Hidden Markov model-based smart annotation for benchmark cyclic activity recognition database using wearables

Christine F. Martindale (Author), Sebastijan Šprager (Author), Björn M. Eskofier (Author)

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.

Keywords

prepoznavanje;podatkovne baze;activity recognition;benchmark database;gait analysis;inertial measurement unit;cyclic activities;home monitoring;smart annotation;semi-supervised learning;

Data

Language:	English
Year of publishing:	2019
Typology:	1.01 - Original Scientific Article
Organization:	UL FE - Faculty of Electrical Engineering
UDC:	004.5
COBISS:	31415043
ISSN:	1424-8220
Views:	208
Downloads:	69
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	prepoznavanje;podatkovne baze;
Type (COBISS):	Article
Pages:	str. 1-21
Volume:	ǂVol. ǂ19
Issue:	ǂiss. ǂ8
Chronology:	2019
DOI:	10.3390/s19081820
ID:	13736587