Human activity recognition using either wearable devices or smartphones can benefit various applications including healthcare, fitness, smart home, etc. Instead of using wearable devices which are intrusive and require extra cost, we shall leverage on modern smartphones embedded with a variety of sensors. Due to the flexibility of using smartphones, the recognition accuracy will degrade with orientation, placement, and subject variations. In this paper, we propose a robust human activity recognition system in terms of orientation, placement, and subject variations based on coordinate transformation and principal component analysis (CT-PCA) and online support vector machine (OSVM). The proposed CT-PCA scheme is utilized to eliminate the effect of orientation variations. Experiments show that the proposed scheme significantly improves the activity recognition accuracy and outperforms the state-of-the-art methods on leave one orientation out experiments, which demonstrates the generalization ability of the proposed scheme on the data from unseen orientations. We also show the effectiveness of this scheme on placement and subject variations. However, the inherent difference of signal properties for different placement and subject dramatically reduces the recognition accuracy, especially for different placement. Thus, we present an efficient OSVM algorithm, that is, online-independent support vector machine (OISVM), which utilizes a small portion of data from the unseen placement or subject to online update the parameters of the SVM algorithm. The experimental results demonstrate the effectiveness of this OISVM algorithm on placement and subject variations.

Human activity recognition (HAR) based on inertial sensors has been investigated for many industrial informatics applications, such as healthcare and ubiquitous computing. Existing methods mainly rely on supervised learning schemes which require large labeled training data. However, labeled data is sometimes difficult to acquire, while unlabeled data is readily available. Thus, we intend to make use of both labeled and unlabeled data with semi-supervised learning for accurate HAR. In this paper, we propose a semi-supervised deep learning approach, using temporal ensembling of Deep Long Short-Term Memory (DLSTM), to recognize human activities with smartphone inertial sensors. With the deep neural network processing, features are extracted for local dependencies in the recurrent framework. Besides, with an ensemble approach based on both labeled and unlabeled data, we can combine together the supervised and unsupervised losses, so as to make good use of unlabeled data which the supervised learning method cannot leverage. Experimental results indicate the effectiveness of our proposed semi-supervised learning scheme, when compared to several state-of-the-art semi-supervised learning approaches.

Buildings consume quite a lot of energy; hence, the issue of building energy efficiency has attracted a great deal of attention in recent years. A key factor in achieving this objective is occupancy information that directly impacts on energy-related building control systems. In this paper, we leverage on environmental sensors that are nonintrusive and cost-effective for building occupancy estimation. Our result relies on feature engineering and learning. The conventional feature engineering requires one to manually extract relevant features without a clear guideline. This blind feature extraction is labor intensive and may miss some significant implicit features. To address this issue, we propose a convolutional deep bidirectional long short-term memory (CDBLSTM) approach that contains a convolutional network and a deep structure to automatically learn significant features from the sensory data without human intervention. Moreover, the long short-term memory networks are able to capture temporal dependencies in the data and the bidirectional structure can take the past and future contexts into consideration for the final identification of occupancy. We have conducted real experiments to evaluate the performance of our proposed CDBLSTM approach. Instead of estimating the exact number of occupants, we attempt to identify the range of occupants, i.e., zero, low, medium, and high, which is adequate for most of building control systems. The experimental results indicate the effectiveness of our proposed approach compared with the state-of-the-art methods.