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

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

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.

• Language: English
• Keywords: activity recognition, benchmark database, gait analysis, inertial measurement unit, gait phases, cyclic activities, home monitoring, smart annotation, semi-supervised learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2019
• Number of pages: 21 str.
• Numbering: Vol. 19, iss. 8, art. 1820
• PID: 20.500.12556/RUL-132338
• UDC: 004.5
• ISSN on article: 1424-8220
• DOI: 10.3390/s19081820
• COBISS.SI-ID: 31415043
• Publication date in RUL: 22.10.2021

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 16.04.2019

Secondary language

• Language: Slovenian
• Keywords: prepoznavanje, podatkovne baze

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: German Research Foundation (DFG), Heisenberg professorship program
• Project number: ES 434/8-1

• Funder: EC - European Commission
• Funding programme: EIT health innovation projects programme
• Acronym: HOOP 2.0