ObjectivesFalling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data.MethodsWe first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall.ResultsTo evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups.ConclusionsIn this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.

An Intelligent Tutoring System (ITS) is a computer software that help students in learning educational or academics concepts in customized environment. ITSs are instructional systems that have capability to facilitate user by providing instantaneous feedback and instructions without any human intervention. The advancement of new technologies has integrated computer based learning with artificial intelligence methods with aim to develop better custom-made education systems that referred as ITS. One of the important factors that affect students learning process is self-learning; all students cannot have similar experience of learning scholastic concepts from same educational material. Because students have individual differences that make some topics difficult or easy to understand regarding taken subjects. These systems have capability to improve teaching and learning process in different educational domains while respecting individual learning needs. In this study an attempt is made to review the research in field of ITSs and highlight the educational areas or domains in which ITSs have been introduced. Techniques, delivering modes and evaluation methodologies that have been used in developed ITSs have also been discussed in this work. This work will be helpful for both academia and new comers in the field of ITSs to further strengthen basis of tutoring systems in educational domains.

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The advancement in Information and Communication Technology (ICT) has provided new opportunities for teaching and learning in the form of e-learning. However, developing specialized contents, accommodating profiles of learners, e-learning pedagogy and available ICT infrastructure are the real challenges that need to be properly addressed for any successful e-learning system. The adaptability in an e-learning system can be used to address many of these challenges and issues. This paper proposes a learner model for adaptable e-learning model. The proposed model is based on the findings of a survey conducted to investigate the profiles and preferences of the local learners. The conceptual framework highlights the layered model of adaptable e-learning with the knowledge level of learners as the foundation layer. The foundation layer is derived from four components of adaptable elearning, i.e., domain, program pedagogy, student model and technology interface. The learner algorithm retrieves the adaptable contents from the domain model by analyzing the learner information stored in the student model. The eassessment is part of the program pedagogy and the assessment results are used to control the presentation and navigation of adaptable contents during the learning process. The model has been tested on a Computer Science course offered by Allama Iqbal Open University, Islamabad, Pakistan at Post Graduate Diploma level. The results show that the proposed adaptable elearning model has significantly improved the knowledge level of the learners.