Wearable Injury Prediction Using Machine Learning
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Solution Overview
Problem
Current injury prediction technologies lack devices capable of collecting data from individuals before, during, and after an injury, and face challenges in addressing the complexity of injury prediction due to multiple measurable and non-measurable factors involved.
Innovation Solution
A wearable device equipped with multiple sensors that collects motion data and utilizes machine learning techniques to predict injuries by processing raw sensor data into feature values, which are then input into a trained machine learning model to identify the likelihood of injury and provide actionable insights to prevent it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to collect comprehensive data for injury prediction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the injury prediction task into multiple independent sensor modules (accelerometer, gyroscope, magnetometer, barometer, GPS, camera) that can be independently selected and configured. Each sensor captures specific aspects of user activity, and the machine learning model processes these segmented data streams separately before integration, allowing comprehensive monitoring while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The wearable device employs a universal sensor platform that can perform multiple functions: injury prediction, activity tracking, fall detection, and location monitoring. The same sensor array serves both specialized injury prediction tasks and general fitness tracking, reducing the need for separate dedicated systems and thereby managing complexity while achieving high measurement precision through multi-functional data collection.
2Reliability
If comprehensive sensor data is collected for injury prediction, then reliability of prediction is improved, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary action by continuously collecting and pre-processing sensor data in real-time during user activities, rather than waiting to accumulate large datasets before analysis. The machine learning model is pre-trained on comprehensive injury data, enabling it to process incoming sensor streams efficiently and generate injury predictions with high reliability without significant processing delays.
Solution Approach 2:
The patent replaces traditional mechanical analysis methods with machine learning-based processing. Instead of using complex algorithmic rules to analyze sensor data sequentially, a trained neural network model processes multiple sensor inputs simultaneously, significantly reducing computation time while maintaining or improving prediction reliability through pattern recognition capabilities.
3Measurement precision
If machine learning model processes all sensor data, then injury prediction accuracy is improved, but use of energy increases
Solution Approach 1:
The system extracts only the most relevant features from comprehensive sensor data before feeding them to the machine learning model. Instead of processing all raw sensor inputs, the system identifies and extracts key motion patterns, impact forces, and activity characteristics that are most predictive of injury risk, thereby maintaining high prediction accuracy while significantly reducing the computational energy required for processing.
Solution Approach 2:
The machine learning model is designed to process a partial subset of sensor data that contains the most predictive information for injury detection. Rather than exhaustively analyzing every sensor reading, the system focuses on critical moments and key parameters identified during training, achieving sufficient prediction accuracy with reduced energy consumption by avoiding unnecessary processing of redundant data.
Data Source
AI summary
Systems and methods of the present disclosure enable injury prediction using one or more processors for receiving a time-varying signal of sensor measurements from a sensor device associated with a user. The processor(s) generate time windows of the time-varying signal, including a series of the sensor measurements across a predetermined time period, and generate motion features based at least in part on the series of the sensor measurements of the time windows. The processor(s) utilize an injury risk classification machine learning model to predict an injury risk during each time window based at least in part on the motion features. An injury alert message is generated based at least in part on the injury risk being predicted; and transmitting the injury alert message to at least one user computing device.