Deep Learning and Signal-Processing Methods for Multisensor Data Fusion : Applications to Ambulatory Health Monitoring / by Arlene John, Barry Cardiff, Deepu John.

Format:

Book

Author/Creator:

John, Arlene.

Series:

Engineering Series

Language:

English

Subjects (All):

Electronic circuit design.

Cooperating objects (Computer systems).

Signal processing.

Electronics Design and Verification.

Cyber-Physical Systems.

Digital and Analog Signal Processing.

Local Subjects:

Electronics Design and Verification.

Cyber-Physical Systems.

Digital and Analog Signal Processing.

Physical Description:

1 online resource (328 pages)

Edition:

1st ed. 2026.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2026.

Summary:

This book focuses on the development of multisensor fusion algorithms for wearable devices that are useful in ambulatory health monitoring using signal-processing and deep learning-based methods. The algorithms described account for the signal quality prior to fusion, in order to enable reliable inferences without contributing to additional computational overhead. The content discussed is beneficial in the broad application of multisensor fusion, as the algorithms developed or discussed in the final chapters are generalized cases of the methods developed in the initial chapters, offering relevance to the broader multisensor fusion community.

Contents:

Chapter 1. Introduction

Chapter 2. Fusion- a multi-domain topic

Chapter 3. Signal quality indicators for ECG signals obtained from wearable IoT sensors

Chapter 4. Multi-sensor fusion for heartrate estimation

Chapter 5. Multimodal data fusion for heartbeat detection

Chapter 6. Multiresolution fusion for sleep apnea detection

Chapter 7. Multi-level fusion for atrial fibrillation detection

Chapter 8. Conclusion.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-96724-0

9783031967245

OCLC:

1569123394