With the rapid deployment of indoor Wi-Fi networks, channel state information (CSI) has been used for device-free occupant activity recognition (OAR). However, various environmental factors interfere with the stable propagation of Wi-Fi signals indoors, which causes temporal variation of CSI data. In this study, we investigated temporal CSI variation in a real-world housing environment and its impact on learning-based OAR. The CSI variation over time changes distributions of the CSI data, and the pretrained model’s accuracy performance becomes degraded during long-term monitoring. In order to address the temporal dependency issue, we developed an effective long-term OAR model based on the semi-supervised meta-learning approach. Our model leveraged unlabeled target data with its pseudo labels and synthesized numerous query data sets using mixup-based data augmentation, which generalized the model during training. The model provided an average of 91.09% activity classification accuracy for the target data, which had different statistical characteristics from the source data. This result demonstrates that our model can reliably monitor occupant activities for long-term periods. The data set presented in this study is available in IEEE DataPort athttps://dx.doi.org/10.21227/z10g-vt48.

The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This article focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of nonintrusive motion sensors in a smart home environment. In this article, we propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which provide an effective way to mitigate noise artifacts and confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably capture the gradual change in the degree of routineness compared to baseline approaches that measure the similarity between two consecutive days or capture variations in the occurrence of recognized activities.

Construction workers who usually work in high altitudes and hazardous construction zones are prone to accidents that may lead to injuries and fatalities. This calls for advanced monitoring technologies that can locate workers and hazardous areas within a dynamically evolving outdoor/indoor area. More accurate localization techniques indicate more safety for protecting them from an accident. In this paper, a multiple transmit power assisted sequence-based localization (MPSBL) solution is developed to achieve high localization accuracy. The theoretical analysis and system model design illustrate the feasibility of MPSBL. Simulations and empirical experiments in construction zones have been conducted, which show that MPSBL outperforms state-of-the-art approaches.