Browsing by Author "Samani, H"

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Thumbnail Image
    item: Conference-Full-text
    Human activity recognition using cnn & lstm
    (Faculty of Information Technology, University of Moratuwa., 2020-12) Shiranthika, C; Premakumara, N; Chiu, HL; Samani, H; Shyalika, C; Yang, CY; Karunananda, AS; Karunananda, AS; Talagala, PD
    In identifying objects, understanding the world, analyzing time series and predicting future sequences, the recent developments in Artificial Intelligence (AI) have made human beings more inclined towards novel research goals. There is a growing interest in Recurrent Neural Networks (RNN) by AI researchers today, which includes major applications in the fields of speech recognition, language modeling, video processing and time series analysis. Recognition of Human Behavior or the Human Activity Recognition (HAR) is one of the difficult issues in this wonderful AI field that seeks answers. As an assistive technology combined with innovations such as the Internet of Things (IoT), it can be primarily used for eldercare and childcare. HAR also covers a broad variety of real-life applications, ranging from healthcare to personal fitness, gaming, military applications, security fields, etc. HAR can be achieved with sensors, images, smartphones or videos where the advancement of Human Computer Interaction (HCI) technology has become more popular for capturing behaviors using sensors such as accelerometers and gyroscopes. This paper introduces an approach that uses CNN and Long Short-Term Memory (LSTM) to predict human behaviors on the basis of the WISDM dataset.
  • No Thumbnail Available
    item: Conference-Full-text
    iot driving assistant system for elderly
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2019-12) Huang, KM; Hsieh, TL; Yi, CA; Samani, H; Yang, CY; Sudantha, BH
    Nowadays in this modern globalizing world, elders are given a more prominent place in the society. Thus a lot of new technological innovations are being invented in order to facilitate their day to day activities. The elder driving assistive technology is one such big step which has introduced to cater the demands of elderly people. This technology is beneficial in increasing safety of the driver and passengers, and also has affect in reducing the public cost of society safety. This study proposes a driver assistive tool for elder drivers which respond immediately in case of potential accidents by giving appropriate warning or arrestments. This tool monitors the movement of the vehicle dynamically with the behavior of the driver, detects the irregularity between the driver and the vehicle. Sensory devices which have been installed in the vehicle include the imaging camera, inertial measurement unit, Lidar scanner, steering wheel angle sensor, depression sensors on accelerator pedal and brake pedal to form a sensory network for the purpose of collecting signals for irregularity identification. In order to detect the surrounding suspicious objects, an array of ultrasonic sensors was installed on the vehicle and to evaluate the irregularity level of the danger, a sensor harness was integrated. When the risk level reaches a significant level, a danger classification information will be delivered to the processing center and a corresponding ensemble of sensory feedback will be activated to remind to the driver. Driver will be notified via the signal interface automatically, which has been implemented to resist the operation or even stop the vehicle immediately when a dangerous situation related to inaccurate behavior is detected to prevent potential disasters.
  • No Thumbnail Available
    item: Conference-Full-text
    Object detection with deep learning for underwater environment
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2019-12) Wang, CC; Samani, H; Yang, CY; Sudantha, BH
    In this research we have investigated the usage of deep learning algorithms for object detection in underwater environment and specifically we have employed YOLOv3 algorithm in our study. Details of the algorithm and experimental results are presented. We used available underwater database for training and investigated the method by experimenting to detect and identify the type of the fish in an aquarium in the lab. The results are also explained in this paper.