Automated Time Studies using Computer Vision and Large Language Models

Description:

This technology uses advanced machine learning algorithms and computer vision techniques to automatically analyze video recordings of work processes, segmenting frames to identify and classify human actions like general movement or tool usage. It employs predetermined time study methods such as Basic MOST to estimate task durations accurately, enabling real-time monitoring and scalable time study analysis without manual intervention.

Key Advantages:

• Fully automated time study process reducing manual labor.
• Real-time monitoring and analysis of work activities.
• Enhanced accuracy through precise action recognition and segmentation.
• Scalable solution adaptable across various industries.
• Standardization of time study methodologies using machine learning.

Problems Solved:

• Eliminates time-consuming and error-prone manual time study methods.
• Addresses challenges in feature extraction and action segmentation from complex video data.
• Reduces variability and subjectivity in traditional time measurement.
• Enables consistent and repeatable time analysis across diverse work environments.

Market Applications:

• Manufacturing and industrial process optimization.
• Workplace productivity and ergonomic studies.
• Construction and field service time management.
• Quality control and labor efficiency monitoring.
• Training and performance assessment automation.