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Progress Estimation and Phase Detection for Sequential Processes.

Xinyu Li1, Yanyi Zhang2, Jianyu Zhang3

  • 1Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey; x1264@rutgers.edu.

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Summary
This summary is machine-generated.

This study introduces a real-time, sensor-based system for estimating work process progress using multimodal deep learning. The system accurately predicts process phases and remaining time for complex tasks like medical resuscitation and sports events.

Keywords:
Activity RecognitionConvolutional Neural NetworkDeep LearningLSTMMultimodalSensor Network

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Area of Science:

  • Computer Science
  • Engineering
  • Human-Computer Interaction

Background:

  • Advanced human-computer interfaces and automation rely on process modeling and understanding.
  • Current research predominantly focuses on activity recognition, with limited work on sensor-based process progress detection.

Purpose of the Study:

  • To introduce a real-time, sensor-based system for modeling, recognizing, and estimating work process progress.
  • To enable accurate prediction of process phases and remaining time for sequential processes.

Main Methods:

  • Implemented a multimodal deep learning architecture for spatio-temporal feature extraction from diverse sensory inputs.
  • Utilized a novel deep regression structure for overall completeness estimation.
  • Introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss for system training.

Main Results:

  • Achieved over 86% phase detection accuracy and an F1-score of 0.67 in trauma resuscitation.
  • Demonstrated a completeness estimation error under 12.6% and remaining-time estimation error less than 7.5 minutes for medical processes.
  • Attained 88% accuracy and an F1-score of 0.58 in Olympic swimming, with low estimation errors for completeness (6.3%) and remaining time (2.9 minutes).

Conclusions:

  • The developed system effectively models, recognizes, and estimates progress in complex real-world processes.
  • Sensor-based progress estimation offers significant improvements over existing methods for applications in healthcare and sports analytics.