IOS Press Ebooks - Multimodal Signal Fusion for Heartbeat Monitoring on eScooters (original) (raw)

Multimodal Signal Fusion for Heartbeat Monitoring on eScooters

Authors

Himanshu Singh, Joana M. Warnecke, Alexander Picker, Nagarajan Ganapathy, Thomas M. Deserno

Pages

973 - 977

DOI

10.3233/SHTI240573

Category

Research Article

Series

Ebook

Abstract

Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures. We perform an evaluation study using skin-attached ECG electrodes for ground truth generation. The CNN+LSTM late fusion accurately measures the heartbeat for 76.17% of the driving time.