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

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

Povzetek

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.

Ključne besede

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

Podatki

Jezik:	Angleški jezik
Leto izida:	2019
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FE - Fakulteta za elektrotehniko
UDK:	004.5
COBISS:	31415043
ISSN:	1424-8220
Št. ogledov:	208
Št. prenosov:	69
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	prepoznavanje;podatkovne baze;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 1-21
Letnik:	ǂVol. ǂ19
Zvezek:	ǂiss. ǂ8
Čas izdaje:	2019
DOI:	10.3390/s19081820
ID:	13736587