The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowdsourced data collection, or the use of semi-supervised algorithms. However, semi-supervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength (RSS) or channel state information (CSI) in wireless sensor networks to localize users in indoor/outdoor environments. In this paper, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reduce data-collection costs while achieving acceptable accuracy.

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Smartphones are linked with individuals and are valuable and yet easily available sources for characterizing users' behavior and activities. User's location is among the characteristics of each individual that can be utilized in the provision of location-based services (LBs) in numerous scenarios such as remote health-care and interactive museums. Mobile phone tracking and positioning techniques approximate the position of a mobile phone and thereby its user, by disclosing the actual coordinate of a mobile phone. Considering the advances in positioning techniques, indoor positioning is still a challenging issue, because the coverage of satellite signals is limited in indoor environments. One of the promising solutions for indoor positioning is fingerprinting in which the signals of some known transmitters are measured in several reference points (RPs). This measured data, which is called dataset is stored and used to train a mathematical

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