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Dr. Syed Aziz Shah
Associate Professor, Centre for Intelligent Healthcare, Coventry University, UK.

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0 Implants
0 Wireless body sensor networks
0 Intelligent healthcare
0 Disease monitoring
0 Agriculture technologies

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Machine learning for wireless sensing, radar technology

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Editorial
Published: 27 August 2021 in Micromachines
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Micro-/nano-scaled structures, materials, and devices enable the continuous monitoring of human physical activities and behaviors, as well as physiological and biochemical parameters during daily life

ACS Style

Syed Aziz Shah; Naeem Ramzan; Muhammad Ali Imran; Qammer Hussain Abbasi. Editorial for the Special Issue on Security and Sensing Devices for Healthcare Technologies. Micromachines 2021, 12, 1028 .

AMA Style

Syed Aziz Shah, Naeem Ramzan, Muhammad Ali Imran, Qammer Hussain Abbasi. Editorial for the Special Issue on Security and Sensing Devices for Healthcare Technologies. Micromachines. 2021; 12 (9):1028.

Chicago/Turabian Style

Syed Aziz Shah; Naeem Ramzan; Muhammad Ali Imran; Qammer Hussain Abbasi. 2021. "Editorial for the Special Issue on Security and Sensing Devices for Healthcare Technologies." Micromachines 12, no. 9: 1028.

Journal article
Published: 17 August 2021 in Electronics
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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

ACS Style

Abdullah Lakhan; Qurat-Ul-Ain Mastoi; Mazhar Ali Dootio; Fehaid Alqahtani; Ibrahim R. Alzahrani; Fatmah Baothman; Syed Yaseen Shah; Syed Aziz Shah; Nadeem Anjum; Qammer Hussain Abbasi; Muhammad Saddam Khokhar. Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network. Electronics 2021, 10, 1974 .

AMA Style

Abdullah Lakhan, Qurat-Ul-Ain Mastoi, Mazhar Ali Dootio, Fehaid Alqahtani, Ibrahim R. Alzahrani, Fatmah Baothman, Syed Yaseen Shah, Syed Aziz Shah, Nadeem Anjum, Qammer Hussain Abbasi, Muhammad Saddam Khokhar. Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network. Electronics. 2021; 10 (16):1974.

Chicago/Turabian Style

Abdullah Lakhan; Qurat-Ul-Ain Mastoi; Mazhar Ali Dootio; Fehaid Alqahtani; Ibrahim R. Alzahrani; Fatmah Baothman; Syed Yaseen Shah; Syed Aziz Shah; Nadeem Anjum; Qammer Hussain Abbasi; Muhammad Saddam Khokhar. 2021. "Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network." Electronics 10, no. 16: 1974.

Journal article
Published: 04 June 2021 in Sensors
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The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.

ACS Style

William Taylor; Kia Dashtipour; Syed Shah; Amir Hussain; Qammer Abbasi; Muhammad Imran. Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors 2021, 21, 3881 .

AMA Style

William Taylor, Kia Dashtipour, Syed Shah, Amir Hussain, Qammer Abbasi, Muhammad Imran. Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors. 2021; 21 (11):3881.

Chicago/Turabian Style

William Taylor; Kia Dashtipour; Syed Shah; Amir Hussain; Qammer Abbasi; Muhammad Imran. 2021. "Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning." Sensors 21, no. 11: 3881.

Journal article
Published: 02 June 2021 in Sensors
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Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.

ACS Style

Mubashir Rehman; Raza Shah; Muhammad Khan; Najah AbuAli; Syed Shah; Xiaodong Yang; Akram Alomainy; Muhmmad Imran; Qammer Abbasi. RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. Sensors 2021, 21, 3855 .

AMA Style

Mubashir Rehman, Raza Shah, Muhammad Khan, Najah AbuAli, Syed Shah, Xiaodong Yang, Akram Alomainy, Muhmmad Imran, Qammer Abbasi. RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. Sensors. 2021; 21 (11):3855.

Chicago/Turabian Style

Mubashir Rehman; Raza Shah; Muhammad Khan; Najah AbuAli; Syed Shah; Xiaodong Yang; Akram Alomainy; Muhmmad Imran; Qammer Abbasi. 2021. "RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices." Sensors 21, no. 11: 3855.

