  Recent studies in Human Activity Recognition (HAR) have shown that Deep
Learning methods are able to outperform classical Machine Learning algorithms.
One popular Deep Learning architecture in HAR is the DeepConvLSTM. In this
paper we propose to alter the DeepConvLSTM architecture to employ a 1-layered
instead of a 2-layered LSTM. We validate our architecture change on 5 publicly
available HAR datasets by comparing the predictive performance with and without
the change employing varying hidden units within the LSTM layer(s). Results
show that across all datasets, our architecture consistently improves on the
original one: Recognition performance increases up to 11.7% for the F1-score,
and our architecture significantly decreases the amount of learnable
parameters. This improvement over DeepConvLSTM decreases training time by as
much as 48%. Our results stand in contrast to the belief that one needs at
least a 2-layered LSTM when dealing with sequential data. Based on our results
we argue that said claim might not be applicable to sensor-based HAR.

Human-Computer Interaction;Machine Learning