Title:

Wearable Systems for Health and Well-being Monitoring Applications

Presenter:

Dr. Mona Ghassemian

Time and Location:

15:30, Tuesday July 24th, 2018

Bush House (S)5.01, King’s College London

Abstract. Recent technological advancements in wireless low power/low range communication systems, MEMS technology and integrated circuits have enabled low-power, intelligent, and miniaturised sensor nodes strategically placed around the human body to be used in wearable wireless healthcare and wellbeing monitoring systems.
This seminar addresses the current state-of-art of WBANs based on the low power communication standards (such as IEEE 802.15.6, IEEE 802.15.4j and IEEE 802.11ba standards) which enable wearable systems to collect required data patterns for clinical translation. From these applications, we will abstract out the major challenges to realising the wearable wireless sensors systems for healthcare monitoring applications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in wearable healthcare systems. Prototype developments (activity recognition, Parkinson Disease, and stress monitoring) and practical research challenges for wearable wireless solutions will conclude this seminar.

M.Sc. Thesis Defense Session

in

Information Technology (Electronic Commerce)

Tittle:

Propose a method for security and protecting privacy in smart homes in the context of the internet of things

By

Masoumeh Sharafi

Supervisor:

Dr. Faranak Fotouhi

2nd Supervisor:

Dr. Mona Ghassemian

Examination Committee:

Dr. Mohsen Nikraay – Dr. Yaghoub Farjami

Location: Defense Room, School of Engineering, University of Qom.

Time: Saturday,  January 20 , 2018 (30 Dey 1396) – 11:30.

Abstract: Today, the concept of the Internet of objects has emerged with the advancement of technology and the ability to connect objects to each other and the Internet. The Internet of objects technology provides the grounds for devices to receive and process data from the environment, in addition to communication with each other and with the surrounding environment. Meanwhile, smart home is one of the most important applications in the Internet of objects domain. Smart homes allow smart objects, including environmental sensors, to receive, process and send information from the environment and send to base stations. Access to personal information of individuals should be made consciously and with their consent, otherwise the privacy of individuals will be violated. Security and privacy are one of the most important challenges in the field of smart home. Privacy is divided into two parts: privacy of data and privacy of context. Cryptography is one of the most commonly used methods for data privacy. On the other hand, sensors have limited hardware resources, power and memory constraints, as well as poor processing, hence traditional encryption methods are not suitable for these sensors. Chaos based encryption has been one of the major issues in the field of encryption in recent years. The unique features of chaos based mapping, including the sensitivity to initial conditions and the proper diffusion and distortion, have transformed these mapping into one of the options used in the design of password systems.

The current paper proposes a chaotic chip-based encryption system for wireless sensors called MBCC, which is an improved encryption algorithm based on chaos in the wireless sensor network environment called BCC. The proposed algorithm, coupled with some of the algorithms used in wireless sensor network security protocols, has been implemented in Java in the SunSPOT sensor environment. By reducing the number of calls to some operations in the MBCC algorithm, this algorithm has better relative performance indicators depending on the processor used compared to the BCC algorithm. In terms of performance in MBCC, the time and energy of the encryption operation of a 32-byte data has been reduced by about 5.4 and 5 times, respectively, compared to that of BCC. Also, RAM and ROM memory usage in cache and decryption operations in MBCC decreased by 15.09% and increased by 0.06%, respectively, compared to that of BCC. Increasing the key length from 64 bits to 128 bits has led to an increase in the robustness of the MBCC algorithm against a Brute-force attack. Finally, in this study, in addition to improving the efficiency of a chaotic encryption algorithm, various types of encryption methods are implemented and compared in the SunSPOT sensor environment.