Journal article
Published: 27 May 2021 in Electronics
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Technology plays a vital role in patient rehabilitation, improving the quality of life of an individual. The increase in functional independence of disabled individuals requires adaptive and commercially available solutions. The use of sensor-based technology helps patients and therapeutic practices beyond traditional therapy. Adapting skeletal tracking technology could automate exercise tracking, records, and feedback for patient motivation and clinical treatment interventions and planning. In this paper, an exoskeleton was designed and subsequently developed for patients who are suffering from monoparesis in the upper extremities. The exoskeleton was developed according to the dimensions of a patient using a 3D scanner, and then fabricated with a 3D printer; the mechanism for the movement of the hand is a tendon flexion mechanism with servo motor actuators controlled by an ATMega2560 microcontroller. The exoskeleton was used for force augmentation of the patient’s hand by taking the input from the hand via flex sensors, and assisted the patient in closing, opening, grasping, and picking up objects, and it was also able to perform certain exercises for the rehabilitation of the patient. The exoskeleton is portable, reliable, durable, intuitive, and easy to install and use at any time.

ACS Style

Muhammad Saad bin Imtiaz; Channa Babar Ali; Zareena Kausar; Syed Shah; Syed Shah; Jawad Ahmad; Muhammad Imran; Qammer Abbasi. Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications. Electronics 2021, 10, 1279 .

AMA Style

Muhammad Saad bin Imtiaz, Channa Babar Ali, Zareena Kausar, Syed Shah, Syed Shah, Jawad Ahmad, Muhammad Imran, Qammer Abbasi. Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications. Electronics. 2021; 10 (11):1279.

Chicago/Turabian Style

Muhammad Saad bin Imtiaz; Channa Babar Ali; Zareena Kausar; Syed Shah; Syed Shah; Jawad Ahmad; Muhammad Imran; Qammer Abbasi. 2021. "Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications." Electronics 10, no. 11: 1279.

Journal article
Published: 20 May 2021 in Sensors
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Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute’s policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications.

ACS Style

Fawad Khan; Saad Khan; Shahzaib Tahir; Jawad Ahmad; Hasan Tahir; Syed Shah. Granular Data Access Control with a Patient-Centric Policy Update for Healthcare. Sensors 2021, 21, 3556 .

AMA Style

Fawad Khan, Saad Khan, Shahzaib Tahir, Jawad Ahmad, Hasan Tahir, Syed Shah. Granular Data Access Control with a Patient-Centric Policy Update for Healthcare. Sensors. 2021; 21 (10):3556.

Chicago/Turabian Style

Fawad Khan; Saad Khan; Shahzaib Tahir; Jawad Ahmad; Hasan Tahir; Syed Shah. 2021. "Granular Data Access Control with a Patient-Centric Policy Update for Healthcare." Sensors 21, no. 10: 3556.

Encyclopedia
Published: 30 March 2021 in Reference Module in Biomedical Sciences
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In this article, we discuss the recent developments of using radiofrequency (RF) and terahertz (THz) waves in the tracking the wellbeing of living organisms that include human beings and plants. We particularly focus on the accurate monitoring of human activity through RF waves found in the environment around us and sensing a plant's health by detecting the leaf water content using THz waves.

ACS Style

Qammer H Abbasi; Hasan T. Abbas; Syed A Shah; Adnan Zahid; Muhammad A Imran; Akram Alomainy. Microwave and Terahertz Sensing for Well Being. Reference Module in Biomedical Sciences 2021, 1 .

AMA Style

Qammer H Abbasi, Hasan T. Abbas, Syed A Shah, Adnan Zahid, Muhammad A Imran, Akram Alomainy. Microwave and Terahertz Sensing for Well Being. Reference Module in Biomedical Sciences. 2021; ():1.

Chicago/Turabian Style

Qammer H Abbasi; Hasan T. Abbas; Syed A Shah; Adnan Zahid; Muhammad A Imran; Akram Alomainy. 2021. "Microwave and Terahertz Sensing for Well Being." Reference Module in Biomedical Sciences , no. : 1.

