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Towards Efficient and Real-Time Human Activity Recognition Using Wearable Sensors: A Shapelet-Based Pattern Matching Approach

  • Delaram YazdansepasEmail author
  • Nitin Saroha
  • Lakshmish Ramaswamy
  • Khaled Rasheed
Conference paper
  • 55 Downloads
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

With the advent and proliferation of wearable sensors, Human Activity Recognition (HAR) has received considerable research attention in recent years. Most existing HAR systems operate in a batch-processing (offline) mode, and they rely upon complex features from accelerometer readings for activity recognition. On the other hand, many applications such as continuous patient monitoring and elder fall detection demand real-time human activity recognition, and existing offline systems are inadequate for these applications. In this paper, we investigate challenges of real-time human activity recognition and present an effective framework based on a waveform pattern matching approach. We introduce the concept of A-Shapelets (activity shapelets), which is a representative pattern for each activity. Our framework incorporates several novel aspects; first, we present a scheme for computing the most distinctive A-Shapelet for each activity. Our scheme extracts repetitive patterns from wave forms. Second, our framework builds decision tree models using a personalized library of A-Shapelets. Third, we present a low-overhead matching algorithm for classifying incoming accelerometer data stream in real-time. This paper reports a series of experiments to evaluate the proposed framework. Our experiments demonstrate that the performance of our scheme is very good and the accuracy is comparable to offline HAR systems.

Keywords

Activity recognition Time series Shapelet Pattern matching Wearable device Accelerometer 

Notes

Acknowledgements

The authors would like to thank Paula Capece, Grant Cooksey, Dr. Jennifer Gay, Dr. Fredrick Maier for their contributions and help in this project. Also we would like to thank Dr. Matthew Buman for providing us the dataset. This research has been partially funded by the National Science Foundation (NSF) under grant numbers CCF-1442672 and SES- 1637277. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors, and do not necessarily reflect the views of the NSF.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Delaram Yazdansepas
    • 1
    Email author
  • Nitin Saroha
    • 1
  • Lakshmish Ramaswamy
    • 1
  • Khaled Rasheed
    • 1
  1. 1.Department of Computer ScienceUniversity of GeorgiaAthensUSA

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