On Stair Walk Recognition Using a Single Magnetometer-free IMU and Deep Learning

verfasst von

Timo Kuhlgatz, Marco Jordine, Dustin Lehmann, Thomas Seel

Abstract

Human activity recognition (HAR) is an expanding area of research. Although sensors are becoming more readily available, there is a trend toward minimizing the number of associated sensors for better applicability. Neural networks are often employed for HAR, as they can identify movement patterns within data. To optimize the amount of useful information provided to the network, feature extraction methods are commonly applied. However, these feature methods complicate the data processing pipeline, and thus are less applicable to continuous real-time applications. In this work, we investigate the impact of using quaternions as input features for an LSTM network on HAR, specifically focusing on level walking and stair climbing activities, while also considering the inference time. We demonstrate that combining quaternions and raw IMU data, i. e., acceleration and angular rates significantly enhances classification accuracy without adversely affecting the inference time. By additionally adding an approximate estimate of the vertical position change to the input data, the classification accuracy is further improved to a value of 96.18%.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Medizintechnik & Bildverarbeitung
Institut für Produktentwicklung und Gerätebau
Data Management and Analytics (DMA)

Typ

Artikel

Journal

Current Directions in Biomedical Engineering

Band

10

Seiten

404-407

Anzahl der Seiten

4

Publikationsdatum

01.12.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Biomedizintechnik

Elektronische Version(en)

https://doi.org/10.1515/cdbme-2024-2099 (Zugang: Offen)