Original article
Published: 10 January 2021 in International Journal of Machine Learning and Cybernetics
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The protection and privacy of the 5G-IoT framework is a major challenge due to the vast number of mobile devices. Specialized applications running these 5G-IoT systems may be vulnerable to clone attacks. Cloning applications can be achieved by stealing or distributing commercial Android apps to harm the advanced services of the 5G-IoT framework. Meanwhile, most Android app stores run and manage Android apps that developers have submitted separately without any central verification systems. Android scammers sell pirated versions of commercial software to other app stores under different names. Android applications are typically stored on cloud servers, while API access services may be used to detect and prevent cloned applications from being released. In this paper, we proposed a hybrid approach to the Control Flow Graph (CFG) and a deep learning model to secure the smart services of the 5G-IoT framework. First, the newly submitted APK file is extracted and the JDEX decompiler is used to retrieve Java source files from possibly original and cloned applications. Second, the source files are broken down into various android-based components. After generating Control-Flow Graphs (CFGs), the weighted features are stripped from each component. Finally, the Recurrent Neural Network (RNN) is designed to predict potential cloned applications by training features from different components of android applications. Experimental results have shown that the proposed approach can achieve an average accuracy of 96.24% for cloned applications selected from different android application stores.

ACS Style

Farhan Ullah; Muhammad Rashid Naeem; Leonardo Mostarda; Syed Aziz Shah. Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model. International Journal of Machine Learning and Cybernetics 2021, 1 -13.

AMA Style

Farhan Ullah, Muhammad Rashid Naeem, Leonardo Mostarda, Syed Aziz Shah. Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model. International Journal of Machine Learning and Cybernetics. 2021; ():1-13.

Chicago/Turabian Style

Farhan Ullah; Muhammad Rashid Naeem; Leonardo Mostarda; Syed Aziz Shah. 2021. "Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model." International Journal of Machine Learning and Cybernetics , no. : 1-13.

Review
Published: 03 October 2020 in Sensors
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COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

ACS Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors 2020, 20, 5665 .

AMA Style

William Taylor, Qammer H. Abbasi, Kia Dashtipour, Shuja Ansari, Syed Aziz Shah, Arslan Khalid, Muhammad Ali Imran. A Review of the State of the Art in Non-Contact Sensing for COVID-19. Sensors. 2020; 20 (19):5665.

Chicago/Turabian Style

William Taylor; Qammer H. Abbasi; Kia Dashtipour; Shuja Ansari; Syed Aziz Shah; Arslan Khalid; Muhammad Ali Imran. 2020. "A Review of the State of the Art in Non-Contact Sensing for COVID-19." Sensors 20, no. 19: 5665.

Journal article
Published: 07 September 2020 in IEEE Sensors Journal
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The field of Artificial Intelligence (AI) being applied to human motion detection is a field with considerable interest in recent years. Human motion detection deliverers the possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper will look at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this paper were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this paper is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data.

ACS Style

Syed Aziz Shah; Jawad Ahmad; Fawad Masood; Haris Pervaiz; William Taylor; Muhammad Ali Imran; Qammer H. Abbasi. Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps. IEEE Sensors Journal 2020, 21, 3669 -3679.

AMA Style

Syed Aziz Shah, Jawad Ahmad, Fawad Masood, Haris Pervaiz, William Taylor, Muhammad Ali Imran, Qammer H. Abbasi. Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps. IEEE Sensors Journal. 2020; 21 (3):3669-3679.

Chicago/Turabian Style

Syed Aziz Shah; Jawad Ahmad; Fawad Masood; Haris Pervaiz; William Taylor; Muhammad Ali Imran; Qammer H. Abbasi. 2020. "Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps." IEEE Sensors Journal 21, no. 3: 3669-3679.

