Multitask Network for Respiration Rate Estimation -- A Practical Perspective
This work addresses the challenge of unobtrusive respiration monitoring for health and fitness applications, representing an incremental improvement over existing deep learning approaches.
This paper tackles the problem of estimating respiration rate from wearable sensor data during daily activities by presenting a multitasking deep learning architecture that processes ECG and accelerometer signals. The proposed model achieved better overall accuracy than existing methods and outperformed individual sensor modalities across different activities.
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.