Improving CNN-based activity recognition by data augmentation and transfer learning
Gerasimos Kalouris, Evangelia I. Zacharaki, Vasileios Megalooikonomou · 2019
Activity classification is a challenging problem due to large signal dimensionality, high intra-and inter-subject variability in activity patterns, presence of transitional classes showing mixture of patterns, and dominance of the null class. Supervised learning has been the prevalent choice with deep neural networks (DNNs) showing some promising potential. Deep learning however requires a large number of labeled samples which is difficult to acquire, especially from vulnerable older people. In this paper we implement 3 different convolutional neural network architectures trained on data from older people, incorporating Bayesian optimization for efficient hyperparameter tuning. We exploit various augmentation methods for time-series to make invariant predictions and also cross-utilize knowledge about physical activity of younger persons in order to improve generalization in our models designed for older adults.