Journal article
Published: 20 August 2020 in Electronics
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Human activity (HA) sensing is becoming one of the key component in future healthcare system. The prevailing detection techniques for IHA uses ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concerns as well. This paper proposes a novel, non-invasive, easily-deployable, flexible and scalable test-bed for identifying large-scale body movements based on Software Defined Radios (SDRs). Two Universal Software Radio Peripheral (USRP) models, working as SDR based transceivers, are used to extract the Channel State Information (CSI) from continuous stream of multiple frequency subcarriers. The variances of amplitude information obtained from CSI data stream are used to infer daily life activities. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis and Naïve Bayes are used to evaluate the overall performance of the test-bed. The training, validation and testing processes are performed by considering the time-domain statistical features obtained from CSI data. The K-nearest neighbour outperformed all aforementioned classifiers, providing an accuracy of 89.73%. This preliminary non-invasive work will open a new direction for design of scalable framework for future healthcare systems.

ACS Style

Aboajeila Milad Ashleibta; Adnan Zahid; Aziz Shah; Qammer H. Abbasi; Muhammad Ali Imran. Flexible and Scalable Software Defined Radio Based Testbed for Large Scale Body Movement. Electronics 2020, 9, 1354 .

AMA Style

Aboajeila Milad Ashleibta, Adnan Zahid, Aziz Shah, Qammer H. Abbasi, Muhammad Ali Imran. Flexible and Scalable Software Defined Radio Based Testbed for Large Scale Body Movement. Electronics. 2020; 9 (9):1354.

Chicago/Turabian Style

Aboajeila Milad Ashleibta; Adnan Zahid; Aziz Shah; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "Flexible and Scalable Software Defined Radio Based Testbed for Large Scale Body Movement." Electronics 9, no. 9: 1354.

Journal article
Published: 24 June 2020 in IEEE Sensors Journal
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Parkinson’s disease (PD) is a progress and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson’s patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multi-resolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ~87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ~98% using data fusion..

ACS Style

Syed Aziz Shah; Ahsen Tahir; Jawad Ahmad; Adnan Zahid; Haris Pervaiz; Aboajeila Milad Abdulhadi Ashleibta; Aamir Hasanali; Shadan Khattak; Qammer Hussain Abbasi. Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging. IEEE Sensors Journal 2020, 20, 14410 -14422.

AMA Style

Syed Aziz Shah, Ahsen Tahir, Jawad Ahmad, Adnan Zahid, Haris Pervaiz, Aboajeila Milad Abdulhadi Ashleibta, Aamir Hasanali, Shadan Khattak, Qammer Hussain Abbasi. Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging. IEEE Sensors Journal. 2020; 20 (23):14410-14422.

Chicago/Turabian Style

Syed Aziz Shah; Ahsen Tahir; Jawad Ahmad; Adnan Zahid; Haris Pervaiz; Aboajeila Milad Abdulhadi Ashleibta; Aamir Hasanali; Shadan Khattak; Qammer Hussain Abbasi. 2020. "Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging." IEEE Sensors Journal 20, no. 23: 14410-14422.

Journal article
Published: 06 May 2020 in Sensors
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Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

ACS Style

William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors 2020, 20, 2653 .

AMA Style

William Taylor, Syed Aziz Shah, Kia Dashtipour, Adnan Zahid, Qammer H. Abbasi, Muhammad Ali Imran. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors. 2020; 20 (9):2653.

Chicago/Turabian Style

William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H. Abbasi; Muhammad Ali Imran. 2020. "An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare." Sensors 20, no. 9: 2653.

Journal article
Published: 30 April 2020 in Journal of Intelligent & Fuzzy Systems
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ACS Style

Jawad Ahmad; Ahsen Tahir; Hadi Larijani; Fawad Ahmed; Syed Aziz Shah; Adam James Hall; William J. Buchanan. Energy demand forecasting of buildings using random neural networks. Journal of Intelligent & Fuzzy Systems 2020, 38, 4753 -4765.

AMA Style

Jawad Ahmad, Ahsen Tahir, Hadi Larijani, Fawad Ahmed, Syed Aziz Shah, Adam James Hall, William J. Buchanan. Energy demand forecasting of buildings using random neural networks. Journal of Intelligent & Fuzzy Systems. 2020; 38 (4):4753-4765.

Chicago/Turabian Style

Jawad Ahmad; Ahsen Tahir; Hadi Larijani; Fawad Ahmed; Syed Aziz Shah; Adam James Hall; William J. Buchanan. 2020. "Energy demand forecasting of buildings using random neural networks." Journal of Intelligent & Fuzzy Systems 38, no. 4: 4753-4765.

