Inertia Based Recognition of Daily Activities with ANNs and Spectrotemporal Features

As mobile and personal health devices gain in popularity, increasing amounts of data is collected via their embedded sensors such as heart rate monitors and accelerometers. Data analytics and more specifically machine learning algorithms can transform this data into actionable information to improve personal healthcare and quality of life. The main objective of this study is to develop an algorithmic classification framework using feed-forward multilayer perceptrons and statistically rich spectrotemporal features to recognize daily activities based on 3-axis acceleration data. A multitude of MLP topologies and setups, such as different numbers and sizes of hidden layers, supervised output structuring, etc. are tested to comprehensively analyze the clustering capabilities of the artificial neural network for a wide-range of settings. In addition, the contribution of subset of features to classification accuracy is studied to identify respective information potentials and further improve accuracy. Publicly available wrist-worn accelerometer dataset from University of California Irvine's machine learning repository is used for fair comparison with the most recent literature published using the same dataset. Results indicate a significant improvement in recognition rate where the overall accuracy over seven selected activity classes is 91% compared to 54% of the latest publication using the same dataset.