A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Feature extraction <s> Due to the rapid aging of the European population, an effort needs to be made to ensure that the elderly can live longer independently with minimal support of the working-age population. The Confidence project aims to do this by unobtrusively monitoring their activity to recognize falls and other health problems. This is achieved by equipping the user with radio tags, from which the locations of body parts are determined, thus enabling posture and movement reconstruction. In the paper we first give a general overview of the research on fall detection and activity recognition. We proceed to describe the machine learning approach to activity recognition to be used in the Confidence project. In this approach, the attributes characterizing the user’s behavior and a machine learning algorithm must be selected. The attributes we consider are the locations of body parts in the reference coordinate system (fixed with respect to the environment), the locations of body parts in a body coordinate system (affixed to the user’s body) and the angles between adjacent body parts. Eight machine learning algorithms are compared. The highest classification accuracy of over 95 % is achieved by Support Vector Machine used on the reference attributes and angles. <s> BIB001 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Feature extraction <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> Feature extraction <s> The proliferation of smartphones has significantly facilitated people’s daily life, and diverse and powerful embedded sensors make smartphone a ubiquitous platform to acquire and analyze data, which may also provide great potential for efficient human activity recognition. This paper presents a systematic performance analysis of motion-sensor behavior for human activity recognition via smartphones. Sensory data sequences are collected via smartphones, when participants perform typical and daily human activities. A cycle detection algorithm is applied to segment the data sequence for obtaining the activity unit, which is then characterized by time-, frequency-, and wavelet-domain features. Then both personalized and generalized model using diverse classification algorithms are developed and implemented to perform activity recognition. Analyses are conducted using 27 681 sensory samples from 10 subjects, and the performance is measured in the form of F-score under various placement settings, and in terms of sensitivity to user space, stability to combination of motion sensors, and impact of data imbalance. Extensive results show that each individual has its own specific and discriminative movement patterns, and the F-score for personalized model and generalized model can reach 95.95% and 96.26%, respectively, which indicates our approach is accurate and efficient for practical implementation. <s> BIB003
Over the years, feature extraction methods known as TDFD methods have shown good result in literature. Few authors who have conducted research using TDFD methods to extract features include BIB002 BIB001 ) where they investigated 17 features comprises of mean, STD, correlation, SMA and entropy using support vector machine (SVM) for recognition performance. The such as (mean, STD, COR, RMS and FFT_RMS) were utilised and used in BIB002 . It features mentioned above. It is necessary for mean to be generated first before other statistical metric features can be extracted based on computed mean BIB003 . The entire TD features were later transformed to FD using the FFT method.