  Human Activity Recognition (HAR) is one of the fundamental building blocks of
human assistive devices like orthoses and exoskeletons. There are different
approaches to HAR depending on the application. Numerous studies have been
focused on improving them by optimising input data or classification
algorithms. However, most of these studies have been focused on applications
like security and monitoring, smart devices, the internet of things, etc. On
the other hand, HAR can help adjust and control wearable assistive devices, yet
there has not been enough research facilitating its implementation. In this
study, we propose several models to predict four activities from inertial
sensors located in the ankle area of a lower-leg assistive device user. This
choice is because they do not need to be attached to the user's skin and can be
directly implemented inside the control unit of the device. The proposed models
are based on Artificial Neural Networks and could achieve up to 92.8% average
classification accuracy
