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Optimizing Neural Networks for Activity Recognition in Daily Living: A Case Study Using Signal Processing and Smartwatch Sensors

Authors:
Klemens Waldhör
Philipp Müller

Keywords: Activity Recognition; Signal Processing; Neural Networks; Wearable Computing; Smartwatch Sensors.

Abstract:
This study explores the impact of various signal processing techniques on neural network performance for activity recognition using smartwatch sensor data. Four common activities of daily living (ADLs) including drinking, tumbling, teeth brushing, and walking are evaluated. Signal processing methods, Gaussian filtering, principal component analysis (PCA), Fourier transform (FT), empirical mode decomposition (EMD), and Hilbert-Huang transform (HHT), are systematically assessed for their effectiveness in improving neural network classification accuracy.

Pages: 126 to 130

Copyright: Copyright (c) IARIA, 2025

Publication date: July 6, 2025

Published in: conference

ISBN: 978-1-68558-284-5

Location: Venice, Italy

Dates: from July 6, 2025 to July 10, 2025