You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters 
Chapter Title:  Context-related information that has been used for DM 
1. Abstract of Cited Paper (BIB001):  Many observations of the dynamics of pedestrian crowds, including various self-organization phenomena, have been successfully described by simple many-particle models. For ethical reasons, however, there is a serious lack of experimental data regarding crowd panic. Therefore, we have analyzed video recordings of the crowd disaster in Mina/Makkah during the Hajj in 1426H on 12 January 2006. They reveal two subsequent, sudden transitions from laminar to stop-and-go and "turbulent" flows, which question many previous simulation models. While the transition from laminar to stop-and-go flows supports a recent model of bottleneck flows [D. Helbing, Phys. Rev. Lett. 97, 168001 (2006)], the subsequent transition to turbulent flow is not yet well understood. It is responsible for sudden eruptions of pressure release comparable to earthquakes, which cause sudden displacements and the falling and trampling of people. The insights of this study into the reasons for critical crowd conditions are important for the organization of safer mass events. In particular, they allow one to understand where and when crowd accidents tend to occur. They have also led to organizational changes, which have ensured a safe Hajj in 1427H. 
2. Abstract of Cited Paper (BIB002):  Abstract Our research works focuses on the design and implementation of a novel ubiquitous multi context-aware mobile phone sensing network for mitigation of crowd disasters using machine-to-machine (M2M) communications. A mobile sensor network system integrated with wireless multimedia sensor networks (WMSNs) was designed for effective prediction of a stampede during crowd disasters. This proposed sensor network consists of mobile devices that are used as crowd monitoring participant nodes that employ light sensors, accelerometers, as well as audio and video sensors to collect the relevant data. Real-time crowd dynamics modeling and real-time activity modeling have been achieved by implementing the algorithms developed for Context Acquisition and multi-context fusion. Dynamic crowd monitoring was achieved by implementing the context based region identification and grouping of participants, distributed crowd behavior estimation, and stampede prediction based on distributed consensus. The implementation of the proposed architecture in Android smartphone provides light-weight, easy to deploy, context aware wireless services for effective crowd disaster mitigation and generation of an in time alert to take measures to avoid the occurrence of a stampede. The system has been tested and illustrated within a group of people for stampede prediction by using empirically collected data. 
3. Abstract of Cited Paper (BIB003):  In this paper, the authors describe a method of accurately detecting human activity using a smartphone accelerometer paired with a dedicated chest sensor. The design, implementation, testing and validation of a custom mobility classifier are also presented. Offline analysis was carried out to compare this custom classifier to de-facto machine learning algorithms, including C4.5, CART, SVM, Multi-Layer Perceptrons, and Naïve Bayes. A series of trials were carried out in Ireland, initially involving N=6 individuals to test the feasibility of the system, before a final trial with N=24 subjects took place in the Netherlands. The protocol used and analysis of 1165min of recorded activities from these trials are described in detail in this paper. Analysis of collected data indicate that accelerometers placed in these locations, are capable of recognizing activities including sitting, standing, lying, walking, running and cycling with accuracies as high as 98%.

Literature Review:
Time domain (TD) is a feature extraction method conventionally used in AR research domain BIB003 Khan et al., 2014; BIB001 . Example mean, standard deviation, (STD), minimum (min.), maximum (max.), range, variance, root mean square (RMS) and interquarter tile in Table 6 , the TD features that has been extracted and used for AR specifically for prediction of crowd disaster are mean along x, y, z axes; STD along x, y, z axes; correlation coefficient (COR) of xy, xz, yz axes; RMS of x, y and z axes BIB003 . In addition to this, the frequency domain (FD) for RMS has equally been used. Consequently, the TD and FD metrics (mean, STD, COR, RMS and FFT_RMS) have produced good result in AR research BIB002 , outside their commonly used as feature extraction methods in the AR domain (Khan et al., 2014; BIB004 . The features are of light weight in nature this potential makes the features suitable for smartphone with average memory size and processing speed. Therefore, the features require less battery consumption of smartphone device when used to monitor movement behaviour of people in a crowd scenario. Most importantly, some salient features capable of producing higher accuracy for early detection of danger using AR in a crowd, but shows good result in other domain not considered in the previous disaster domain research are among the challenges that this SLR equally identified as part of finding.