Smart Fabric Impact Mitigation and Injury Mapping
Solution Overview
Problem
Existing smart fabrics lack effective mechanisms to assess and respond to impacts, particularly in emergency situations where immediate medical attention is required.
Innovation Solution
The integration of pressure sensors in smart fabrics that trigger a stimulus based on a positional relationship with a single-track vehicle, generating a pressure map and an injury map using machine learning, to display criticality levels of injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pressure sensors are integrated into smart fabrics to detect impact, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines pressure sensors, machine learning models, and injury assessment algorithms into an integrated smart fabric system. The sensors are embedded within the fabric structure itself, merging detection, processing, and assessment functions into a unified wearable system that maintains flexibility while providing comprehensive impact monitoring.
Solution Approach 2:
The smart fabric system performs multiple functions: impact detection through pressure sensors, real-time data processing via machine learning models, injury risk assessment, and emergency response triggering. This multi-functional integration allows a single system to handle the entire impact assessment workflow from detection to emergency notification.
2Reliability
If machine learning models are used to generate injury maps, then reliability of injury assessment is improved, but computing power requirements increase
Solution Approach 1:
The machine learning models are pre-trained offline with extensive medical and impact data to establish injury prediction algorithms. During actual wear and impact events, the pre-trained models perform rapid inference using minimal computational resources, allowing accurate injury assessment without requiring high real-time processing power or excessive energy consumption.
3Loss of time
If stimulus is triggered based on positional relationship with single-track vehicle, then response time to emergency is improved, but measurement precision requirements increase
Solution Approach 1:
The system continuously monitors positional relationships with single-track vehicles and pre-positions emergency response protocols before accidents occur. When the vehicle approaches within critical distances or enters hazard zones, the system proactively triggers warnings and prepares emergency protocols, enabling rapid response without requiring ultra-precise real-time positioning during the actual emergency event.
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
Enhances user safety by mitigating impact through variable stiffness in smart fabrics, improves emergency response by providing critical injury information, and optimizes smart fabric utilization in impact scenarios.
Implementation Method 1
triggering a stimulus if a threshold is reached or exceeded based on a positional relationship between a body suit and a single-track vehicle, wherein the stimulus activates one or more pressure sensors of the body suit
Implementation Method 2
In response to certain stimuli, smart fabrics may be able to vary stiffness of the clothing. Varying stiffness of the clothing may be able to mitigate an impact of a fall on a user
Data Source
AI summary
A method, computer system, and a computer program product for impact response is provided. The present invention may include triggering a stimulus if a threshold is reached or exceeded based on a positional relationship between a body suit and a single-track vehicle, wherein the stimulus activates one or more pressure sensors of the body suit. The present invention may include generating a pressure map of the body suit utilizing the one or more pressure sensors. The present invention may include generating an injury map using a machine learning model, wherein the injury map is based on the pressure map and one or more input parameters, and wherein the input parameters are specific to a user of the body suit.