Improving CNNbased activity recognition by data augmentation and transfer learning Activity classification is a challenging problem due to large signal dimensionality, high intraand intersubject 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 timeseries to make invariant predictions and also crossutilize knowledge about physical activity of younger persons in order to improve generalization in our models designed for older adults.

Smart Phone Based Human Activity Recognition Human Activity Recognition (HAR) is a field that uses collected data to classify different human actions. One simple and general approach to HAR is to use the sensor data from a mobile device to recognize different patterns behind complex motions. Early studies show promising results on simple activities using manually selected features from accelerometer readings. As newer publicly available datasets include more complex data and activities, manual feature selection have become cumbersome, impractical and face limitations in finding the optimal feature sets for HAR. In this paper, we present an empirical approach to defining models of 3D tensor data structures from 2D time series data obtained from multiple sensors on a smart phone, and a new Convolutional Neural Network (CNN) model, which uses the tensor data and performs automatic feature extraction and classification for HAR. We use the public benchmark dataset, MobiAct v2.0, to train and validate our model, which achieved an overall better performance in classifying 11 Activities of Daily Living (ADL) than the stateoftheart approaches. Compared to the approach presented by Chatzaki et al. which has a very high rate of misclassifications for carstep out (CSO), carstep in (CSI), sit to stand (CHU), and stand to sit (SCH) classes, our proposed approach has 15% higher sensitivity for each of these activities with the optimal number of training epochs being only 25.