# Human Activity Recognition Using CSI Information with Nexmon
## 3 Related Work  
As HAR is getting widely used, there are many studies on this topic. In 2017 [PERSON] et al. developed a system called Wi-Fall mainly for detecting falls using Wi-Fi CSI data [22]. In addition to fall, the following activities were analyzed: walking, sitting down, and standing up. These activities were performed in three different locations as chamber, laboratory, and dormitory. Activities were collected using three transmitting and three receiving antennas, which means nine streams were analyzed. As a first step anomaly detection algorithm, more precisely local outlier factor (LOF) was performed. The main idea of the LOF algorithm is to first calculate the density of a point and its k-nearest neighbors and then compare the density of each point with its neighbor's density. If the density of a point is much smaller than its neighbor's, then the point is considered to be an outlier. In cases when the majority of streams had outliers, this activity was considered an anomaly. After anomaly detection, singular value decomposition (SVD) was performed. It was observed that the first three samples of the SVD matrix can describe most characteristics of the whole matrix, so the three best eigenvalues were extracted for use in classification. As a classification technique, two different classification algorithms were used. First, a one-class support vector machine (SVM) was utilized to detect falls. The following features were extracted for the SVM classifier:  
* Normalized standard deviation
* Offset of signal strength
* Period of motion
* Median absolute deviation (MAD)
* Interquartile range
* Signal entropy
* Velocity of signal change  
As a result, the precision of fall detection was from 83% to 96% depending on the location of the performed fall and the false alarm rate was from 11% to 18%. To detect other activities besides fall, random forest was used. With random forest classification, precision increased and became 89% to 98% with false alarm rate from 10% to 15%. In 2019 [PERSON] et al. [23] suggested Wi-Fi CSI based HAR using Deep Recurrent Neural Network (HARNN). The idea of their approach is to extract features for Recurrent Neural Network (RNN) training and recognize the activity. This work consists of four main parts:  
* Human Activity Detecting
* Data Processing
* Feature Extraction
* Human Activity Recognition  
The first module is used to detect whether any activity was performed. This decision is made by a two-level decision tree that uses variance and correlation coefficient of raw data. If the decision tree detects any activity, the next module is executed. The second module is responsible for denoising data. The feature extraction module is later performed on denoised data where two features are extracted, namely channel power variation in the time domain and time-frequency analysis in the frequency domain. The last module consists of an LSTM that is trained with extracted features to recognize the activity. The following activities were analyzed: running, walking, standing, sitting, crouching, and lying. Activities were collected using Intel 5300 NIC together with CSI Tool [21]. As a result, the average accuracy of all the above-mentioned activities is 96%. Recently, convolutional neural networks (CNNs) have been applied together with bidirectional long short-term memory (Bi-LSTM) for classification of human activities including fall [24], achieving an overall accuracy of 95% to 98%. In [12] similar results have been obtained (with an accuracy of 100% for FALL which was obtained with a multi-staged approach using two different SVMs for removing false positives). While the majority of the research is still using Intel 5300 NIC together with the CSI Tool, latest research was done in 2020 on activity recognition [25] using Raspberry Pi and Nexmon firmware to extract the CSI data. The following activities were collected: nothing, stand-up, sit-down, get into bed, cook, washing dishes, brush teeth, drink, pet cat, sleeping, and walking.