Special issue article
Published: 27 April 2020 in Transactions on Emerging Telecommunications Technologies
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There is a high demand for secure and reliable communications for Connected Autonomous Vehicles (CAVs) in the automotive industry. Privacy and security are key issues in CAVs, where network attacks can result in fatal accidents. The computational time, cost, and robustness of encryption algorithms are important factors in low latency 5G‐enabled secure CAV networks. The presented chaotic Tangent‐Delay Ellipse Reflecting Cavity‐Map system and PieceWise Linear Chaotic Map‐based encryption on short messages exchanged in a CAV network provide both robustness and high speed encryption. In this work, we propose a 5G radio network architecture, which leverages multiple radio access technologies and utilizes Cloud Radio Access Network functionalities for privacy preserved and secure CAV networks. The proposed Vehicular Safety Message identifier algorithm meets transmission requirements with a high probability of 85% for low round trip delay of ≤50 milliseconds. The proposed chaos‐based encryption algorithm exhibits faster speeds with a computational time of 2 to 3 milliseconds, showcasing its lightweight properties ideal for time critical applications.

ACS Style

Shuja Ansari; Jawad Ahmad; Syed Aziz Shah; Ali Kashif Bashir; Tuleen Boutaleb; Sinan Sinanovic. Chaos‐based privacy preserving vehicle safety protocol for 5G Connected Autonomous Vehicle networks. Transactions on Emerging Telecommunications Technologies 2020, 31, 1 .

AMA Style

Shuja Ansari, Jawad Ahmad, Syed Aziz Shah, Ali Kashif Bashir, Tuleen Boutaleb, Sinan Sinanovic. Chaos‐based privacy preserving vehicle safety protocol for 5G Connected Autonomous Vehicle networks. Transactions on Emerging Telecommunications Technologies. 2020; 31 (5):1.

Chicago/Turabian Style

Shuja Ansari; Jawad Ahmad; Syed Aziz Shah; Ali Kashif Bashir; Tuleen Boutaleb; Sinan Sinanovic. 2020. "Chaos‐based privacy preserving vehicle safety protocol for 5G Connected Autonomous Vehicle networks." Transactions on Emerging Telecommunications Technologies 31, no. 5: 1.

Journal article
Published: 03 April 2020 in Micromachines
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Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person’s body. The Wi-Fi signals received using non-wearable devices are converted into time–frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%.

ACS Style

Syed Aziz Shah; Jawad Ahmad; Ahsen Tahir; Fawad Ahmed; Gordon Russell; William J. Buchanan; Qammer H. Abbasi; Syed Yaseen Shah. Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication. Micromachines 2020, 11, 379 .

AMA Style

Syed Aziz Shah, Jawad Ahmad, Ahsen Tahir, Fawad Ahmed, Gordon Russell, William J. Buchanan, Qammer H. Abbasi, Syed Yaseen Shah. Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication. Micromachines. 2020; 11 (4):379.

Chicago/Turabian Style

Syed Aziz Shah; Jawad Ahmad; Ahsen Tahir; Fawad Ahmed; Gordon Russell; William J. Buchanan; Qammer H. Abbasi; Syed Yaseen Shah. 2020. "Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication." Micromachines 11, no. 4: 379.

Journal article
Published: 01 March 2020 in Symmetry
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Smart building control, managing queues for instant points of service, security systems, and customer support can benefit from the number of occupants information known as occupancy. Due to interrupted real-time continuous monitoring capabilities of state-of-the-art cameras, a vision-based system can be easily deployed for occupancy monitoring. However, processing of images or videos over insecure channels can raise several privacy concerns due to constant recording of an image or video footage. In this context, occupancy monitoring along with privacy protection is a challenging task. This paper presents a novel chaos-based lightweight privacy preserved occupancy monitoring scheme. Persons’ movements were detected using a Gaussian mixture model and Kalman filtering. A specific region of interest, i.e., persons’ faces and bodies, was encrypted using multi-chaos mapping. For pixel encryption, Intertwining and Chebyshev maps were employed in confusion and diffusion processes, respectively. The number of people was counted and the occupancy information was sent to the ThingSpeak cloud platform. The proposed chaos-based lightweight occupancy monitoring system is tested against numerous security metrics such as correlation, entropy, Number of Pixel Changing Rate (NPCR), Normalized Cross Correlation (NCC), Structural Content (SC), Mean Absolute Error (MAE), Mean Square Error (MSE), Peak to Signal Noise Ratio (PSNR), and Time Complexity (TC). All security metrics confirm the strength of the proposed scheme.

