eiman kanjo | Nottingham Trent University (original) (raw)

Papers by eiman kanjo

IEEE Sensors Letters, 2024

Smart Farming is a progressive domain marked by extensive research and solutions. The predominant... more Smart Farming is a progressive domain marked by extensive research and solutions. The predominant approach involves the use of LPWANs for communication, with subsequent data transfer to a cloud server once Internet connectivity is established. In this letter, we present the design and development of a novel smart farming sensor system that combines heterogeneous short-mid range communication technologies with low-cost edge devices, sensors and a multi-hopping algorithm to transmit real-time animal alert data without internet connectivity or a cloud server. The system consists of smart collar devices for livestock, fixed gateway(s) for storage, and a mobile unit to exchange data with a farmers mobile phone. Our testing in a controlled real-world environment demonstrated the viability of such a network with over 90% real-time alerts that trigger a notification on a mobile phone in several unique test cases.

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In recent years, machine learning has developed rapidly, enabling the development of applications... more In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5-89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions.

Sensors Journal, 2020

COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the maj... more COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long-established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. The objective of this study is to investigate how a reduction in the exposure of vulnerable individuals to COVID-19 can minimise the number of deaths caused by the disease, using the UK as a case study. To overcome the limitation of long-established compartmental epidemiological models, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation (100,000 runs) along the proposed SEIR-v model is used to study the number of deaths which could be avoided as a function of the decrease in the exposure of vulnerable individuals to the disease. The results indicate a large number of deaths could be avoided by a slight realistic decrease in the exposure of vulnerable groups to the disease. The mean values across the simulations indicate 3681 and 7460 lives could be saved when such exposure is reduced by 10% and 20% respectively. From the encouraging results of the modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer.

IEEE Sensors, 2020

The ability to unobtrusively measure mental wellbeing states using non-invasive sensors has the p... more The ability to unobtrusively measure mental wellbeing states using non-invasive sensors has the potential to greatly improve mental wellbeing by alleviating the effects of high stress levels. Multiple sensors, such as electrodermal activity, heart rate and accelerometers, embedded within tangible devices pave the way to continuously and non-invasively monitor wellbeing in real-world environments. On the other hand, fidgeting tools enable repetitive interaction methods that may help to tap into individual's psychological need to feel occupied and engaged; hence potentially reducing stress. In this paper, we present the design, implementation, and deployment of Tangible Fidgeting Interfaces (TFIs) in the form of computerised iFidgetCubes. iFidgetCubes embed non-invasive sensors along with fidgeting mechanisms to aid relaxation and ease restlessness. We take advantage of our labeling techniques at the point of collection to implement multiple subject-independent deep learning classifiers to infer wellbeing. The obtained performance demonstrates that these new forms of tangible interfaces combined with deep learning classifiers have the potential to accurately infer wellbeing in addition to providing fidgeting tools.

IEEE Sensors

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2020

Mental health problems are on the rise globally and strain national health systems worldwide. Men... more Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.

arXiv:1905.00288 , 2019

Mental health problems are on the rise globally and strain national health systems worldwide. Men... more Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.

Personal and Ubiquitous Computing, 2019

In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all type... more In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all types of sensing applications and interaction methods, including mobile journaling and self-reporting to add metadata and to label sensor data streams. Mobile self-report techniques are used to record user ratings of their experiences during structured studies, instead of traditional paper-based surveys. These techniques can be timely and convenient when data are collected Bin the wild^. This paper proposes three new viable methods for mobile self-reporting projects and in real-life settings such as recording weather information or urban noise mapping. These techniques are Volume Buttons control, NFC-on-Body, and NFC-on-Wall. This work also provides an experimental and comparative analysis of various self-report techniques regarding user preferences and submission rates based on a series of user experiments. The statistical analysis of our data showed that pressing screen buttons and screen touch allowed for higher labelling rates, while Volume Buttons proved to be more valuable when users engaged in other activities, e.g. while walking. Similarly, based on participants' preferences, we found that NFC labelling was also an easy and intuitive technique when used in the context of self-reporting and place-tagging. Our hope is that by reviewing current self-reporting interfaces and user requirements, we will be able to enable new forms of self-reporting technologies that were not possible before.

Springer

Information Fusion, 2019

The detection and monitoring of emotions are important in various applications, e.g., to enable n... more The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)

Urban spaces have a great impact on how people's emotion and behaviour. There are number of facto... more Urban spaces have a great impact on how people's emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical , robust, and reliable as part of our ongoing research. In addition, we present some enabling applications using the proposed architecture.

Over the past few years, there has been a noticeable advancement in environmental models and info... more Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study 'in the wild' with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate. Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting.

