Activity recognition (AR) systems for older adults are common in residential health care including hospitals or nursing homes; therefore, numerous solutions and studies presented to improve the performance of the AR systems. Yet, delivering sufficiently robust AR systems from sensor data recorded is a challenging task. AR in a smart environment utilizes large amounts of sensor data to derive effective features from the data to track the activity daily living. This paper maximizes the performance of AR system from using the convolutional neural network (CNN). Here, it analyzes signals from the network sensors distributed in different places in two clinical rooms at the Elizabeth hospital, such as W2ISP and RFID sensors. The proposed approach recognized the daily activities that consider a key to falling cases for older adults at a hospital or a nursing health house. A deep activity CNNets is used to train the effective features of daily activities sensors data then used for recognizing the highest falling risk activities in testing data. This approach used existing data of fourteen healthy older volunteers (ten females and four males) and then compared to other proposed approaches that used the same dataset. The experimental results show that this approach is superior to others. It achieved (96.37±3.63%) in the first clinic room and (98.37±1.63%) in the second clinic room. As the result, this experiment concludes that deep learning methodology is effectively assessing fall risk based on wearable sensors.

No abstract

No abstract

This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. The results indicate that CNNets are competitive with state-of-the-art methods, exhibiting enhanced IoMT accuracy of 98.37%, which is the highest among the proposed solutions using the same dataset.

E-management means an electronic system implemented to transform the administrative work from manual management to upgraded electronic-management used computer application within an institute. The electronic administration and management are concentrated on the branches of the E-Systems that reduce administrative working costs and upgrade performance achievement and overcome the problem of geographical and temporal dimensions. In another meaning, it tries to develop the administrative structure of society and the development of a working mechanism. Moreover, it overcomes the daily business problems with informatics infrastructure safe and robust and compatible with each other, through resource management and operations using communication networks. This paper focuses on providing an approach to e-governance that is currently the environment of the University of Diyala using. The proposed model is for controlling off the flow of data and protecting it. The verification list questionnaire and the data collection were then implemented from several levels of students and staff at the Faculty of Physical Education and Sport Science from the University of Diyala. The obtained results from the questionnaire showed that (54.8%) of the tested sample accepted the new e management model, the other (45.2%) does not accept this model for various reasons such as lack of computer use and lack of familiarity with electronic applications, or limited community culture.