Keywords: Internet of Things, Data Security and Privacy, SunSPOT Sensors Security, Chaos- based Block Encryption

PhD Proposal Defense Session

in

Computer Engineering (Hardware)

Tittle:

An energy-efficient and privacy preserving solution in healthcare applications of Internet of Things (IoT)

By

Mohsen Shirali

Supervisor:

Dr. Mona Ghassemian

Advisor:

Dr. Reza Khosrowabadi

Examination Committee:

Dr. Ahmad Khonsari – Dr. Mohammad Reza Daliri

Dr. Keivan Navi – Dr. Maghsoud Abbaspour – Dr. Ali Jahanian

Location: Room 117, Faculty of Computer Science and Engineering.

Time: Wednesday, January 17 , 2018 (27 Dey 1396) – 12:00.

Keywords: Emotion detection, Physical and mental health monitoring, Internet of Things (IoT), wearable devices, Privacy preservation, Energy-aware detection algorithms, Utility, Sensor, Feature extraction and selection.

Presentation by

Dr. Mona Ghassemian

Department of Computer Science and Engineering, Shahid Beheshti University

 Title

“Haptic Wearables and Applications”

Location:

Institute of Medical Science and Technology (IMSAT)

Sunday 14 January 2018 , 14:00 – 15:30

Time:

Wednesday 22nd Nov – 4:00pm (GMT)

Location:

University of Essex

Abstract: Recent technological advancements in wireless low power/low range communication systems, MicroElectroMechanical Systems (MEMS) technology and integrated circuits have enabled low-power, intelligent, miniaturised, nano-technology sensor nodes strategically placed around the human body to be used in wearable wireless healthcare monitoring systems. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardised for Internet of Things for personal health and wellbeing monitoring.

This seminar addresses the current state-of-art of WBANs based on the latest standards which enable IoT for health and wellbeing solutions with a range of representative applications. From these applications, we will abstract out the major challenges to realising the wearable wireless sensors systems for healthcare monitoring applications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in wearable systems. Prototype development and practical challenges of research for wearable wireless solutions will conclude this talk.

Bio: Mona Ghassemian received her PhD in “Mobile and Personal Communications” research from King’s College London in 2006 with NTT DoCoMo scholarship. After completion of her PhD she continued at King’s College London as a research associate in E-Sense (a project of the 6th Framework Programme of the European Commission). She worked at University of Greenwich as a senior lecturer and PG programme leader in computer networking 2007-2012. She visited a number of universities; namely UoT, TU, UCI, UPV, univ of Sydney and SUT and collaborated with their research groups. She is a faculty member at SBU and a visiting research associate at King’s College London. Her research interest is mainly on issues related to Wireless Sensor Networks with a focus on Smart Health and well-being. She is an active in IEEE UK & Ireland section and the IEEE Region 8 in various positions since 2012.

Title:

An implementation study of wearable sensors for activity recognition systems

Time and Location:

Aug 21st 2017 , 10:30 am

University of Sydney, Electrical Eng, WiNG

Abstract. This seminar addresses a number of human activity recognition (HAR) methods for a wearable sensor system. A system structure including the data acquisition solutions is discussed to provide a general view about HAR systems. Influential HAR system design issues such as attributes and sensor selections, obtrusiveness, data collection protocols, energy limitation, and user flexibility will be discussed. The HAR methods including different feature extraction and learning mechanisms are described.  To provide an overview of HAR analysis, our recent published case studies will be reviewed and evaluation techniques will be compared with an emphasis on the analysis metrics, assumptions and scenarios. This seminar aims to highlight research perspectives and open problems across different levels; i.e., hardware design, accurate measurement techniques, power efficient mechanism and reproducible case studies.

Dr Mona Ghassemian had a seminar at Ferdowsi University of Mashhad on 14 February 2017 (26 Bahman 1395)  with the title of “Wearable Wireless Healthcare Monitoring Systems”.

You can download Dr. Ghassemian presentation’s slides from here (Market-wearable sensors-14Feb2017)

M.Sc. Thesis Defense Session

in

Computer Engineering (Hardware)

Tittle:

Online EEG Signal Analysis of Collected Data from ADHD Kids

By

Farzaneh Javidmehr

1st Supervisor:

Dr. Mona Ghassemian

2nd Supervisor:

Dr. Reza Khosrowabadi

Examination Committee:

Dr. MohammadReza Daliri – Dr. HamidReza Mahdiani – Dr. Keyvan Navi

Location: Room 117, Faculty of Computer Science and Engineering.

