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Fusion of Multiple Representations Extracted from a Single Sensor’s Data for Activity Recognition Using CNNs

With the emerging ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual’s behavioral information (e.g. physical activity, location, and mood) by using sensors embedded in smartwatches and smartphones. To detect human’s daily life activities, accelerometers have been widely used in wearable devices. In the current research, usually a single data representation is used, i.e., either image or feature vector representations. In this paper, a novel method is proposed to address two key aspects for the future development of robust deep learning methods for Human Activity Recognition (HAR): (1) multiple representations of a single sensor’s data and (2) fusion of these multiple representations. The presented method utilizes Deep Convolutional Neural Networks (CNNs) and was evaluated using a publicly available HAR dataset. The proposed method showed promising performance, with the best result reaching an overall accuracy of 0.97, which outperforms current conventional approaches.