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Related Concept Videos

Hückel's Rule Diagram of π MOs: Frost Circle01:08

Hückel's Rule Diagram of π MOs: Frost Circle

The Frost circle or the inscribed polygon method is a graphical method for determining the relative energies of π molecular orbitals (MOs) for planar, fully conjugated, and monocyclic compounds. This method was first described by A. A. Frost and Boris Musulin in 1953.
A Frost circle is constructed by drawing a polygon whose number of edges is equal to the number of carbons of the given cyclic system, with one of the vertices pointing down. Then, a circle is drawn enclosing the polygon so that...

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Related Experiment Video

Updated: Jun 20, 2026

The ITS2 Database
16:17

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Published on: March 12, 2012

Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models.

Christine F Martindale1, Florian Hoenig2, Christina Strohrmann3

  • 1Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany. christine.f.martindale@fau.de.

Sensors (Basel, Switzerland)
|October 14, 2017
PubMed
Summary

This study introduces a smart annotation method using semi-supervised learning to reduce labeling costs for cyclic sensor data. The hierarchical hidden Markov model (hHMM) accurately analyzes human motion and heart activity, enabling efficient data analysis.

Keywords:
activity recognitionannotation costcyclic sensor datagait classificationhierarchical hidden Markov modelsinertial sensorssegmentationsemi-supervised learningsmart annotationwearable sensors

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Biomedical Engineering

Background:

  • Cyclic signals (e.g., human motion, heart activity) are crucial for clinical and sports applications.
  • High annotation costs and limited data hinder algorithm development for analyzing these signals.
  • Current methods often require strict laboratory conditions or extensive manual segmentation.

Purpose of the Study:

  • To present a smart annotation method that significantly reduces labeling costs for sensor-based cyclic data.
  • To enable analysis of data collected outside of laboratory settings ('in the wild').
  • To improve the efficiency and accuracy of analyzing cyclic data for various applications.

Main Methods:

  • Utilized semi-supervised learning on sections of cyclic data with known cycle numbers.
  • Employed a hierarchical hidden Markov model (hHMM) for data analysis.
  • Applied the trained hHMM to simultaneously segment and classify continuous 'in the wild' data.

Main Results:

  • Achieved a mean absolute error of 0.041 ± 0.020 s compared to manual annotations.
  • Demonstrated successful segmentation and classification of continuous, real-world data.
  • Obtained results comparable to fully-supervised methods with reduced annotation effort.

Conclusions:

  • The semi-supervised hHMM approach offers a significant reduction in annotation cost and human error.
  • This method is effective for analyzing cyclic data in real-world conditions, applicable to continuous monitoring.
  • Enables more accessible and cost-effective analysis for applications like gait analysis and movement disorder monitoring.