Farzan Majeed Noori

• Noori, Farzan Majeed; Hafeez, Azeem; Malik, Hafiz; Uddin, Md Zia & Tørresen, Jim (2023). Source Linking Framework in Vehicular Networks for Security of Electric Vehicles using Machine Learning. In Coleri, Sinem; Altintas, Onur & Kargl, Frank (Ed.), 2023 IEEE Vehicular Networking Conference (VNC) proceedings. IEEE conference proceedings. ISSN 979-8-3503-3549-1. p. 207–214. doi: 10.1109/VNC57357.2023.10136272.
• Majeed, Aaqib; Golsanami, Naser; Gong, Bin; Ahmad, Qazi Adnan; Rifaqat, Samia & Zeeshan, Ahmad [Show all 7 contributors for this article] (2022). Analysis of thermal radiation in magneto-hydrodynamic motile gyrotactic micro-organisms flow comprising tiny nanoparticle towards a nonlinear surface with velocity slip. Alexandria Engineering Journal. ISSN 1110-0168. doi: 10.1016/j.aej.2022.11.012.
• Mantelli, Mathias; Noori, Farzan Majeed; Pittol, Diego; Maffei, Renan; Tørresen, Jim & Kolberg, Mariana (2022). Semantic Temporal Object Search System Based on Heat Maps. Journal of Intelligent and Robotic Systems. ISSN 0921-0296. 106(69). doi: 10.1007/s10846-022-01760-8.
• Majeed, Aaqib; Rifaqat, Samia; Zeeshan, Ahmad; Alhodaly, Mohammed Sh. & Noori, Farzan Majeed (2022). Impact of velocity slip and radiative magnetized Casson nanofluid with chemical reaction towards a nonlinear stretching sheet: Three-stage Lobatto collocation scheme. International Journal of Modern Physics B. ISSN 0217-9792. doi: 10.1142/S0217979223500881.
• Majeed, Aaqib; Zeeshan, Ahmad; Ali, Sajjad & Noori, Farzan Majeed (2022). CONSEQUENCE OF THERMOPHORESIS AND BROWNIAN MOTION ON THREE-DIMENSIONAL MAGNETOHYDRODYNAMIC CASSON NANO-FLUID FLOW WITH HEAT TRANSFER ANALYSIS. Transactions of A. Razmadze Mathematical Institute. ISSN 2346-8092. 176(1), p. 57–72.
• Bordvik, David Andreas; Hou, Jie; Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2022). Monitoring In-Home Emergency Situation and Preserve Privacy using Multi-modal Sensing and Deep Learning, 2022 International Conference on Electronics, Information, and Communication (ICEIC 2022) . IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665409353. doi: 10.1109/ICEIC54506.2022.9748829. Full text in Research Archive Show summary

  Videos and images are commonly used in home monitoring systems. However, detecting emergencies in-home while preserving privacy is a challenging task concerning Human Activity Recognition (HAR). In recent years, HAR combined with deep learning has drawn much attention from the general public. Besides that, relying entirely on a single sensor modal-ity is not promising. In this paper, depth images and radar presence data were used to investigate if such sensor data can tackle the challenge of a system's ability to detect abnormal and normal situations while preserving privacy. The recurrence plots and wavelet transformations were used to make a two-dimensional representation of the presence radar data. Moreover, we fused data from both sensors using data-level, feature-level, and decision-level fusions. The decision-level fusion showed its superiority over the other two techniques. For the decision-level fusion, a combination of the depth images and presence data recurrence plots trained first on convolutional neural networks (CNN). The output was fed into support vector machines, which yielded the best accuracy of 99.98%.

