Machine Learning Action Quality Scoring From User Movement Sensors
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Solution Overview
Problem
Healthcare providers lack effective systems for continuously assessing and improving the quality of actions performed in assisting patients, as they often receive limited oversight after initial training.
Innovation Solution
A system utilizing machine learning models to analyze sensor data from various sources, including wearable devices and environmental sensors, to evaluate action quality, patient outcomes, and user injury risk, and initiate interventions when criteria are not met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If healthcare providers receive initial training and instruction, then they can perform assistance actions, but they do not receive further instruction or oversight when performing the action subsequently
Solution Approach 1:
The system continuously monitors healthcare provider actions using sensors and provides real-time feedback through the machine learning model, which evaluates action quality and triggers interventions when criteria are not met. This closed-loop feedback mechanism ensures consistent action quality without requiring complex manual oversight systems.
Solution Approach 2:
The healthcare provider performs self-assessment through the system by simply performing the assistance action while being monitored. The machine learning model automatically evaluates their performance and provides guidance, allowing the provider to self-improve without requiring external supervision or complex oversight infrastructure.
2Reliability
If a system continuously monitors and evaluates action quality, then action performance improves, but sensor data processing requirements increase
Solution Approach 1:
The machine learning model is pre-trained with comprehensive knowledge of proper assistance actions and quality criteria. This preliminary training allows the model to efficiently evaluate new sensor data without requiring extensive real-time computation, reducing energy consumption while maintaining high monitoring accuracy.
Solution Approach 2:
The system dynamically adjusts monitoring intensity and processing resources based on risk parameters. When the machine learning model identifies actions that meet quality criteria, the system reduces monitoring intensity and processing energy. When quality thresholds are approached or interventions are triggered, processing resources are increased, optimizing the balance between action quality and energy consumption.
Data Source
AI summary
Techniques for improved machine learning are provided. Sensor data collected by a set of sensors is accessed, the sensor data indicating movement of a user in a physical environment. An action that the user was performing when the sensor data was collected is determined, where the user was performing the action to assist a patient. A quality score for performance of the action, by the first user, is generated based on processing the sensor data using a trained machine learning model. In response to determining that the quality score does not satisfy one or more criteria, one or more interventions are initiated for the user.