Limited-memory warping LCSS for real-time low-power pattern recognition in wireless nodes

Roggen, Daniel, Ponce Cuspinera, Luis, Pombo, Guilherme, Ali, Falah and Nguyen-Dinh, Long-Van (2015) Limited-memory warping LCSS for real-time low-power pattern recognition in wireless nodes. 12th European Conference on Wireless Sensor Networks, Porto, Portugal, 9-11 February 2015. Published in: Abdelzaher, Tarek, Pereira, Nuno and Tovar, Eduardo, (eds.) Wireless Sensor Networks 12th European Conference, EWSN 2015, Proceedings. 8965 151-167. Springer, Cham, Switzerland. ISSN 0302-9743 ISBN 978-3-319-15581-4

[img] PDF - Accepted Version
Download (255kB)

Abstract

We present and evaluate a microcontroller-optimized limited-memory implementation of a Warping Longest Common Subsequence algorithm (WarpingLCSS). It permits to spot patterns within noisy sensor data in real-time in resource constrained sensor nodes. It allows variability in the sensed system dynamics through warping; it uses only integer operations; it can be applied to various sensor modalities; and it is suitable for embedded training to recognize new patterns. We illustrate the method on 3 applications from wearable sensing and activity recognition using 3 sensor modalities: spotting the QRS complex in ECG, recognizing gestures in everyday life, and analyzing beach volleyball. We implemented the system on a low-power 8-bit AVR wireless node and a 32-bit ARM Cortex M4 microcontroller. Up to 67 or 140 10-second gestures can be recognized simultaneously in real-time from a 10Hz motion sensor on the AVR and M4 using 8mW and 10mW respectively. A single gesture spotter uses as few as 135μW on the AVR. The method allows low data rate distributed in-network recognition and we show a 100 fold data rate reduction in a complex activity recognition scenario. The versatility and low complexity of the method makes it well suited as a generic pattern recognition method and could be implemented as part of sensor front-ends.

Item Type: Conference Proceedings
Additional Information: Published in: Wireless sensor networks (Lecture notes in computer science, Volume 8965, 2015, pages 151-167)
Keywords: Activity Recognition, Wearable Sensing, Streaming pattern spotting, Distributed Recognition, Machine Learning, Event Processing
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics > TK7885 Computer engineering. Computer hardware
Depositing User: Daniel Roggen
Date Deposited: 04 Feb 2015 13:49
Last Modified: 09 Apr 2019 14:59
URI: http://sro.sussex.ac.uk/id/eprint/52754

View download statistics for this item

📧 Request an update