• Riaz, Aqsa; Hassan, Qazi Mahmood Ul; Abbas, Tasawar; Ghachem, Kaouther; Majeed, Aaqib & Noori, Farzan Majeed [Show all 7 contributors for this article] (2022). A study on effectiveness of the variational theory in fluid dynamics applications. Alexandria Engineering Journal. ISSN 1110-0168. 61(12), p. 10779–10789. doi: 10.1016/j.aej.2022.04.021.
• Majeed, Aaqib; Zeeshan, Ahmad; Shaheen, Aqila; Alhodaly, Mohammed Sh. & Noori, Farzan Majeed (2022). Hall Current and Viscous Dissipation Impact on MHD Mixed Convection Flow towards a Porous Exponentially Surface with its Engineering Applications. Journal of magnetics. ISSN 1226-1750. 27(2), p. 223–231. doi: 10.4283/JMAG.2022.27.2.223.
• Lamo, Yngve; Mukhiya, Suresh Kumar; Rabbi, Fazle; Aminifar, Amin; Lillehaug, Svein Ivar & Tørresen, Jim [Show all 25 contributors for this article] (2022). Towards adaptive technology in routine mental health care. Digital Health. 8, p. 1–16. doi: 10.1177/20552076221128678. Full text in Research Archive Show summary

  This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients’ needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.

• Pham, Hoang Minh; Noori, Farzan Majeed & Tørresen, Jim (2021). Emotion Recognition using Speech Data with Convolutional Neural Network. In Bouani, Faouzi (Eds.), 2021 International Conference on Signal, Control and Communication (SCC). IEEE conference proceedings. ISSN 9781728135977. Full text in Research Archive
• Khan, Haroon; Noori, Farzan Majeed; Yazidi, Anis; Uddin, Md Zia; Khan, M.N Afzal & Mirtaheri, Peyman (2021). Classification of Individual Finger Movements from Right Hand Using fNIRS Signals. Sensors. ISSN 1424-8220. 21(23), p. 1–15. doi: 10.3390/s21237943. Full text in Research Archive
• Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2021). Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning. IEEE Access. ISSN 2169-3536. 9, p. 138132–138143. doi: 10.1109/ACCESS.2021.3117667. Full text in Research Archive Show summary

  With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare.

• Nordgreen, Tine; Rabbi, Fazle; Tørresen, Jim; Skar, Yngvar Sigmund; Guribye, Frode & Inal, Yavuz [Show all 23 contributors for this article] (2021). Challenges and possible solutions in cross-disciplinary and cross-sectorial research teams within the domain of e-mental health. Journal of Enabling Technologies (JET). ISSN 2398-6263. 15(4), p. 241–251. doi: 10.1108/JET-03-2021-0013. Full text in Research Archive Show summary

  Mental illness presents a huge individual, societal and economic challenges, currently accounting for 20% of the worldwide burden of disease. There is a gap between the need for and access to services. Digital technology has been proven effective in e-mental health for preventing and treating mental health problems. However, there is a need for cross-disciplinary efforts to increase the impact of e-mental health services. This paper aims to report key challenges and possible solutions for cross-disciplinary and cross-sectorial research teams within the domain of e-mental health.

• Adnan Ahmad, Qazi; Irfan Ehsan, Muhammad; Khan, Nasir; Majeed, Aaqib; Zeeshan, Ahmed & Ahmad, Rafique [Show all 7 contributors for this article] (2021). Numerical simulation and modeling of a poroelastic media for detection and discrimination of geo-fluids using finite difference method. Alexandria Engineering Journal. ISSN 1110-0168. 61(5), p. 3447–3462. doi: 10.1016/j.aej.2021.08.064.
• Nazeer, Hammad; Naseer, Noman; Khan, Rayyan Azam; Noori, Farzan Majeed; Qureshi, Nauman Khalid & Khan, Umar Shahbaz [Show all 7 contributors for this article] (2020). Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis. Journal of Neural Engineering. ISSN 1741-2560. 17(5). doi: 10.1088/1741-2552/abb417.
• Khan, Rayyan Azam; Naseer, Noman; Saleem, Sajid; Qureshi, Nauman Khalid; Noori, Farzan Majeed & Khan, Muhammad Jawad (2020). Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study. Journal of Healthcare Engineering. ISSN 2040-2295. 2020, p. 1–15. doi: 10.1155/2020/9152369.
• Uddin, Md Zia; Noori, Farzan Majeed & Tørresen, Jim (2020). In-Home Emergency Detection Using an Ambient Ultra-Wideband Radar Sensor and Deep Learning. In Noori, Farzan Majeed (Eds.), IEEE 10th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7314-6. p. 1089–1093. doi: 10.1109/UkrMW49653.2020.9252708. Show summary

