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A fully labelled image dataset serves as a valuable tool for reproducible research inquiries and data processing in various computational areas, such as machine learning, computer vision, artificial intelligence and deep learning. Today's research on ageing is intended to increase awareness on research results and their applications to assist public and private sectors in selecting the right equipments for the elderlies. Many researches related to development of support devices and care equipment had been done to improve the elderly's quality of life. Indoor object detection and classification for autonomous systems require large annotated indoor images for training and testing of smart computer vision applications. This dataset entitled MYNursingHome is an image dataset for commonly used objects surrounding the elderlies in their home cares. Researchers may use this data to build up a recognition aid for the elderlies. This dataset was collected from several nursing homes in Malaysia comprises 37,500 digital images from 25 different indoor object categories including basket bin, bed, bench, cabinet and others.
Asmida Ismail; Siti Anom Ahmad; Azura Che Soh; Mohd Khair Hassan; Hazreen Haizi Harith. MYNursingHome: A fully-labelled image dataset for indoor object classification. Data in Brief 2020, 32, 106268 .
AMA StyleAsmida Ismail, Siti Anom Ahmad, Azura Che Soh, Mohd Khair Hassan, Hazreen Haizi Harith. MYNursingHome: A fully-labelled image dataset for indoor object classification. Data in Brief. 2020; 32 ():106268.
Chicago/Turabian StyleAsmida Ismail; Siti Anom Ahmad; Azura Che Soh; Mohd Khair Hassan; Hazreen Haizi Harith. 2020. "MYNursingHome: A fully-labelled image dataset for indoor object classification." Data in Brief 32, no. : 106268.
Falls are among the main causes of injuries in elderly individuals. Balance and mobility impairment are major indicators of fall risk in this group. The objective of this research was to develop a fall risk feedback system that operates in real time using an inertial sensor-based instrumented cane. Based on inertial sensor data, the proposed system estimates the kinematics (contact phase and orientation) of the cane. First, the contact phase of the cane was estimated by a convolutional neural network. Next, various algorithms for the cane orientation estimation were compared and validated using an optical motion capture system. The proposed cane contact phase prediction model achieved higher accuracy than the previous models. In the cane orientation estimation, the Madgwick filter yielded the best results overall. Finally, the proposed system was able to estimate both the contact phase and orientation in real time in a single-board computer.
Ibai Gorordo Fernandez; Siti Anom Ahmad; Chikamune Wada. Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation. Sensors 2020, 20, 4675 .
AMA StyleIbai Gorordo Fernandez, Siti Anom Ahmad, Chikamune Wada. Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation. Sensors. 2020; 20 (17):4675.
Chicago/Turabian StyleIbai Gorordo Fernandez; Siti Anom Ahmad; Chikamune Wada. 2020. "Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation." Sensors 20, no. 17: 4675.
Trans-radial prosthesis is a wearable device that intends to help amputees under the elbow to replace the function of the missing anatomical segment that resembles an actual human hand. However, there are some challenging aspects faced mainly on the robot hand structural design itself. Improvements are needed as this is closely related to structure efficiency. This paper proposes a robot hand structure with improved features (four-bar linkage mechanism) to overcome the deficiency of using the cable-driven actuated mechanism that leads to less structure durability and inaccurate motion range. Our proposed robot hand structure also took into account the existing design problems such as bulky structure, unindividual actuated finger, incomplete fingers and a lack of finger joints compared to the actual finger in its design. This paper presents the improvements achieved by applying the proposed design such as the use of a four-bar linkage mechanism instead of using the cable-driven mechanism, the size of an average human hand, five-fingers with completed joints where each finger is moved by motor individually, joint protection using a mechanical stopper, detachable finger structure from the palm frame, a structure that has sufficient durability for everyday use and an easy to fabricate structure using 3D printing technology. The four-bar linkage mechanism is the use of the solid linkage that connects the actuator with the structure to allow the structure to move. The durability was investigated using static analysis simulation. The structural details and simulation results were validated through motion capture analysis and load test. The motion analyses towards the 3D printed robot structure show 70%–98% similar motion range capability to the designed structure in the CAD software, and it can withstand up to 1.6 kg load in the simulation and the real test. The improved robot hand structure with optimum durability for prosthetic uses was successfully developed.
Mohamad Aizat Abdul Wahit; Siti Anom Ahmad; Mohammad Hamiruce Marhaban; Chikamune Wada; Lila Iznita Izhar. 3D Printed Robot Hand Structure Using Four-Bar Linkage Mechanism for Prosthetic Application. Sensors 2020, 20, 4174 .
