Norasmadi Abdul Rahim
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Preferred name
Norasmadi Abdul Rahim
Official Name
Norasmadi, Abdul Rahim
Alternative Name
Abdul Rahim, Norasmadi
Rahim, N. A.
Rahim, Norasmadi Abd
Rahim, N. Abdul
Rahim, Norasmadi Abdul
Rahim, Norasmadi Bin Abdul
Main Affiliation
Scopus Author ID
36901996000
Researcher ID
DNS-5050-2022

Research Output

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  • Publication
    Rssi-based for device-free localization using deep learning technique
    ( 2020-06-01)
    Abdul Syafiq Abdull Sukor
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Sukhairi Sudin
    ;
    Hiromitsu Nishizaki
    Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.
      3  30
  • Publication
    A study of lower limb muscles fatigue during running based on EMG signals
    ( 2019-07-01)
    Yousif H.A.
    ;
    Norasmadi Abdul Rahim
    ;
    Ahmad Faizal Salleh
    ;
    Ammar Zakaria
    ;
    Alfarhan K.A.
    ;
    Mahmood M.
    Incorrect running may lead to discomfort and injuries, where each day around the world, the numbers of runners are increasing. The goal of this research work is to evaluate and study the lower limb muscles fatigue during running for 400-meters with two types of running strategies based on the Electromyography (EMG) signals. The EMG signals are collected from Rectus Femoris (RF), Biceps Femoris (BF), and Gastrocnemius Lateralis (GL) muscles during the run on the tartan athletic track with two types of running strategies. The first type: the first 200-meters running with normal speed and the last 200-meters running with full speed. The second type: the first 300-meters running with normal speed and the last 100-meters running with full speed. The EMG signals were transformed into the frequency domain using fast Fourier transform (FFT) to extract the features of mean frequency (MNF) and median frequency (MDF). From the results of the two strategies with MDF and MNF features of the selected muscles, the lowest fatigue index was during the 1st strategy for most the selected muscles.
      1  17
  • Publication
    A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes
    ( 2019)
    Abdul Syafiq Abdull Sukor
    ;
    Ammar Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Latifah Munirah Kamarudin
    ;
    Rossi Setchi
    ;
    Hiromitsu Nishizaki
    Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.
      19  16