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A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

Alessandro Manzi1, Paolo Dario2, Filippo Cavallo3

  • 1The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56026 Pontedera (PI), Italy. alessandro.manzi@santannapisa.it.

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Summary

This study presents a human activity recognition system using skeleton data and machine learning. It achieves excellent performance with minimal data, outperforming state-of-the-art methods on key datasets.

Keywords:
RGB-D cameraSVM, SMOassisted livingclustering, x-meansdepth camerahuman activity recognitionskeleton data

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human Activity Recognition

Background:

  • Human activity recognition is crucial for applications like ambient assisted living.
  • Existing methods often require extensive data for accurate action classification.

Purpose of the Study:

  • To develop an efficient human activity recognition system using skeleton data.
  • To identify the minimum data required for effective action classification.
  • To leverage machine learning for classifying actions based on basic postures.

Main Methods:

  • Utilized skeleton data from depth cameras for activity recognition.
  • Employed multiclass Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) for training.
  • Applied X-means algorithm for dynamic cluster number determination.
  • Extracted posture-based features independently from activity instances.

Main Results:

  • Achieved excellent performance using approximately 4 seconds (~100 frames) of input data.
  • Outperformed state-of-the-art methods on the Cornell Activity Dataset (CAD-60) with around 500 frames.
  • Demonstrated that 2-4 clusters effectively model activity instances.
  • Validated on CAD-60 and Telecommunication Systems Team (TST) Fall detection datasets.

Conclusions:

  • The proposed system offers a promising approach for real-world human activity recognition.
  • Effective activity recognition can be achieved with a reduced number of informative postures and frames.
  • The method shows potential for deployment in practical, context-aware applications.