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
Now showing 1 - 8 of 8
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Publication

Accelerometer-based physical fatigue assessment in 400 meter running event

2021-05-03 , Atiya S.O. , Ahmad Faizal Salleh , Norasmadi Abdul Rahim , Mohammad Shahril Salim , Saidatul A. , Nurhidayah Omar , Hamzah S.

Monitoring physical fatigue and capacity is important in high-performance environments to optimize the training stimulus and minimize unplanned physical fatigue. For that purpose, accelerometer sensors have emerged as a tool with the potential to measure and reflect changes in runner's acceleration. An accelerometer was used to measure the acceleration patterns which provided an analogue signal that could analyzed further by using different feature extractions. This research was aimed to estimate the location of physical fatigue occurrence in 400 m running. Basically, the experiments were conducted in outdoor track field, 6 subjects were involved, with age ranging between 19-24 years. In this study, two features have been used which are root mean square (RMS) and player load (PL) to indicate fatigue. The obtained results have shown that there were declines in the player load values in the last 150 m of running for most of the subjects which indicate occurrence of physical fatigue. On the other hand, some subjects have tried to save their energy in the 2nd and 3rd 100 m to speed up again in the last 100 m but there were obvious decrements in their player load in 400 m compared to the 1 100 m of running.

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Publication

A study of heat insulation methods for enhancing the internal temperature on artificial stingless bee hive

2024 , Muhammad Ammar Asyraf Che Ali , Bukhari Ilias , Norasmadi Abdul Rahim , Shazmin Aniza Abdul Shukor , Abdul Hamid Adom , Mohd Fauzi Abu Hassan

The stingless bees have gained a large popularity among the beekeepers, particularly in tropical and subtropical regions such as the Americas, Africa, and Southeast Asia. This is because the honey of stingless bees has a distinct flavour and is highly valued for its medicinal qualities. Traditionally, stingless bee colonies constructed from wood logs are fragile and vulnerable to outside attacks. These predator or parasite attacks can cause Colony Collapse Disorder (CCD) if not eliminated. Thus, a PVC, 3D-printed PET-G, and acrylic artificial hive has been created to replace the old one. According to earlier research, stingless bees are especially susceptible to temperatures above 38°C. This paper's main goal is to discuss the results of studies on the best artificial hive insulation method. Over a month and a half, clay, wood powder, polystyrene, bubble aluminium foil, and a water- cooling system were tested as insulators. Results shows that artificial hives with bubble aluminium foil have the biggest average difference between internal and external temperatures (6.4°C) and are closest to traditional hives (8.6°C). The average temperature difference between the artificial hive's exterior and inside was 2.9°C without heat insulation. Clay-insulated artificial hives have the lowest standard deviation value for humidity at 0.46. Since temperature is vital to stingless bee survival, bubble aluminium foil container is the best insulation solution since it increases heat resistance more than other materials.

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A study of embedded fuzzy logic to determine artificial stingless bee hive condition and honey volume

2024 , Bukhari Ilias , Norasmadi Abdul Rahim , Shazmin Aniza Abdul Shukor , Abdul Hamid Adom , Muhammad Ammar Asyraf Che Ali , Mohd Al-Haffiz Saad , Mohd Fauzi Abu Hassan

