Multitask Network for Respiration Rate Estimation -- A Practical Perspective

TL;DR

This paper introduces a multitask deep learning architecture that accurately estimates both instantaneous and average respiration rates from ECG and accelerometer data during daily activities, addressing noise and motion artifacts.

Contribution

It presents a novel multitasking deep learning model combining Encoder-Decoder and Encoder-IncResNet architectures for respiration rate estimation from wearable sensor data.

Findings

Outperforms existing ML and DL models in accuracy

Achieves lower MAE and RMSE across activities

Demonstrates efficiency in real-world scenarios

Abstract

The exponential rise in wearable sensors has garnered significant interest in assessing the physiological parameters during day-to-day activities. Respiration rate is one of the vital parameters used in the performance assessment of lifestyle activities. However, obtrusive setup for measurement, motion artifacts, and other noises complicate the process. This paper presents a multitasking architecture based on Deep Learning (DL) for estimating instantaneous and average respiration rate from ECG and accelerometer signals, such that it performs efficiently under daily living activities like cycling, walking, etc. The multitasking network consists of a combination of Encoder-Decoder and Encoder-IncResNet, to fetch the average respiration rate and the respiration signal. The respiration signal can be leveraged to obtain the breathing peaks and instantaneous breathing cycles. Mean absolute error(MAE), Root mean square error (RMSE), inference time, and parameter count analysis has been used to compare the network with the current state of art Machine Learning (ML) model and other DL models developed in previous studies. Other DL configurations based on a variety of inputs are also developed as a part of the work. The proposed model showed better overall accuracy and gave better results than individual modalities during different activities.