# A CSI-Based Multi-Environment Human Activity Recognition Framework
### Related Work
#### 1.1.2 Non-Wearable Activity Recognition Approaches  
We refer to the approaches by which the activities of a human can be recognized without placing any sensor on the subject as _non-wearable activity recognition approaches_. Non-wearable approaches can be generally grouped into two main categories: vision-based approaches and Wi-Fi-based approaches [18; 19]. Vision-based activity recognition was the first approach by which researchers attempted to recognize human activities. The work of [20] utilized low-cost depth cameras to recognize human activities. The effectiveness of their approach was tested on three public benchmark datasets, including: MSR daily activity 3D [21], MSR action pair dataset [22], and MSR action 3D dataset [23]. The approach presented in [20] partitioned the body into moving parts, with each of these parts used to generate a set of features. Ref. [24] proposed a compact representation of the human postures via histograms of the 3D location of joints. Hidden Markov models were used to analyze the extracted feature vectors to build the classification models. A novel skeleton representation was proposed by [7]. Inspired by the observation that the geometric relationship between the body parts holds much more meaning than their abstract location, a skeletal representation was developed. In [6], using the chain coding technique, the silhouettes from different frames are bounded together and converted into joint points to provide an online human action recognition system. Despite the promising results attained by vision-based approaches, there are several limitations that reduce the potentials of using these approaches in real-world applications, including: (1) The need to record the activities of the subject implies that there is an invasion of the privacy of the subject. (2) These approaches fail to recognize the subject's activity if the subject was located behind an obstacle. As a remedy to the aforementioned limitations, researchers have investigated other techniques, such as Wi-Fi signals. Wi-Fi-based activity recognition came to light due to the increasing popularity of the Internet of Things (IoT) concept. Almost all devices in one's household can now connect with each other and with the Internet via wireless connectivity. Researchers noticed this trend and utilized these wireless signals to recognize the activities the subject performs. Particularly, researchers have utilized the CSI values embedded within the exchanged wireless packets to recognize human activities. Unlike the vision-based approaches, wireless signals can penetrate objects; thus, the subject's activity can be recognized even with the existence of obstacles. Furthermore, HAR systems that relies on wireless signals can preserve the privacy of subjects. In this regard, researchers have utilized the CSI signals to recognize human emotions [25], sleep monitoring [26], vital signs monitoring [27], human-human interactions recognition [28; 29], and human identification [30; 31]. An example on CSI-based HAR systems is the approach presented by [32], in which they developed an activity recognition system composed of two main models. Specifically, the first model determines the speed at which the subject is performing the activity, while the second model maps the speed from the first model with the activity being performed. The researchers in [32] attempted to determine the activity from the speed of the subject, which was determined using the CSI information contained in the Wi-Fi signals. Similarly, Ref. [33] attempted to recognize human activities in an indoor environment. The first problem they tried to solve was determining if an activity has occurred or not, which was solved using a two-level decision tree. The first level detects activities, while the second level utilizes a deep neural network to determine the activity detected in the first level. An accuracy between 96% and 98% was achieved by increasing the number of receiver antennas.