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Mohsin I. Tiwana
Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan

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Journal article
Published: 31 May 2021 in Electronics
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The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.

ACS Style

Haider Javaid; Mohsin Tiwana; Ahmed Alsanad; Javaid Iqbal; Muhammad Riaz; Saeed Ahmad; Faisal Almisned. Classification of Hand Movements Using MYO Armband on an Embedded Platform. Electronics 2021, 10, 1322 .

AMA Style

Haider Javaid, Mohsin Tiwana, Ahmed Alsanad, Javaid Iqbal, Muhammad Riaz, Saeed Ahmad, Faisal Almisned. Classification of Hand Movements Using MYO Armband on an Embedded Platform. Electronics. 2021; 10 (11):1322.

Chicago/Turabian Style

Haider Javaid; Mohsin Tiwana; Ahmed Alsanad; Javaid Iqbal; Muhammad Riaz; Saeed Ahmad; Faisal Almisned. 2021. "Classification of Hand Movements Using MYO Armband on an Embedded Platform." Electronics 10, no. 11: 1322.

Journal article
Published: 30 November 2020 in Infrared Physics & Technology
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Experimentation and analysis of Functional near-infrared spectroscopy (fNIRS) in Brain-Computer Interface (BCI) has increasingly been studied as a communication possibility for patients who are severely paralyzed. This study has applied this technique to distinguish brain activities during four different mental tasks. These tasks include Mental Arithmetic (MA), Motor Imagery of Left-Hand (LHMI) and Right-Hand (RHMI) and Rest. fNIRS data used is from an open access dataset of 29 individuals which was collected by Continuous-wave imaging system (NIR Scout). In this research Data integration is performed before the data is preprocessed. Usual preprocessing is done using Butterworth filter to minimize or eliminate any unwanted signal distortion. After that an extensive signal analysis is done in which six different statistical features (Signal Mean (SM), Skewness (SK), Kurtosis (KR), Standard Deviation (SD), Signal Peak (SP), and Signal Variance (SV)) are obtained in the time domain and 13 Mel Frequency Cepstral Coefficients (MFCC) features are obtained from the frequency domain. As per literature review, MFCC has never been used as feature towards classification of fNIRS signal, which is a novel contribution towards this study. Separate Classification analysis is performed on each domain features. We were able to compare, differentiate and distinguish the brain signal activities captured while performing four different tasks using three different classifiers i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The average classification accuracy of 90.54% is achieved from K Nearest Neighbors (KNN) using the time domain features and accuracy achieved from Support Vector Machine (SVM) using the frequency domain features is 95.7%. Comparison with benchmark study shows the efficiency of MFCC as suitable features for improved classification accuracy.

ACS Style

Muhammad Saad Bin Abdul Ghaffar; Umar S. Khan; J. Iqbal; Nasir Rashid; Amir Hamza; Waqar S. Qureshi; Mohsin I. Tiwana; U. Izhar. Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC). Infrared Physics & Technology 2020, 112, 103589 .

AMA Style

Muhammad Saad Bin Abdul Ghaffar, Umar S. Khan, J. Iqbal, Nasir Rashid, Amir Hamza, Waqar S. Qureshi, Mohsin I. Tiwana, U. Izhar. Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC). Infrared Physics & Technology. 2020; 112 ():103589.

Chicago/Turabian Style

Muhammad Saad Bin Abdul Ghaffar; Umar S. Khan; J. Iqbal; Nasir Rashid; Amir Hamza; Waqar S. Qureshi; Mohsin I. Tiwana; U. Izhar. 2020. "Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)." Infrared Physics & Technology 112, no. : 103589.

Journal article
Published: 13 October 2016 in Applied Sciences
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The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker mimicking, tactile sensor is presented. Unlike previous designs reported in the literature, this sensor mimics the mouse whisker not only mechanically, but it also makes macro movements just like a real mouse whisker in a natural environment. We have developed a mathematical model and performed finite element analysis using COMSOL, in order to optimise the whisker to have the same natural frequency as that of a biological whisker. Similarly, we have developed a control system that enables the whisker mimicking sensor to vibrate at variable frequencies and conducted practical experiments to validate the response of the sensor. The natural frequency of the whisker can be designed anywhere between 35 and 110 Hz, the same as a biological whisker, by choosing different materials and physical dimensions. The control system of this sensor enables the whisker to vibrate between 5 and 236 Hz.

ACS Style

Mohsin Islam Tiwana; Moazzam Islam Tiwana; Stephen James Redmond; Nigel Hamilton Lovell; Javaid Iqbal. Bio-Inspired PVDF-Based, Mouse Whisker Mimicking, Tactile Sensor. Applied Sciences 2016, 6, 297 .

AMA Style

Mohsin Islam Tiwana, Moazzam Islam Tiwana, Stephen James Redmond, Nigel Hamilton Lovell, Javaid Iqbal. Bio-Inspired PVDF-Based, Mouse Whisker Mimicking, Tactile Sensor. Applied Sciences. 2016; 6 (10):297.

Chicago/Turabian Style

Mohsin Islam Tiwana; Moazzam Islam Tiwana; Stephen James Redmond; Nigel Hamilton Lovell; Javaid Iqbal. 2016. "Bio-Inspired PVDF-Based, Mouse Whisker Mimicking, Tactile Sensor." Applied Sciences 6, no. 10: 297.

Journal article
Published: 19 November 2014 in Elektronika ir Elektrotechnika
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ACS Style

Moazzam Islam Tiwana; Syed Junaid Nawaz; Ataul Aziz Ikram; Mohsin Islam Tiwana. Self-Organizing Networks: A Packet Scheduling Approach for Coverage/Capacity Optimization in 4G Networks Using Reinforcement Learning. Elektronika ir Elektrotechnika 2014, 20, 1 .

AMA Style

Moazzam Islam Tiwana, Syed Junaid Nawaz, Ataul Aziz Ikram, Mohsin Islam Tiwana. Self-Organizing Networks: A Packet Scheduling Approach for Coverage/Capacity Optimization in 4G Networks Using Reinforcement Learning. Elektronika ir Elektrotechnika. 2014; 20 (9):1.

Chicago/Turabian Style

Moazzam Islam Tiwana; Syed Junaid Nawaz; Ataul Aziz Ikram; Mohsin Islam Tiwana. 2014. "Self-Organizing Networks: A Packet Scheduling Approach for Coverage/Capacity Optimization in 4G Networks Using Reinforcement Learning." Elektronika ir Elektrotechnika 20, no. 9: 1.

Journal article
Published: 24 September 2013 in Journal of Network and Systems Management
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With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach.

ACS Style

Moazzam Islam Tiwana; Mohsin Islam Tiwana. A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility. Journal of Network and Systems Management 2013, 22, 235 -258.

AMA Style

Moazzam Islam Tiwana, Mohsin Islam Tiwana. A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility. Journal of Network and Systems Management. 2013; 22 (2):235-258.

Chicago/Turabian Style

Moazzam Islam Tiwana; Mohsin Islam Tiwana. 2013. "A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility." Journal of Network and Systems Management 22, no. 2: 235-258.