# A CSI-Based Multi-Environment Human Activity Recognition Framework
###### Abstract  
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%. channel state information (CSI); human activity recognition (HAR); multi-environment; support vector machine (SVM) +
Footnote †: journal: Applied sciences  
(NLOS) configuration, which is considered an advantage over vision-based human activity recognition approaches that are restricted to line-of-sight (LOS) configuration [6; 7]. The Wi-Fi-based human activity recognition approach also has the advantage over wearable human activity recognition approaches in the sense that it does not affect the motion of the subject. Literature reveals that researchers have utilized two Wi-Fi-related quantities to develop human activity recognition systems, namely the Received Signal Strength (RSS) and the Channel State Information (CSI). The RSS is widely used in determining the performed activity, based on observing the changes in the power of the received signal. A main drawback of the RSS is related to the fact that it measures the power of the transmitted signal, which decays as the distance between the subject and the receiver increases. Thus, the further the distance between the subject and the measuring node, the lower the system's accuracy [8]. The other measurement used to recognize the different activities performed by a human is the CSI values. The CSI values provide a measure of the channel properties, where these properties are strongly affected by the surrounding environment and the changes that occur within the environment, such as the movements of the subjects whether these movements are as small as chest breathing movements [9] or as large as a subject walking from one point to another point within the environment [10]. In this work, we propose a CSI-based approach for recognizing several daily-life human activities. The proposed approach consists of five phases. The first phase applies a denoising method and a dimensionality reduction procedure to the recorded CSI signals. This phase is needed to reduce the interference encapsulated within the recorded CSI signals and, at the same time, reduce the amount of data without losing the information encapsulated within the available data. In the second phase, a segmentation algorithm is applied to the CSI signals obtained from the first phase to extract the segments of the signals that represent intervals during which the subjects performed various activities. After that, in the third phase, the segments obtained from the second phase are cropped from the raw CSI signals, and the noise reduction procedure, which is applied in the first phase, is applied to the extracted activity segments.