Empirical Mode Decomposition and Hilbert Spectrum for Abnormality Detection in Normal and Abnormal Walking Transitions

Bayu Erfianto, Achmad Rizal () and Sugondo Hadiyoso
Additional contact information
Bayu Erfianto: School of Computing, Telkom University, Bandung 40257, Indonesia
Achmad Rizal: School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
Sugondo Hadiyoso: School of Applied Science, Telkom University, Bandung 40257, Indonesia

IJERPH, 2023, vol. 20, issue 5, 1-14

Abstract: Sensor-based human activity recognition (HAR) is a method for observing a person’s activity in an environment. With this method, it is possible to monitor remotely. HAR can analyze a person’s gait, whether normal or abnormal. Some of its applications may use several sensors mounted on the body, but this method tends to be complex and inconvenient. One alternative to wearable sensors is using video. One of the most commonly used HAR platforms is PoseNET. PoseNET is a sophisticated platform that can detect the skeleton and joints of the body, which are then known as joints. However, a method is still needed to process the raw data from PoseNET to detect subject activity. Therefore, this research proposes a way to detect abnormalities in gait using empirical mode decomposition and the Hilbert spectrum and transforming keys-joints, and skeletons from vision-based pose detection into the angular displacement of walking gait patterns (signals). Joint change information is extracted using the Hilbert Huang Transform to study how the subject behaves in the turning position. Furthermore, it is determined whether the transition goes from normal to abnormal subjects by calculating the energy in the time-frequency domain signal. The test results show that during the transition period, the energy of the gait signal tends to be higher than during the walking period.

Keywords: human activity recognition; joint; PoseNET; Hilbert Huang Transform (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/20/5/3879/pdf (application/pdf)
https://www.mdpi.com/1660-4601/20/5/3879/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:20:y:2023:i:5:p:3879-:d:1076423

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().