  Inertial Measurement Unit (IMU) sensors are becoming increasingly ubiquitous
in everyday devices such as smartphones, fitness watches, etc. As a result, the
array of health-related applications that tap onto this data has been growing,
as well as the importance of designing accurate prediction models for tasks
such as human activity recognition (HAR). However, one important task that has
received little attention is the prediction of an individual's heart rate when
undergoing a physical activity using IMU data. This could be used, for example,
to determine which activities are safe for a person without having him/her
actually perform them. We propose a neural architecture for this task composed
of convolutional and LSTM layers, similarly to the state-of-the-art techniques
for the closely related task of HAR. However, our model includes a
convolutional network that extracts, based on sensor data from a previously
executed activity, a physical conditioning embedding (PCE) of the individual to
be used as the LSTM's initial hidden state. We evaluate the proposed model,
dubbed PCE-LSTM, when predicting the heart rate of 23 subjects performing a
variety of physical activities from IMU-sensor data available in public
datasets (PAMAP2, PPG-DaLiA). For comparison, we use as baselines the only
model specifically proposed for this task, and an adapted state-of-the-art
model for HAR. PCE-LSTM yields over 10% lower mean absolute error. We
demonstrate empirically that this error reduction is in part due to the use of
the PCE. Last, we use the two datasets (PPG-DaLiA, WESAD) to show that PCE-LSTM
can also be successfully applied when photoplethysmography (PPG) sensors are
available to rectify heart rate measurement errors caused by movement,
outperforming the state-of-the-art deep learning baselines by more than 30%.