AMA StyleMohamad Aizat Abdul Wahit, Siti Anom Ahmad, Mohammad Hamiruce Marhaban, Chikamune Wada, Lila Iznita Izhar. 3D Printed Robot Hand Structure Using Four-Bar Linkage Mechanism for Prosthetic Application. Sensors. 2020; 20 (15):4174.
Chicago/Turabian StyleMohamad Aizat Abdul Wahit; Siti Anom Ahmad; Mohammad Hamiruce Marhaban; Chikamune Wada; Lila Iznita Izhar. 2020. "3D Printed Robot Hand Structure Using Four-Bar Linkage Mechanism for Prosthetic Application." Sensors 20, no. 15: 4174.
Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson’s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.
Noor Kamal Al-Qazzaz; Mohannad K. Sabir; Sawal Hamid Bin Mohd Ali; Siti Anom Ahmad; Karl Grammer. Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers. Sensors 2019, 20, 59 .
AMA StyleNoor Kamal Al-Qazzaz, Mohannad K. Sabir, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Karl Grammer. Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers. Sensors. 2019; 20 (1):59.
Chicago/Turabian StyleNoor Kamal Al-Qazzaz; Mohannad K. Sabir; Sawal Hamid Bin Mohd Ali; Siti Anom Ahmad; Karl Grammer. 2019. "Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers." Sensors 20, no. 1: 59.
This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.
Ahmed M. M. Almassri; Wan Zuha Wan Hasan; Siti Anom Ahmad; Suhaidi Shafie; Chikamune Wada; Keiichi Horio. Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network. Sensors 2018, 18, 2561 .
AMA StyleAhmed M. M. Almassri, Wan Zuha Wan Hasan, Siti Anom Ahmad, Suhaidi Shafie, Chikamune Wada, Keiichi Horio. Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network. Sensors. 2018; 18 (8):2561.
Chicago/Turabian StyleAhmed M. M. Almassri; Wan Zuha Wan Hasan; Siti Anom Ahmad; Suhaidi Shafie; Chikamune Wada; Keiichi Horio. 2018. "Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network." Sensors 18, no. 8: 2561.
In this paper, an assessment on the health index (HI) of transformers is carried out based on Neural-Fuzzy (NF) method. In-service condition assessment data, such as dissolved gases, furans, AC breakdown voltage (ACBDV), moisture, acidity, dissipation factor (DF), color, interfacial tension (IFT), and age were fed as input parameters to the NF network. The NF network were trained individually based on two sets of data, known as in-service condition assessment and Monte Carlo Simulation (MCS) data. HI was also obtained from the scoring method for comparison with the NF method. It is found that the HI of transformers that was obtained by NF trained by MCS method is closer to scoring method than NF trained by in-service condition assessment method. Based on the total of 15 testing transformers, NF trained by MCS data method gives 10 transformers with the same assessments as scoring method as compared to eight transformers given by NF trained by in-service condition data method. Analysis based on all 73 transformers reveals that 62% of transformers have the same assessments between NF trained by MCS data and scoring methods.
Emran Jawad Kadim; Norhafiz Azis; Jasronita Jasni; Siti Anom Ahmad; Mohd Aizam Talib. Transformers Health Index Assessment Based on Neural-Fuzzy Network. Energies 2018, 11, 710 .
AMA StyleEmran Jawad Kadim, Norhafiz Azis, Jasronita Jasni, Siti Anom Ahmad, Mohd Aizam Talib. Transformers Health Index Assessment Based on Neural-Fuzzy Network. Energies. 2018; 11 (4):710.
Chicago/Turabian StyleEmran Jawad Kadim; Norhafiz Azis; Jasronita Jasni; Siti Anom Ahmad; Mohd Aizam Talib. 2018. "Transformers Health Index Assessment Based on Neural-Fuzzy Network." Energies 11, no. 4: 710.
In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying window sizes. Therefore, in this study, the authors endeavoured to determine the suitable window size to analyse EMG signals during isotonic contractions utilising stationary tests, reverse arrangement (RA), and modified reverse arrangement (MRA). There were slight differences in the average percentages of signal stationarity for RA and MRA tests in 100 ms, 500 ms, and 1000 ms window sizes. However, there was none for the 200 ms window size. On average, a window size of 200 ms provided stationary information with 88.57% of EMG signals compared to other window sizes. This study also recommended the MRA test to determine EMG signals stationarity for future studies, as the performances were better in comparison to RA tests. However, the following recommendation is only valid for window sizes greater than 200 ms. For a real-time application, the size of the analysis window together with the processing time should be less than 300 ms and a window size of 200 ms is applicable for isotonic contractions.
