Next Article in Journal
Basic MAC Scheme for RF Energy Harvesting Wireless Sensor Networks: Throughput Analysis and Optimization
Previous Article in Journal
A High Sensitivity Temperature Sensing Probe Based on Microfiber Fabry-Perot Interference
Previous Article in Special Issue
Recognition and Repetition Counting for Complex Physical Exercises with Deep Learning
Article Menu

Export Article

Open AccessArticle
Sensors 2019, 19(8), 1820; https://doi.org/10.3390/s19081820

Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables

1
Machine Learning and Data Analytics Lab, Computer Science Department, 91052 Erlangen, Germany
2
Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Martindale, C., Roth, N.; Hannink, J.; Sprager, S.; Eskofier, B. Smart Annotation Tool for Multi-sensor gait-based daily activity data. In Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece, 19–23 March 2018.
Received: 2 March 2019 / Revised: 1 April 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
  |  
PDF [1846 KB, uploaded 16 April 2019]
  |  

Abstract

Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%. For scenarios dominated by walking, this annotation effort is as low as 8%. The pipeline consists of three smart annotation approaches, namely edge detection of the pressure data, local cyclicity estimation, and iteratively trained hierarchical hidden Markov models. Using this pipeline, we have collected and labeled a dataset with over 150,000 labeled cycles, each with 2 phases, from 80 subjects, which we have made publicly available. The dataset consists of 12 different task-driven activities, 10 of which are cyclic. These activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions. Each participant wore 5 synchronized inertial measurement units (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video. We believe that this dataset and smart annotation pipeline are a good basis for creating a benchmark dataset for validation of other semi- and unsupervised algorithms. View Full-Text
Keywords: activity recognition; benchmark database; gait analysis; inertial measurement unit; gait phases; cyclic activities; home monitoring; smart annotation; semi-supervised learning activity recognition; benchmark database; gait analysis; inertial measurement unit; gait phases; cyclic activities; home monitoring; smart annotation; semi-supervised learning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Martindale, C.F.; Sprager, S.; Eskofier, B.M. Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables. Sensors 2019, 19, 1820.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top