Details
Originalsprache | Englisch |
---|---|
Seiten (von - bis) | 404-407 |
Seitenumfang | 4 |
Fachzeitschrift | Current Directions in Biomedical Engineering |
Jahrgang | 10 |
Ausgabenummer | 4 |
Publikationsstatus | Veröffentlicht - 1 Dez. 2024 |
Abstract
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Biomedizintechnik
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in: Current Directions in Biomedical Engineering, Jahrgang 10, Nr. 4, 01.12.2024, S. 404-407.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - On Stair Walk Recognition Using a Single Magnetometer-free IMU and Deep Learning
AU - Kuhlgatz, Timo
AU - Jordine, Marco
AU - Lehmann, Dustin
AU - Seel, Thomas
N1 - © 2024 by Walter de Gruyter Berlin/Boston
PY - 2024/12/1
Y1 - 2024/12/1
N2 - 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%.
AB - 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%.
KW - Human activity recognition
KW - deep learning
KW - feature assessment
KW - gait classification
KW - quaternions
UR - http://www.scopus.com/inward/record.url?scp=85217083855&partnerID=8YFLogxK
U2 - 10.1515/cdbme-2024-2099
DO - 10.1515/cdbme-2024-2099
M3 - Article
VL - 10
SP - 404
EP - 407
JO - Current Directions in Biomedical Engineering
JF - Current Directions in Biomedical Engineering
IS - 4
ER -