ACS Style

Jawad Ahmad; Fawad Masood; Aziz Shah; Sajjad Shaukat Jamal; Iqtadar Hussain. A Novel Secure Occupancy Monitoring Scheme Based on Multi-Chaos Mapping. Symmetry 2020, 12, 350 .

AMA Style

Jawad Ahmad, Fawad Masood, Aziz Shah, Sajjad Shaukat Jamal, Iqtadar Hussain. A Novel Secure Occupancy Monitoring Scheme Based on Multi-Chaos Mapping. Symmetry. 2020; 12 (3):350.

Chicago/Turabian Style

Jawad Ahmad; Fawad Masood; Aziz Shah; Sajjad Shaukat Jamal; Iqtadar Hussain. 2020. "A Novel Secure Occupancy Monitoring Scheme Based on Multi-Chaos Mapping." Symmetry 12, no. 3: 350.

Journal article
Published: 28 February 2020 in Entropy
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Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme.

ACS Style

Fawad Masood; Jawad Ahmad; Syed Aziz Shah; Sajjad Shaukat Jamal; Iqtadar Hussain. A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map. Entropy 2020, 22, 274 .

AMA Style

Fawad Masood, Jawad Ahmad, Syed Aziz Shah, Sajjad Shaukat Jamal, Iqtadar Hussain. A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map. Entropy. 2020; 22 (3):274.

Chicago/Turabian Style

Fawad Masood; Jawad Ahmad; Syed Aziz Shah; Sajjad Shaukat Jamal; Iqtadar Hussain. 2020. "A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map." Entropy 22, no. 3: 274.

Journal article
Published: 10 February 2020 in IEEE Access
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Shujaat Ali Khan Tanoli; Syed Aziz Shah; Muhammad Bilal Khan; Faiza Nawaz; Amir Hussain; Ahmed Y. Al-Dubai; Imran Khan; Ayoub Alsarhan. Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications. IEEE Access 2020, 8, 29395 -29406.

AMA Style

Shujaat Ali Khan Tanoli, Syed Aziz Shah, Muhammad Bilal Khan, Faiza Nawaz, Amir Hussain, Ahmed Y. Al-Dubai, Imran Khan, Ayoub Alsarhan. Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications. IEEE Access. 2020; 8 ():29395-29406.

Chicago/Turabian Style

Shujaat Ali Khan Tanoli; Syed Aziz Shah; Muhammad Bilal Khan; Faiza Nawaz; Amir Hussain; Ahmed Y. Al-Dubai; Imran Khan; Ayoub Alsarhan. 2020. "Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications." IEEE Access 8, no. : 29395-29406.

Journal article
Published: 01 December 2019 in Electronics
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Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson’s Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time–frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.

ACS Style

Ahsen Tahir; Jawad Ahmad; Syed Aziz Shah; Gordon Morison; Dawn A. Skelton; Hadi Larijani; Qammer H. Abbasi; Muhammad Ali Imran; Ryan M. Gibson. WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning. Electronics 2019, 8, 1433 .

AMA Style

Ahsen Tahir, Jawad Ahmad, Syed Aziz Shah, Gordon Morison, Dawn A. Skelton, Hadi Larijani, Qammer H. Abbasi, Muhammad Ali Imran, Ryan M. Gibson. WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning. Electronics. 2019; 8 (12):1433.

Chicago/Turabian Style

Ahsen Tahir; Jawad Ahmad; Syed Aziz Shah; Gordon Morison; Dawn A. Skelton; Hadi Larijani; Qammer H. Abbasi; Muhammad Ali Imran; Ryan M. Gibson. 2019. "WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning." Electronics 8, no. 12: 1433.