  Human behavior analysis in indoor environments from ambient sensor data is one of the most challenging human-machine interaction research topics. In this paper, we present a deep learning-based novel approach for human posture recognition and emergency detection using an ultra-wideband radar sensor. The strength of this sensor is that it collects less privacy-related information than a regular camera. The sensor is installed on a mobile robot to observe a subject from a short distance. At first, raw data is collected from the sensor. Then, the data is used to train a Recurrent Neural Network (RNN) for modeling of different human activities and conditions, including normal and emergency conditions. Finally, the trained model is used for testing the model on new inputs. The experiments were performed using two datasets recorded in lab environments, and the proposed approach produced better recognition performance than the conventional ones. The proposed method can be applied in many prominent research fields (e.g., human-robot interaction) for different practical applications such as mobile robots for eldercare.

• Svoren, Henrik; Lasantha Bandara Thambawita, Vajira; Halvorsen, Pål; Jakobsen, Petter; Garcia-Ceja, Enrique & Noori, Farzan Majeed [Show all 10 contributors for this article] (2020). Toadstool: a dataset for training emotional intelligent machines playing Super Mario Bros. In Alay, Özgü & Toni, Laura (Ed.), MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference. Association for Computing Machinery (ACM). ISSN 978-1-4503-6845-2. p. 309–314. doi: 10.1145/3339825.3394939.
• Noori, Farzan; Riegler, Michael; Uddin, Md Zia & Tørresen, Jim (2020). Human Activity Recognition from Multiple Sensors Data Using Multi-Fusion Representations and CNNs. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP). ISSN 1551-6857. 16(2). doi: 10.1145/3377882. Show summary

  With the emerging interest in the ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual’s behavioral pattern (e.g., physical activity, location, and mood) by using sensors embedded in smart-watches and smartphones. The multi-sensor environments also come with some challenges, such as how to fuse and combine different sources of data. In this article, we explore several methods of fusion for multi-representations of data from sensors. Furthermore, multiple representations of sensor data were generated and then fused using data-level, feature-level, and decision-level fusions. The presented methods were evaluated using three publicly available human activity recognition (HAR) datasets. The presented approaches utilize Deep Convolutional Neural Networks (CNNs). A generic architecture for the fusion of different sensors is proposed. The proposed method shows promising performance, with the best results reaching an overall accuracy of 98.4% for the Context-Awareness via Wrist-Worn Motion Sensors (HANDY) dataset and 98.7% for the Wireless Sensor Data Mining (WISDM version 1.1) dataset. Both results outperform previous approaches.

• Majeed, Aaqib; Zeeshan, Ahmed; Noori, Farzan Majeed & Masud, Usman (2019). Influence of rotating magnetic field on Maxwell saturated ferrofluid flow over a heated stretching sheet with heat generation/absorption. Mechanics and Industry (M&I). ISSN 2257-7777. 20(5). doi: 10.1051/meca/2019022.
• Majeed, A; Zeeshan, A & Noori, Farzan Majeed (2019). Numerical study of Darcy-Forchheimer model with activation energy subject to chemically reactive species and momentum slip of order two. AIP Advances. ISSN 2158-3226. 9(4). doi: 10.1063/1.5095546.
• Majeed, Aaqib; Zeeshan, Ahmad & Noori, Farzan Majeed (2019). Analysis of chemically reactive species with mixed convection and Darcy–Forchheimer flow under activation energy: a novel application for geothermal reservoirs. Journal of Thermal Analysis and Calorimetry. ISSN 1388-6150. doi: 10.1007/s10973-019-08978-z.
• Frogner, Joakim Ihle; Noori, Farzan Majeed; Halvorsen, Pål; Hicks, Steven; Garcia-Ceja, Enrique & Tørresen, Jim [Show all 7 contributors for this article] (2019). One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression. In Boll, Susanne; Lee, Jeannie T; Meyer, Jochen; Nag, Nitish & O’Connor, Noel E. (Ed.), HealthMedia '19: Proceedings of the 4th International Workshop on Multimedia for Personal Health & Health Care. Association for Computing Machinery (ACM). ISSN 978-1-4503-6914-5. p. 9–15. doi: 10.1145/3347444.3356238. Show summary

