@inproceedings{9d488511a9424eb6a821241264777f55,
title = "Fusion from Multimodal Gait Spatiotemporal Data for Human Gait Speed Classifications",
abstract = "Human gait pattens remain largely undefined when relying on a single sensing modality. We report a pilot implementation of sensor fusion to classify gait spatiotemporal signals, from a publicly available dataset of 50 participants, harvested from four different type of sensors. For fusion we propose a hybrid Convolutional Neural Network and Long Short-Term Memory (hybrid CNN+LSTM) and Multi-stream CNN. The classification results are compared to single modality data using Single-stream CNN, a state-of-the-art Vision Transformer, and statistical classifiers algorithms. The fusion models outperformed the single modality methods and classified gait speed of previously unseen 10 random subjects with 97% F1-score prediction accuracy of the four gait speed classes.",
keywords = "Deep Convolutional Neural Networks (CNN), Gait Speed, Long Short-Term Memory (LSTM), Multimodal Data, Transformers",
author = "Alharthi, {Abdullah S.} and Ozanyan, {Krikor B.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE Sensors, SENSORS 2021 ; Conference date: 31-10-2021 Through 04-11-2021",
year = "2021",
doi = "10.1109/SENSORS47087.2021.9639816",
language = "English (US)",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Sensors, SENSORS 2021 - Conference Proceedings",
address = "United States",
}