  Unlike the more commonly analyzed ECG or PPG data for activity
classification, heart rate time series data is less detailed, often noisier and
can contain missing data points. Using the BigIdeasLab_STEP dataset, which
includes heart rate time series annotated with specific tasks performed by
individuals, we sought to determine if general classification was achievable.
Our analyses showed that the accuracy is sensitive to the choice of
window/stride size. Moreover, we found variable classification performances
between subjects due to differences in the physical structure of their hearts.
Various techniques were used to minimize this variability. First of all,
normalization proved to be a crucial step and significantly improved the
performance. Secondly, grouping subjects and performing classification inside a
group helped to improve performance and decrease inter-subject variability.
Finally, we show that including handcrafted features as input to a deep
learning (DL) network improves the classification performance further.
Together, these findings indicate that heart rate time series can be utilized
for classification tasks like predicting activity. However, normalization or
grouping techniques need to be chosen carefully to minimize the issue of
subject variability.
