus and method for enhancing physiological thermoregulation, the apparatus including a housing configured to contact a foot of a user, wherein the housing includes a cooling element; and a plurality of sensors configured to generate a biometric datum; at least a processor communicatively connected to the plurality of sensors; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive the biometric datum from the plurality of sensors; and control the cooling element as a function of the biometric datum.