With the increasingly serious air pollution, people are paying more and more attention to air quality. However, air quality information is not available for all regions, as the number of air quality monitoring stations in a city is limited. Existing air quality estimation methods only consider the multisource data of partial regions and separately estimate the air qualities of all regions. In this article, we propose a deep citywide multisource data fusion-based air quality estimation (FAIRY) method. FAIRY considers the citywide multisource data and estimates the air qualities of all regions at a time. Specifically, FAIRY constructs images from the citywide multisource data (i.e., meteorology, traffic, factory air pollutant emission, point of interest, and air quality) and uses SegNet to learn the multiresolution features from these images. The features with the same resolution are fused by the self-attention mechanism to provide multisource feature interactions. To get a complete air quality image with high resolution, FAIRY refines low-resolution fused features by employing high-resolution fused features through residual connections. In addition, the Tobler's first law of geography is used to constrain the air qualities of adjacent regions, which can fully use the air quality relevance of nearby regions. Extensive experimental results demonstrate that FAIRY achieves the state-of-the-art performance on the Hangzhou city dataset, outperforming the best baseline by 15.7% on MAE.

Unsupervised user adaptation aligns the feature distributions of the data from training users and the new user, so a well-trained wearable human activity recognition (WHAR) model can be well adapted to the new user. With the development of wearable sensors, multiple wearable sensors based WHAR is gaining more and more attention. In order to address the challenge that the transferabilities of different sensors are different, we propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model. It aligns the data of each sensor separately to achieve local alignment, while uniformly aligning the data of all sensors to ensure global alignment. In addition, an attention mechanism is proposed to focus the activity classifier of SALIENCE on the sensors with strong feature discrimination and well distribution alignment. Experiments are conducted on two public WHAR datasets, and the experimental results show that our model can yield a competitive performance.

Complex activity recognition is a valuable issue in mobile and wearable computing. Since complex activities are strongly relevant to users' locations, location data can be used in complex activity recognition. Current activity recognition methods utilizing location data either do not make the most of location data or have a “cold-start” problem. In this paper, we propose CAROLINA (complex activity recognition using acceleration, vital sign, and location data) to recognize complex activities from wearable sensors. CAROLINA divides a map into grid cells and builds a grid cell-activity matrix by integrating check-in and ATUS (American Time Use Survey) datasets. Since some grid cells do not have check-in records, there are some null entries in the matrix, and all the entries are reconstructed by matrix factorization. Meanwhile, a POI (point of interest) category dataset is exploited to build a grid cell-grid cell similarity matrix, which is used to reduce the loss of matrix factorization. The results of experiments show that CAROLINA can effectively utilize location data to improve complex activity recognition performance.

Effectively utilizing multimodal information (e.g., heart rate and acceleration) is a promising way to achieve wearable sensor based human activity recognition (HAR). In this paper, an activity recognition approach MARCEL (Multimodal Activity Recognition with Classifier Ensemble) is proposed, which exploits the diversity of base classifiers to construct a good ensemble for multimodal HAR, and the diversity measure is obtained from both labeled and unlabeled data. MARCEL uses neural network (NN) as base classifiers to construct the HAR model, and the diversity of classifier ensemble is embedded in the error function of the model. In each iteration, the error of the model is decomposed and back-propagated to base classifiers. To ensure the overall accuracy of the model, the weights of base classifiers are learnt in the classifier fusion process with sparse group lasso. Extensive experiments show that MARCEL is able to yield a competitive HAR performance, and has its superiority on exploiting multimodal signals.