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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Human Activities and Postures Recognition: From Inertial Measurements to Quaternion-Based Approaches.

Makia Zmitri1,2, Hassen Fourati3, And Nicolas Vuillerme4,5

  • 1GIPSA-Lab, Department of Automatic Control, University Grenoble Alpes, 38000Grenoble, France. makia.zmitri@gipsa-lab.fr.

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Summary

This study evaluated human activity and posture recognition using Inertial and Magnetic Measurement Units (IMMUs). Using raw sensor data or extracted attitude (quaternion) achieved over 80% accuracy, with attitude further improving performance and reducing computation time.

Keywords:
activity recognitionattitude estimationraw datasubspace KNNwearable sensors

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Human posture and activity recognition is crucial for applications like healthcare and sports analytics.
  • Inertial and Magnetic Measurement Units (IMMUs) are widely used for motion tracking.
  • Optimizing sensor number and placement is key to efficient and accurate recognition systems.

Purpose of the Study:

  • To assess the impact of the number and placement of IMMUs on human posture and activity recognition.
  • To compare the effectiveness of using raw sensor data versus extracted attitude (quaternion) for recognition.
  • To investigate the trade-offs between accuracy, feature set size, and computation time.

Main Methods:

  • Utilized five Inertial and Magnetic Measurement Units (IMMUs) comprising accelerometers, gyroscopes, and magnetometers.
  • Investigated placements of one to three IMMUs on body segments (back, left thigh, left foot).
  • Employed a subspace k-nearest neighbors (KNN) classifier, processing raw sensor data and quaternion-derived attitude.

Main Results:

  • Over 80% accuracy in recognizing postures and activities using a single IMMU (lower back, left thigh, or left foot).
  • Achieved over 90% accuracy when combining three IMMU placements.
  • Extracting attitude (quaternion) significantly improved classifier performance for specific activities, enhancing accuracy and reducing computation time.

Conclusions:

  • The number and placement of IMMUs significantly influence human activity and posture recognition accuracy.
  • Utilizing extracted attitude (quaternion) offers superior performance compared to raw sensor data, especially for complex activities.
  • A reduced feature set using quaternion data provides a favorable balance of high accuracy and computational efficiency.