Nurhazimah Nazmi; Mohd Azizi Abdul Rahman; Shin-Ichiroh Yamamoto; Siti Anom Ahmad; Mb Malarvili; Saiful Amri Mazlan; Hairi Zamzuri. Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions. Applied Sciences 2017, 7, 1050 .
AMA StyleNurhazimah Nazmi, Mohd Azizi Abdul Rahman, Shin-Ichiroh Yamamoto, Siti Anom Ahmad, Mb Malarvili, Saiful Amri Mazlan, Hairi Zamzuri. Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions. Applied Sciences. 2017; 7 (10):1050.
Chicago/Turabian StyleNurhazimah Nazmi; Mohd Azizi Abdul Rahman; Shin-Ichiroh Yamamoto; Siti Anom Ahmad; Mb Malarvili; Saiful Amri Mazlan; Hairi Zamzuri. 2017. "Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions." Applied Sciences 7, no. 10: 1050.
Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation XCorr and peak signal to noise ratio (PSNR) (ANOVA, p ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.
Noor Kamal Al-Qazzaz; Sawal Hamid Bin Mohd Ali; Siti Anom Ahmad; Mohd Shabiul Islam; Javier Escudero. Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks. Sensors 2017, 17, 1326 .
AMA StyleNoor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Mohd Shabiul Islam, Javier Escudero. Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks. Sensors. 2017; 17 (6):1326.
Chicago/Turabian StyleNoor Kamal Al-Qazzaz; Sawal Hamid Bin Mohd Ali; Siti Anom Ahmad; Mohd Shabiul Islam; Javier Escudero. 2017. "Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks." Sensors 17, no. 6: 1326.
Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers’ muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird’s eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.
E. F. Shair; S. A. Ahmad; M. H. Marhaban; S. B. Mohd Tamrin; A. R. Abdullah. EMG Processing Based Measures of Fatigue Assessment during Manual Lifting. BioMed Research International 2017, 2017, 1 -12.
AMA StyleE. F. Shair, S. A. Ahmad, M. H. Marhaban, S. B. Mohd Tamrin, A. R. Abdullah. EMG Processing Based Measures of Fatigue Assessment during Manual Lifting. BioMed Research International. 2017; 2017 ():1-12.
Chicago/Turabian StyleE. F. Shair; S. A. Ahmad; M. H. Marhaban; S. B. Mohd Tamrin; A. R. Abdullah. 2017. "EMG Processing Based Measures of Fatigue Assessment during Manual Lifting." BioMed Research International 2017, no. : 1-12.
In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
Nurhazimah Nazmi; Mohd Azizi Abdul Rahman; Shin-Ichiroh Yamamoto; Siti Anom Ahmad; Hairi Zamzuri; Saiful Amri Mazlan. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors 2016, 16, 1304 .
AMA StyleNurhazimah Nazmi, Mohd Azizi Abdul Rahman, Shin-Ichiroh Yamamoto, Siti Anom Ahmad, Hairi Zamzuri, Saiful Amri Mazlan. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors. 2016; 16 (8):1304.
Chicago/Turabian StyleNurhazimah Nazmi; Mohd Azizi Abdul Rahman; Shin-Ichiroh Yamamoto; Siti Anom Ahmad; Hairi Zamzuri; Saiful Amri Mazlan. 2016. "A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions." Sensors 16, no. 8: 1304.
We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.
Noor Kamal Al-Qazzaz; Sawal Hamid Md Ali; Siti Anom Ahmad; Mohd Shabiul Islam; Javier Escudero. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task. Sensors 2015, 15, 29015 -29035.
AMA StyleNoor Kamal Al-Qazzaz, Sawal Hamid Md Ali, Siti Anom Ahmad, Mohd Shabiul Islam, Javier Escudero. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task. Sensors. 2015; 15 (11):29015-29035.
Chicago/Turabian StyleNoor Kamal Al-Qazzaz; Sawal Hamid Md Ali; Siti Anom Ahmad; Mohd Shabiul Islam; Javier Escudero. 2015. "Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task." Sensors 15, no. 11: 29015-29035.
