The aim of this paper is to present a dataset for Wi-Fi-based human activity recognition. The dataset is comprised of five experiments performed by 30 different subjects in three different indoor environments. The experiments performed in the first two environments are of a line-of-sight (LOS) nature, while the experiments performed in the third environment are of a non-line-of-sight (NLOS) nature. Each subject performed 20 trials for each of the experiments which makes the overall number of recorded trials in the dataset equals to 3000 trials (30 subjects × 5 experiments × 20 trials). To record the data, we used the channel state information (CSI) tool [1] to capture the exchanged Wi-Fi packets between a Wi-Fi transmitter and receiver. The utilized transmitter and receiver are retrofitted with the Intel 5300 network interface card which enabled us to capture the CSI values that are contained in the recorded transmissions. Unlike other publicly available human activity datasets, this dataset provides researchers with the ability to test their developed methodologies on both LOS and NLOS environments, in addition to many different variations of human movements, such as walking, falling, turning, and pen pick up from the ground.

This paper presents a dataset for Wi-Fi-based human-to-human interaction recognition that comprises twelve different interactions performed by 40 different pairs of subjects in an indoor environment. Each pair of subjects performed ten trials of each of the twelve interactions and the total number of trials recorded in our dataset for all the 40 pairs of subjects is 4800 trials (i.e., 40 pairs of subjects × 12 interactions × 10 trials). The publicly available CSI tool [1] is used to record the Wi-Fi signals transmitted from a commercial off-the-shelf access point, namely the Sagemcom 2704 access point, to a desktop computer that is equipped with an Intel 5300 network interface card. The recorded Wi-Fi signals consist of the Received Signal Strength Indicator (RSSI) values and the Channel State Information (CSI) values. Unlike the publicly available Wi-Fi-based human activity datasets, which mainly have focused on activities performed by a single human, our dataset provides a collection of Wi-Fi signals that are recorded for 40 different pairs of subjects while performing twelve two-person interactions. The presented dataset can be exploited to advance Wi-Fi-based human activity recognition in different aspects, such as the use of various machine learning algorithms to recognize different human-to-human interactions.

Channel state information (CSI)-based human activity recognition plays an essential role in various application domains, such as security, healthcare, and Internet of Things. Most existing CSI-based activity recognition approaches rely on manually designed features that are classified using traditional classification methods. Furthermore, the use of deep learning methods for CSI-based activity recognition is still at its infancy with most of the existing approaches focus on recognizing single-human activities. The current study explores the feasibility of utilizing deep learning methods to recognize human-to-human interactions (HHIs) using CSI signals. Particularly, we introduce an end-to-end deep learning framework that comprises three phases, which are the input, feature extraction, and recognition phases. The input phase converts the raw CSI signals into CSI images that comprise time, frequency, and spatial information. In the feature extraction phase, a novel convolutional neural network (CNN) is designed to automatically extract deep features from the CSI images. Finally, the extracted features are fed to the recognition phase to identify the class of the HHI associated with each CSI image. The performance of our proposed framework is assessed using a publicly available CSI dataset that was acquired from 40 different pairs of subjects while performing 13 HHIs. Our proposed framework achieved an average recognition accuracy of 86:3% across all HHIs. Moreover, the experiments indicate that our proposed framework enabled significant improvements over the results achieved using three state-of-the-art pre-trained CNNs as well as the results obtained using four different conventional classifiers that employs traditional handcrafted features.

Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (CSI)-based HAR systems. In this work, we present a multi-environment human activity recognition system based on observing the changes in the CSI values of the exchanged wireless packets carried by OFDM subcarriers. In essence, we introduce a five-stage CSI-based human activity recognition approach. First, the acquired CSI values associated with each recorded activity instance are processed to remove the existing noise from the recorded data. A novel segmentation algorithm is then presented to identify and extract the portion of the signal that contains the activity. Next, the extracted activity segment is processed using the procedure proposed in the first stage. After that, the relevant features are extracted, and the important features are selected. Finally, the selected features are used to train a support vector machine (SVM) classifier to identify the different performed activities. To validate the performance of the proposed approach, we collected data in two different environments. In each of the environments, several activities were performed by multiple subjects. The performed experiments showed that our proposed approach achieved an average activity recognition accuracy of 91.27%.

Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of $10\mathrm{m}\times 10\mathrm{m}$ . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

Radio frequency identification (RFID) and wireless sensor networks (WSN) have been popular in industrial and academic field due to their ability in identity identification and data transmission respectively. Both RFID and wireless sensor networks have undergone huge development separately. In this paper, it has been discussed how helpful and effective it will be when RFID and WSN have been combined together. It also discusses about some applications of this integrated technology in the field of precious animal and patient health monitoring where real time information is of utmost importance. A system structure of the integrated RFID and WSN has been introduced and the simulation results show that the new system outperforms traditional RFID monitory system in terms of the cost of deployment, updating delay and tag capacity requirement.