  Nowadays, it has become possible to measure different human activities using wearable devices. Besides measuring the number of daily steps or calories burned, these datasets have much more potential since different activity levels are also collected. Such data would be helpful in the field of psychology because it can relate to various mental health issues such as changes in mood and stress. In this paper, we present a machine learning approach to detect depression using a dataset with motor activity recordings of one group of people with depression and one group without, i.e., the condition group includes 23 unipolar and bipolar persons, and the control group includes 32 persons without depression. We use convolutional neural networks to classify the depressed and nondepressed patients. Moreover, different levels of depression were classified. Finally, we trained a model that predicts MontgomeryÅsberg Depression Rating Scale scores. We achieved an average F1-score of 0.70 for detecting the control and condition groups. The mean squared error for score prediction was approximately 4.0.

• Noori, Farzan Majeed; Kahlon, Smiti ; Lindner, Philip; Nordgreen, Tine; Tørresen, Jim & Riegler, Michael (2019). Heart Rate Prediction from Head Movement during Virtual Reality Treatment for Social Anxiety, Proceedings of Content Based Multimedia Information (CBMI 2019). IEEE conference proceedings. ISSN 978-1-7281-4673-7. doi: 10.1109/CBMI.2019.8877454.
• Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2019). Robot-Care for the Older People: Ethically Justified or Not? Proceedings of the 9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-8129-9. p. 43–47. doi: 10.1109/DEVLRN.2019.8850706. Show summary

  With the growing popularity of robotics technology, robots have also been introduced to take care of older people. Robots can assist seniors in their daily life tasks, monitor their health or provide them companionship. Despite the benefits of robots, several ethical concerns arise in elderly-robot interaction. The old people might get lonely due to less human interaction, or they feel less control in their lives. The elderly might lose their personal liberty. In this paper, we try to highlight the ethical concerns and provide some preliminary suggestions which would be helpful to reduce the ethical concerns by using customized systems with proper guidelines and consultation with older people.

• Noori, Farzan Majeed; Garcia-Ceja, Enrique; Uddin, Md Zia; Riegler, Michael & Tørresen, Jim (2019). Fusion of Multiple Representations Extracted from a Single Sensor’s Data for Activity Recognition Using CNNs. In Jayne, Chrisina & Somogyvári, Zoltán (Ed.), 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-1985-4. doi: 10.1109/IJCNN.2019.8851898.
• Noori, Farzan Majeed; Wallace, Benedikte; Uddin, Md Zia & Tørresen, Jim (2019). A Robust Human Activity Recognition Approach Using OpenPose, Motion Features, and Deep Recurrent Neural Network. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11482 LNCS, p. 299–310. doi: 10.1007/978-3-030-20205-7_25.
• Majeed, Aaqib; Noori, Farzan Majeed; Zeeshan, A; Mehmood, T; Rehmand, SU & Khan, I (2018). Analysis of activation energy in magnetohydrodynamic flow with chemical reaction and second order momentum slip model. Case Studies in Thermal Engineering. ISSN 2214-157X. 12, p. 765–773. doi: 10.1016/j.csite.2018.10.007. Full text in Research Archive

View all works in Cristin

• Noori, Farzan Majeed (2020). IEEE 10th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS). IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-7281-7314-6. 1145 p.

View all works in Cristin

• Khan, Haroon; Noori, Farzan Majeed; Naseer, Noman; Qureshi, Nauman Khalid; Nazeer, Hammad & Mirtaheri, Peyman (2021). Classification of Individual Finger Movements using fNIRS for BCI Applications.
• Noori, Farzan Majeed (2021). Recognition of Human Activities using UWB Radar and Deep Learning. Show summary