We survey the state of the art in a variety of force sensors for designing and application of robotic hand. Most of the force sensors are examined based on tactile sensing. For a decade, many papers have widely discussed various sensor technologies and transducer methods which are based on microelectromechanical system (MEMS) and silicon used for improving the accuracy and performance measurement of tactile sensing capabilities especially for robotic hand applications. We found that transducers and materials such as piezoresistive and polymer, respectively, are used in order to improve the sensing sensitivity for grasping mechanisms in future. This predicted growth in such applications will explode into high risk tasks which requires very precise purposes. It shows considerable potential and significant levels of research attention.
Ahmed M. Almassri; Wan Zuha Wan Hasan; S. A. Ahmad; A. J. Ishak; A. M. Ghazali; D. N. Talib; Chikamune Wada. Pressure Sensor: State of the Art, Design, and Application for Robotic Hand. Journal of Sensors 2015, 2015, 1 -12.
AMA StyleAhmed M. Almassri, Wan Zuha Wan Hasan, S. A. Ahmad, A. J. Ishak, A. M. Ghazali, D. N. Talib, Chikamune Wada. Pressure Sensor: State of the Art, Design, and Application for Robotic Hand. Journal of Sensors. 2015; 2015 ():1-12.
Chicago/Turabian StyleAhmed M. Almassri; Wan Zuha Wan Hasan; S. A. Ahmad; A. J. Ishak; A. M. Ghazali; D. N. Talib; Chikamune Wada. 2015. "Pressure Sensor: State of the Art, Design, and Application for Robotic Hand." Journal of Sensors 2015, no. : 1-12.
Ahmad Akmal Ahmad Nadzri; Mohd Hanif Mohamad Zaini; Siti Anom Ahmad; Mohd Hamiruce Marhaban; Haslina Jaafar; Sawal Hamid Md Ali. Surface Electromyography Hand Motion Classification Using Time Domain Features and Artificial Neural Network for Real Time Application. Advanced Science, Engineering and Medicine 2014, 6, 917 -920.
AMA StyleAhmad Akmal Ahmad Nadzri, Mohd Hanif Mohamad Zaini, Siti Anom Ahmad, Mohd Hamiruce Marhaban, Haslina Jaafar, Sawal Hamid Md Ali. Surface Electromyography Hand Motion Classification Using Time Domain Features and Artificial Neural Network for Real Time Application. Advanced Science, Engineering and Medicine. 2014; 6 (8):917-920.
Chicago/Turabian StyleAhmad Akmal Ahmad Nadzri; Mohd Hanif Mohamad Zaini; Siti Anom Ahmad; Mohd Hamiruce Marhaban; Haslina Jaafar; Sawal Hamid Md Ali. 2014. "Surface Electromyography Hand Motion Classification Using Time Domain Features and Artificial Neural Network for Real Time Application." Advanced Science, Engineering and Medicine 6, no. 8: 917-920.
Surface electromyography (SEMG) signals can provide important information for prosthetic hand control application. In this study, time domain (TD) features were used in extracting information from the SEMG signal in determining hand motions and stages of contraction (start, middle and end). Data were collected from ten healthy subjects. Two muscles, which are flexor carpi ulnaris (FCU) and extensor carpi radialis (ECR) were assessed during three hand motions of wrist flexion (WF), wrist extension (WE) and co-contraction (CC). The SEMG signals were first segmented into 132.5 ms windows, full wave rectified and filtered with a 6 Hz low pass Butterworth filter. Five TD features of mean absolute value, variance, root mean square, integrated absolute value and waveform length were used for feature extraction and subsequently patterns were determined. It is concluded that the TD features that were used are able to differentiate hand motions. However, for the stages of contraction determination, although there were patterns observed, it is determined that the stages could not be properly be differentiated due to the variability of signal strengths between subjects.
Ahmad Akmal Bin Ahmad Nadzri; Siti Anom Ahmad; Mohd Hamiruce Marhaban; Haslina Jaafar; Mohammad Hamiruce Marhaban. Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction. Australasian Physical & Engineering Sciences in Medicine 2014, 37, 133 -137.
AMA StyleAhmad Akmal Bin Ahmad Nadzri, Siti Anom Ahmad, Mohd Hamiruce Marhaban, Haslina Jaafar, Mohammad Hamiruce Marhaban. Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction. Australasian Physical & Engineering Sciences in Medicine. 2014; 37 (1):133-137.
Chicago/Turabian StyleAhmad Akmal Bin Ahmad Nadzri; Siti Anom Ahmad; Mohd Hamiruce Marhaban; Haslina Jaafar; Mohammad Hamiruce Marhaban. 2014. "Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction." Australasian Physical & Engineering Sciences in Medicine 37, no. 1: 133-137.