A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> FNA for effective mitigation of crowd disaster <s> Wireless Sensor Networks raise the interest of different business domains, including public security [5]. The ability of WSNs to monitor and control physical environments such as football stadiums makes them very attractive. The integration of WSNs into Command and Control (C2) systems aims at avoiding catastrophes such as the Heysel Stadium disaster [16]. In this paper, the architecture and implementation of a prototype for stadium surveillance is presented. We demonstrate the notification of alerts processed on sensor nodes and routed toward a C2 system. The prototype aims at increasing situational awareness of decision makers and at providing real time information related to the occurrence of incidents within the stadium. Decision makers can react accordingly by assigning available first responders to the incidents. Such integration comes along with security requirements; the use of encryption-based access control and trustworthiness evaluation for the generated alerts to ensure their confidentiality and reliability is proposed. <s> BIB001 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> FNA for effective mitigation of crowd disaster <s> 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. <s> BIB002 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> FNA for effective mitigation of crowd disaster <s> Abstract Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks. <s> BIB003
FNA can be determine by false negative rate (FNR). FNR is one of the common measurements in a classification evaluation in AR and data mining research BIB001 . FNR is the number of misclassified negative activity meant to be accurate but predicted wrongly in any classification analysis BIB002 BIB003 . Investigation of event prediction before any terrible accident happen is very important  . Although, it is applicable to classification evaluation but is very relevant when we talk of detection of abnormal movement in a crowd scenario as well. To the best of the researcher's knowledge only four studies related to crowd monitoring found in literature are shown in Figure 6 (a) BIB002 . Most importantly one of the study employed feature extraction method using raw sensor data from AR for the prediction of crowd disaster based on ARAC performance BIB002 . However, major limitation of existing study is high FNA noticed in BIB002 , which is attributed to the ARAC of 92% reported. The ARAC is inadequate for stampede prediction during emergency in a crowd scenario. As a result of the high FNA, there is needed to extend the research conducted by BIB002 , thus form the reason for the SLR conducted in this paper. For example, a case of someone who is almost fallen in a critical moment and such situation is reported to the security or stakeholder as sitting or standing require proper classification; the implication of this may lead to accident or pose a risk to human life if adequate attention is not taken, this scenario is trigger by FNA.