CSI Human Activity

Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person. There are numerous possibilities to use the Wi-Fi-based HAR solution for human-centric applications in intelligent surveillance, such as in the health care sector for human fall detection, smart homes for temperature control, a light control application, motion detection applications, and human fall detection for older people nursing homes. This paper's focal point is to classify human activities such as WALK, SIT, SIT-DOWN, STAND,STAND-UP, FALL, LYING, and EMPTY with deep neural network long-term short memory (LSTM) and support vector machine (SVM). Special care was taken to address practical issues such as using available commodity hardware. Therefore, the open-source tool Nexmon was used for the channel state information (CSI) extraction on inexpensive hardware (Raspberry Pi 3B+, Pi 4B, and Asus RT-AC86U routers). We conducted three different types of experiments using different algorithms, which all demonstrated a similar accuracy in prediction for HAR with an accuracy between 97% to 99.7% which is superior to previously published results. We also disclose details about the experimental setup.