# Human Activity Recognition Using CSI Information with Nexmon
## 1 Introduction  
One of the most discussed recent research topics in wireless technology and smart home applications is human activity recognition (HAR), and there are numerous applications such as health care, ambient assisted living, and children and elderly people monitoring systems. In 2017 in 22 Western European countries there were reported at least 8.4 million injury cases due to falls. Most people who sought medical help were people aged 70 and older. According to [1] 54,504 of them had fatal falls. A total of 5667 per 100,000 people in the age range 70-74 years and 47,239 per 100,000 people in the age category of 95+ years required medical help due to injuries. However, these numbers are greatly underestimated, since not every elderly person reports every fall. As a result, delay of medical assistance can worsen the condition of hidden wounds and make a person more vulnerable to future falls. Once the person falls, the probability of a person falling again doubles [2]. In some cases, immediate help is a matter of life and death because elderly people might lie helpless and unconscious at home for hours before they are admitted to the hospital, as they are notable to call for help. HAR for motion detection and human fall detection could benefit the medical staff in hospitals, elderly people care houses and people with paralysis, epilepsy, hypoglycemia diabetic diseases, and others. Detection of human activities and classification of these activities are challenging tasks. In recent years, devices like the Closed Circuit Television (CCTV) monitoring systems and wearable devices are mostly used to accomplish these tasks. Recent advancements in Wi-Fi-based machine learning solutions could become an alternative and simplified solution for usability and personal privacy concerns [3]. Apart from this, HAR has excellent potential for smart home applications. Instead of using infrared or proximity sensors to control an individual electronic device, the Wi-Fi-based solution could be integrated to control all the devices in the smart home or nursing homes [4]. Another potential application area could be crowd counting and better management of the public places crowd, shopping malls, or open markets in a situation like Coronavirus disease (COVID-19), or the situation where emergency response is required. In wireless communication standard 802.11, the channel state information (CSI) contains useful information in a Wi-Fi packet's preamble. It consists of information about the amplitude attenuation and phase shift of the transmitted signal alongside the transmitted path. Analysis of CSI data can give information about surroundings and variations over time while the Wi-Fi signal is being transmitted [5]; for the reader's convenience, we have included a brief introduction to the subject in Section 5. The HAR system can act as a virtual eye for monitoring and triggering a responsive alarm for quick action emergencies. These days, many elderly people become victims of such instances where quick assistance is critical as falling from stairs, slipping in the bathroom, etc. Wireless HAR can be an alternative solution to video surveillance, as it will protect privacy. There is some CSI-related work already performed in this area by [PERSON] et al. and [PERSON] et al. using the Nexmon tool [6; 7; 8]. The full control and access to wireless hardware's CSI information are not always possible as most wireless chip manufacturers keep these features private and inaccessible to the outer world. Alternatively, the software solutions and hardware solutions provided to get access to this CSI information are expensive. There are some tools based on Linux, which can access the CSI of a channel up to 40 MHz [9; 10]. Interestingly, the open-source Nexmon tool used in this work can analyze up to 80 MHz [7]. Recent work [11; 12] used Intel Wi-Fi Link (IWL) 5300 Network Interface Card (NIC), which supports 802.11n standard.