  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.
