A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Investigation of ARAC <s> Recent years have witnessed the emergence of Smart Environments technology for assisting people with their daily routines and for remote health monitoring. A lot of work has been done in the past few years on Activity Recognition and the technology is not just at the stage of experimentation in the labs, but is ready to be deployed on a larger scale. In this paper, we design a data-mining framework to extract the useful features from sensor data collected in the smart home environment and select the most important features based on two different feature selection criterions, then utilize several machine learning techniques to recognize the activities. To validate these algorithms, we use real sensor data collected from volunteers living in our smart apartment test bed. We compare the performance between alternative learning algorithms and analyze the prediction results of two different group experiments performed in the smart home. <s> BIB001 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Investigation of ARAC <s> Activity recognition has attracted increasing attention as a number of related research areas such as pervasive computing, intelligent environments and robotics converge on this critical issue. It is also driven by growing real-world application needs in such areas as ambient assisted living and security surveillance. This chapter aims to provide an overview on existing approaches, current practices and future trends on activity recognition. It is intended to provide the necessary material to inform relevant research communities of the latest developments in this field in addition to providing a reference for researchers and system developers who are working towards the design and development of activity-based context aware applications. The chapter first reviews the existing approaches and algorithms that have been used for activity recognition in a number of related areas. It then describes the practice and lifecycle of the ontology-based approach to activity recognition that has recently been under vigorous investigation. Finally the chapter presents emerging research on activity recognition by outlining various issues and directions the field will take. <s> BIB002 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Investigation of ARAC <s> Activity-Based Computing [1] aims to capture the state of the user and its environment ::: by exploiting heterogeneous sensors in order to provide adaptation to ::: exogenous computing resources. When these sensors are attached to the subject’s ::: body, they permit continuous monitoring of numerous physiological signals. This ::: has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence ::: (AmI) in daily activity monitoring for elderly people. In this paper, ::: we present a system for human physical Activity Recognition (AR) using smartphone ::: inertial sensors. As these mobile phones are limited in terms of energy and ::: computing power, we propose a novel hardware-friendly approach for multiclass ::: classification. This method adapts the standard Support Vector Machine (SVM) ::: and exploits fixed-point arithmetic. In addition to the clear computational advantages ::: of fixed-point arithmetic, it is easy to show the regularization effect of the ::: number of bits and then the connections with the Statistical Learning Theory. A ::: comparison with the traditional SVM shows a significant improvement in terms ::: of computational costs while maintaining similar accuracy, which can contribute ::: to develop more sustainable systems for AmI. <s> BIB003 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Investigation of ARAC <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> BIB004 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Investigation of ARAC <s> Abstract To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Calinski–Harabasz index, or by choosing appropriate values for ɛ and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand. <s> BIB005
• ARAC: It has been said that intelligent pervasive environments and applications will influence future and living spaces in transforming human lives and impact the society BIB002 . AR which is a classification problem is known as an increasingly important determinant to the success of CA and personalised pervasive applications BIB001 BIB004 . In Figures 6(a)-6(b), some of the publications employed AR by collecting data in real time using different choice of number of participant (student) to investigate ARAC BIB001 BIB004  . However, BIB004 employs the AR using 20 participants to mitigate crowd disaster whenever an emergency is sensed thus predict at what time would stampede occur using CA framework with the CAC-WSN. Lots of researches in AR BIB003 BIB005 were found in the literature but BIB004 is the first to utilise the 92% of ARAC reported using DT (J48) specifically for mitigation of crowd disaster. Crowd disaster is an important global issue aimed to be address through SLR presented in this paper to X-ray the research gap and the unsolved problem in disaster management research. Crowd disaster is an issue in the previous study demands further investigation to further enhance the method of addressing the serious menace in human lives in our society. For example, online approach permits access to each data point of activity in real time with the adequate update AR data, which is capable of detecting abnormal behaviour that can lead to stampede in a crowd prior to the emergency occurrence.