Time: Tuesday, January 24 , 2017 (5 Bahman 1395) – 15:30.

Abstract: The brain consists of nerve cells that create the neural networks and the activity of such networks produce brain waves which are affected by various factors. Neurofeed-back is a modern approach to improve the brain’s functionality by means of self-regulation. In this approach, electrical activities of the brain are captured and demon-strated as brain waves using Brain Computer Interface (BCI) systems; in order to build a bridge between the individual and the outside environment. However, to make BCIs for therapy and neurofeedback applications, it is required to acquire, analyze and process the EEG signals. Therefore, real-time collection and processing of brain signals is the main challenge in proposing BCIs, which is chosen as the aim of this research.
In this thesis, we first review the different methods of linear and non-linear pro-cessing of brain signals, and also review classification algorithms, applied in the majority of BCI schemes; then we implement the most practiced of these algorithms in an online manner. Furthermore, we study and compare the performance of three algorithms of Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Multilayer Perceptron (MLP) for two different scenarios with real EEG data. Extracted signals for the first scenario, which is Motor Imagery (MI) are classified into two distinguished classes of right and left; meanwhile extracted signals for the second scenario, which is Steady-State Visual Evoked Potential (SSVEP) are classified into three separate classes.
Comparing the performance accuracy of these algorithms indicate that the SVM algo-rithm with RBF non-linear kernel for the motor imagery scenario and the SSVEP scenar-io has the most accuracy with 78.68% and 78.70% respectively and has the least execu-tion time after the LDA algorithm. Finally, we study two other scenarios consisting these online approaches for controlling external devices, in which the former involves communication of the MI scenario to guide a robot and the latter is a game which can be used in the treatment of children with ADHD disorder by measuring their attention lev-el.
Performance evaluation of these scenarios demonstrates the capability of such online processing methods to be applied in brain computer interfaces and neurofeedback treat-ments; and we expect a bright horizon for such technologies in near future.
Keywords: Brain Signal Processing, Online Processing, Electroencephalogram, Brain Computer Interface, Neurofeedback, Feature Classification Algorithms

M.Sc. Thesis Defense Session

in

Computer Engineering (Hardware)

Tittle:

Design and Evaluation of Smart Health Networks for Elderly and Disabled Community

By

Seyed Pourya Miralavi

Supervisor:

Dr. Mona Ghassemian

Examination Committee:

Dr. Ali Movaghar – Dr. Farshad Safaei – Dr. Maghsoud Abbaspour

Location: Room 117, Faculty of Computer Science and Engineering.

Time: Sunday, January 22 , 2017 (3 Bahman 1395) – 10:30.

Abstract: Software-Defined Networking (SDN) is an emerging research subject which is widely taken into consideration as a part of the next generation of wireless networks. These networks aim to add programmability to networking which results in high flexibility of the network. In order to do so, SDN decouples the control plane and data plane of the networks into two different layers in which control plane is responsible to take necessary network decisions and data plane is only responsible to forward packets. This simplification has resulted in new horizons towards innovation in networking. Mobility management and specially handoff decision are important challenges with the aim of providing a seamless connection for UE and the remote host in wireless networks. With the help of SDN and its centralized controller, new approaches can be held to further improve handoff decision algorithms. In this thesis, we aim to use LTE-SDN network architecture to improve handover decision algorithm by means of reducing the number of unnecessary handoffs as well as handoff delay by doing handover preparation phase (resource allocation) prior to the execution phase. Our results show 16, 24 and 20 percent improvement of performance in our proposed algorithm compared to the traditional LTE architecture with respect to handoff latency, number of handoffs and handoff signalling overhead respectively.

Keywords: Software-Defined Networking – Handoff – Long-Term Evolution