—Today's mobile phone users are faced with large numbers of notifications on social media, rangin... more —Today's mobile phone users are faced with large numbers of notifications on social media, ranging from new followers on Twitter and emails to messages received from WhatsApp and Facebook. These digital alerts continuously disrupt activities through instant calls for attention. This paper examines closely the way everyday users interact with notifications and their impact on users' emotion. Fifty users were recruited to download our application NotiMind and use it over a five-week period. Users' phones collected thousands of social and system notifications along with affect data collected via self-reported PANAS tests three times a day. Results showed a noticeable correlation between positive affective measures and keyboard activities. When large numbers of Post and Remove notifications occur, a corresponding increase in negative affective measures is detected. Our predictive model has achieved a good accuracy level using three different classifiers " in the wild " (F-measure 74-78% within-subject model, 72-76% global model). Our findings show that it is possible to automatically predict when people are experiencing positive, neutral or negative affective states based on interactions with notifications. We also show how our findings open the door to a wide range of applications in relation to emotion awareness on social and mobile communication.

In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion.

We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: 1) model the short term impact of the ambient environment on human body, 2) predict emotions based on-body sensors and environmental data.

To achieve this, we have conducted a real-world study ‘in the wild’ with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models.

Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate.

Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting.

mrl.nott.ac.uk

EXECUTIVE SUMMARY City as Theatre (CAT) is one of five workpackages called “showcases” within IPe... more EXECUTIVE SUMMARY City as Theatre (CAT) is one of five workpackages called “showcases” within IPerG that demonstrate and study new examples of pervasive games. The CAT showcase is exploring artistled pervasive games, drawing on the talents of artists to create novel and compelling experiences that offer visions of how more mainstream games might be in the future. This has involved developing a prototype public performance called Day of the Figurines, a slow pervasive game in the form of a massivelymultiplayer ...

Urban spaces have a great influence on how people feel and behave. There is a number of factors t... more Urban spaces have a great influence on how people feel and behave. There is a number of factors that affect our health in outdoor space. In this paper, we objectively propose a new way to measure environmental noise impact on health and reactions in places by monitoring their physiological signals in relation to their health and wellbeing. By integrating wearable biosensors with smartphones, we will be able to get multi-devices, geo-annotated and synchronous measurements from users in crowd sourcing fashion. The data then aggregated and analyzed to visualize the body responses data by creating layers over a geographical map. Consequently, we can establish a better understanding of the interdependency between health and environmental surroundings.

The texts in mobile messages are not always easy to decipher since tone and body language is remo... more The texts in mobile messages are not always easy to decipher since tone and body language is removed from the context. Emojis offer an attractive way to express emotions to avoid misunderstandings of message tone. In this paper we shed the light on the roles of Emojis in phone notification, we conducted an in-situ study to gather phone notification data. We outline the relationship between Emojis and various social network applications including WhatsApp, Facebook and Twitter. Early results allow us to draw several conclusions in relation to number, position, type and sentimental value of Emojis. It turns out that most popular Emojis in one social app is not as popular in the others. Emojis sentimental polarity in Twitter is high and overall number of Emojis is less than Facebook. The sentimental value of Emojis is more meaningful when there are multiple Emoji in one notification.

Recently, attention has focused on the development of computer-user interfaces, which combine dig... more Recently, attention has focused on the development of computer-user interfaces, which combine digital information with physical environments. In this work we have used Computer Vision as to support the concept of marrying the digital world with the physical one. This was proven by developing a novel sensing technology that allows users to use their home computer and an web camera to detect multiple small physical objects, referred to as “Interactive Toys” (e.g. any pieces of pawns, cars, animals) in a collaborative environment, referred to as “Interactive Toys Environment” (e.g. playsets or boardgames). The study comprises the development of novel algorithms that detect the intrusion of hands into the camera’s view by monitoring users’ actions (“move and place”). When the user finishes performing the current task by withdrawing their hands from the mobile camera’s view, the system automatically responds according to the positions, directions and colours of the moved objects or toys. Toys tracking and identification are based on novel change detection algorithms that allow the system to know which toy or toys have moved in the last turn. Moreover, particular efforts were given to map the positions of the toys in the Interactive Toys Environment to the image coordinates system under various working conditions (e.g. camera vibration or background slightly moved). Usability and effectiveness of the Interactive Toys Environment are demonstrated through a number of applications of computer games used and played by children. Some of these applications were evaluated in user studies, which have shown that Interactive Toys Environment provides a new application of computer vision to facilitate natural human-computer interaction. It also encourages active thinking and social communication and improves the interaction between users and computers.

2015 International Conference on Interactive Technologies and Games, 2015

IEEE Sensors Letters, 2024

Sensors Journal, 2020

IEEE Sensors, 2020

IEEE Sensors

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2020

arXiv:1905.00288 , 2019

Mental health problems are on the rise globally and strain national health systems worldwide. Men... more Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.

Personal and Ubiquitous Computing, 2019

Springer

Information Fusion, 2019

In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion.

mrl.nott.ac.uk

2015 International Conference on Interactive Technologies and Games, 2015