A comparative study of ANN tuning methods for multiclass daily activity and fall recognition

Smart phones and other sensor-enabled devices are very frequently used daily life devices. Movement data obtained by sensors from these devices can be interpreted by artificial intelligence algorithms and this may be critically helpful in some daily life issues. Such a daily activity and fall classification mechanism is particularly important for rapid and accurate medical intervention to the elderly people who live alone. In addition, the real time human activity recognition (HAR) is important for healthcare solutions and better assistance of intelligent personal assistants (IPAs). In this study, the dataset is obtained from 6 different wearable sensors. It contains 20 daily activities and 16 fall motions on the 3060 observations. To classify these movements separately, 3 different Artificial Neural Network (ANN) training algorithms were chosen as the basis. These are gradient descent, momentum with gradient descent and Adam algorithms. Dropout and L2 regularization techniques are used to obtain better results for the test data. The results have shown that the ANN based approach correctly recognizes the daily activities and falls with 94.58% accuracy score on the test set.