Automated Ergonomics Data Capture Using Multi-Sensor Action Recognition
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Solution Overview
Problem
Current methods for measuring human activities in factories are labor-intensive, outdated, and produce biased datasets, as they rely on manual techniques that may not accurately represent real work conditions.
Innovation Solution
A system that uses deep neural networks to non-intrusively digitize actions performed by humans and machines, enabling the creation of a robust and complete dataset that is not dependent on conscious observation, and allowing for real-time or post-facto analysis of ergonomics data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual techniques are used to gather data on human activities, then the data gathering process is simple and straightforward, but the dataset becomes small, incomplete, and fundamentally biased
Solution Approach 1:
The patent replaces manual observation and recording techniques with automated computer vision systems and machine learning algorithms. Sensors and cameras capture human activities automatically, eliminating the need for manual data collection while producing larger, more complete, and unbiased datasets that accurately represent real work conditions.
Solution Approach 2:
The patent introduces an intermediary automated measurement system that acts as a mediator between human activities and data collection. This intermediary system uses sensors, cameras, and algorithms to objectively capture and analyze human movements, postures, and behaviors without direct human intervention, thereby eliminating observer bias and improving data quality.
2Reliability
If automated sensing systems are deployed to continuously collect data, then the dataset becomes robust and complete, but the system complexity increases
Solution Approach 1:
The patent employs multi-functional sensor systems that can simultaneously capture various types of data (visual, depth, thermal, motion) using a single integrated platform. The automated measurement system performs multiple functions including activity recognition, posture analysis, and ergonomic assessment, reducing the need for separate specialized devices and simplifying system deployment.
Solution Approach 2:
The patent implements self-calibrating and self-configuring measurement systems that automatically adjust to different work environments and tasks. The system autonomously calibrates sensors, adapts to varying lighting conditions, and configures analysis parameters based on the specific application, thereby reducing the complexity of system setup and maintenance while ensuring reliable data collection.
Data Source
AI summary
The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for analyzing ergonomic data from the one or more sensor streams.