  In recent years, human activity recognition has garnered considerable
attention both in industrial and academic research because of the wide
deployment of sensors, such as accelerometers and gyroscopes, in products such
as smartphones and smartwatches. Activity recognition is currently applied in
various fields where valuable information about an individual's functional
ability and lifestyle is needed. In this study, we used the popular WISDM
dataset for activity recognition. Using multivariate analysis of covariance
(MANCOVA), we established a statistically significant difference (p<0.05)
between the data generated from the sensors embedded in smartphones and
smartwatches. By doing this, we show that smartphones and smartwatches don't
capture data in the same way due to the location where they are worn. We
deployed several neural network architectures to classify 15 different hand and
non-hand-oriented activities. These models include Long short-term memory
(LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural
Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models
performed best with watch accelerometer data. Also, we saw that the
classification precision obtained with the convolutional input classifiers (CNN
and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15
activities. Additionally, the CNN model for the watch accelerometer was better
able to classify non-hand oriented activities when compared to hand-oriented
activities.
