  Human Activity Recognition (HAR) is a crucial technology for many
applications such as smart homes, surveillance, human assistance and health
care. This technology utilises pattern recognition and can contribute to the
development of human-in-the-loop control of different systems such as orthoses
and exoskeletons. The majority of reported studies use a small dataset
collected from an experiment for a specific purpose. The downsides of this
approach include: 1) it is hard to generalise the outcome to different people
with different biomechanical characteristics and health conditions, and 2) it
cannot be implemented in applications other than the original experiment. To
address these deficiencies, the current study investigates using a publicly
available dataset collected for pathology diagnosis purposes to train Machine
Learning (ML) algorithms. A dataset containing knee motion of participants
performing different exercises has been used to classify human activity. The
algorithms used in this study are Gaussian Naive Bayes, Decision Tree, Random
Forest, K-Nearest Neighbors Vote, Support Vector Machine and Gradient Boosting.
Furthermore, two training approaches are compared to raw data (de-noised) and
manually extracted features. The results show up to 0.94 performance of the
Area Under the ROC Curve (AUC) metric for 11-fold cross-validation for Gradient
Boosting algorithm using raw data. This outcome reflects the validity and
potential use of the proposed approach for this type of dataset.