  The population of older adults are increasing every day, and the trend is expected to continue. With the help of advanced assistive technologies, we can provide them better healthcare. In this work, we provide a novel sensing approach based on Ultra-wideband (UWB) sensors to recognize human activities and forecast future events. Previously, researchers focused on wearable or video sensors to detect a person’s behaviour. However, it is challenging for older people to wear devices 24/7. Similarly, vision-based sensors always carry privacy concerns. In contrast, a non-contact ambient sensor with no such privacy issues is XeThru ultra-wideband (UWB) radar. We collected the data using multiple modalities, such as UWB, depth images, thermal images, and actigraphy device, i.e. to collect heart rate (HR) for ground truth. The dataset comprised of the participants sitting on the sofa in normal situation. Afterwards, we recommended participants do some exercise for the sake of increasing HR. When the HR increased by more than 140 BPM, the users lie down on the floor in front of sensors until their HR reached a normal level. In this research, we classify normal vs abnormal situations using CNNs and LSTMs. Accuracy, precision, and recall are used as performance measures. We got 95% and 98% accuracies using CNNs and LSTMs, respectively. Furthermore, seven classes were introduced based on HR levels, as shown in Table1. We got promising results with LSTMs. A confusion matrix is shown in Figure 1. It was the first step towards classifying activities using UWB radar. In the future, we are planning to predict the future HR based only on UWB data, as it would be challenging for older people to wear actigraphy devices. Moreover, we will include other modalities which we did not include in our preliminary analysis.

• Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2020). Presentation on In-Home Emergency Detection Using an Ambient Ultra-Wideband Radar Sensor and Deep Learning.
• Noori, Farzan Majeed (2019). A Robust Human Activity Recognition Approach Using OpenPose, Motion Features, and Deep Recurrent Neural Network.
• Noori, Farzan Majeed (2019). Robot-Care for the Older People: Ethically Justified or Not?
• Noori, Farzan Majeed; Tørresen, Jim; Uddin, Md Zia & Riegler, Michael A. (2023). Multimodal Deep Learning Approaches for Human Activity recognition. Universitetet i Oslo. ISSN 1501-7710. Full text in Research Archive Show summary

  Smart homes may be beneficial for people of all ages, but this is especially true for those with care needs, such as the elderly. To assist, monitor for emergencies, and provide companionship for the elderly, a substantial amount of research on human activity recognition systems has been conducted. Several algorithms for activity recognition and prediction of future events have been reported in the scientific literature. However, the majority of published research does not address privacy concerns or employ a variety of ambient sensors. The objective of this thesis is to contribute to the progress in research relevant to activity recognition systems that use sensors that collect less privacy-related information. The following tasks are included in the work: assessment of sensors while keeping privacy concerns in mind, selection of cutting-edge classification methods, and how to fuse the data from multiple sensors. This thesis contributes to making progress on systems for analyzing human activity and state—or vital signs—for application in a mobile robot. This dissertation examines two topics. First, it examines the privacy concerns associated with having a robot in the home. On a robot, an ultra-wideband (UWB) radar-based sensor and an RGB camera (for ground truth) were installed. An actigraphy device was also worn by the users for heart rate monitoring. The UWB sensor was selected to maintain privacy while monitoring human activities. Considering different ways to represent data from a single sensor is the second topic under investigation. That is, how data from multiple representations can be combined. For this purpose, we investigate various data representations from a single sensor’s data and analysis using cutting-edge deep learning algorithms. The contributions provide considerations for equipping a mobile home robot with activity recognition abilities while reducing the amount of privacy-sensitive sensor data. The work also concerns examining the potential privacy restrictions that must be established for the analyzing systems. The thesis contains new methods for combining data from multiple information sources. To achieve our objective, convolutional neural networks and recurrent neural networks were applied and validated using conventional methods. The conclusion of the thesis is that we can achieve good accuracy with limited sensors while maintaining privacy. It is, however, likely adequate for assisting healthcare personnel and caregivers in their work by indicating current activity status and measuring activity levels, providing alerts about abnormal activities. The results can hopefully contribute to older people being able to live alone in their homes with a larger chance of any unwanted events being quickly detected and notified to the caregivers and providers.

• Wang, Lina; Tørresen, Jim & Noori, Farzan Majeed (2021). Emergency Monitoring of Older People Using Multiple Sensors with Privacy in Consideration. Universitetet i Oslo.

View all works in Cristin