Stingless Bee is particularly nutrient-dense in his honey. Therefore, numerous beekeepers for the Stingless Bee have begun this agricultural enterprise, particularly in Malaysia. However, beekeepers encounter challenges when caring for an excessively large stingless bee colony. Due to the risk of causing colony disruption, the beekeeper cannot always access the hives to monitor honey volume and hive condition. Consequently, the purpose of this paper is to aid beekeepers and prevent disruption to bee colonies by determining the condition of the hive and the quantity of honey using an embedded fuzzy logic system. Artificial hives have been created in order to easily measure the weight of a hive of stingless bees and to divide the honey compartment from the brood compartment in order to calculate the honey volume. Since the stingless bee designs its colony with honey on top and larvae on the bottom, honey volume can be determined by weighing the honey compartment using load cell and internal humidity using dht22. DHT22 is used for measuring the internal temperature and humidity, as previous papers have stated that the hive condition can be determined using the internal temperature and humidity. Morever, FLDa (Fuzzy Logic Designer app) by MATLAB was subsequently utilised to construct membership function, rules, fuzzification, and defuzzification. Then, the same input, membership function, and rules that used in FLDa will be implemented on the Nodemcu ESP8266 using eFLL (Embedded Fuzzy Logic Library). A comparison between the crisp output from FLDa and the crisp output from eFLL was conducted to determine whether eFLL is suitable for use in the NodeMCU ESP8266. As a consequence, the standard deviationand averaged percentage error of differences for hive condition, which is 0.22 and 0.17%, isless than the honey volume, which is 0.49 and 0.66%, because hive condition has a strict correlation with temperature. The hive condition will be rated bad (0% when the temperature is cold or hot state), but it will be rated good (100% when the temperature is normal state). As for honey volume, the majority of results correspond to the percentage of honey compartment weight, unless the humidity is dry state, which will cause the value to be cut in half. Finally, the fuzzy logic system is effectively implemented into an embedded system, making it easier for the beekeeper to monitor the hive condition and honey volume without interfering with the activity of stingless bees.

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Publication

Human breathing classification using electromyography signal with features based on mel-frequency cepstral coefficients

2017-01-01 , Ahmad Nasrul Norali , Abdullah A.H. , Zulkifli Zakaria , Norasmadi Abdul Rahim , Nataraj S.K.

Typical method on assessing the human breathing characteristics is based on measurements of breathing air parameters. Another possible method for human breathing assessment is through the analysis of respiratory muscles electromyography (EMG) signal. The EMG signal from different breathing task will be analyzed in order to determine the characteristics of the EMG signal pattern. Thus, feature extraction need to be done on the EMG signals. This paper will look into the use of Mel-Frequency Cepstral Coefficients (MFCC) in providing the features for EMG signal. Analysis is done using different data analysis frame sizes. EMG signal classification is done using K-Nearest Neighbour. Results shows that MFCC is a good feature extraction method for this purpose with classification accuracy exceeds more than 90% for data analysis frame size of 2000 ms, 4000 ms, 5000 ms and 10000 ms.

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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.

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Multiple-criteria decision analysis for effect of shoot growth at difference combination nutrient fertilizer NPK for Harumanis mango

2023 , Erdy Sulino Mohd Muslim Tan , Norasmadi Abdul Rahim , Marni Azira Markom , Abu Hassan Abdullah , Allan Melvin Andrew , Aimi Salihah Abdul Nasir

It is vital to have the correct fertiliser arrangement for effective tree development, fruit yield, and essential fruit quality. The amount of fertiliser suggested with adequate nutrition will be maintained in the soil to supply the needs of the trees as they grow throughout the various growth stages. This study evaluated the effect of different combinations of Nitrogen(N), Phosphorus(P), and Potassium(K) on the vegetative flush physiology of Harumanis mango (Mangifera Indica. L). Single and combinations of N (511g), P(511g), and K(255g) fertilisers were used, which were N, P, NP, and NPK throughout May 2021. The results revealed that the minimum number of mature green leaves and a higher number of healthy panicles were observed in the NPK-treated plants. Moreover, NPK treatment showed the lowest malformation intensity percentage compared to other fertiliser treatments. The data were analysed to obtain the best regrowth pattern of shoots using Multiple-Criteria Decision Analysis (MCDA) techniques. The results on the pattern of regrowth after pruning when federalised with NPK fertiliser showed that the maximum percentage of total vegetative flush was 87.5% and the remaining 12.5% did not reach a satisfactory level according to the MCDA analysis.

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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.

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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.

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