Construction Upskill

The Construction Upskill App has been developed for the construction sector. It provides easy and instant access to training and CPD for construction workers. The current training available consists of four short modules co-designed with the sector to provide the construction sector with insights into collaboration and innovation and how each can be effective in driving productivity in the workplace.

• Client: Innovation Driven Procurement
• Category: Education

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MEMO Project Inscription

MEMO Project Inscription: Make your mark for biodiversity allows you to draw your own virtual inscription. In doing so, you will help to explore the profound threats posed to modern biodiversity by the actions of humanity, identify emerging solutions and innovations, and provide hope for a future that is safe, joyful and fulfilling.

We're on the crest of a breaking wave of a mass extinction event, unlike anything that has been seen for 65 million years. Eden Portland will be an underground cathedral where the ancient art of stone carving and cutting edge technology will combine to breathe life into stories of biodiversity, extinction and evolution.

Above ground, the natural beauty of the Jurassic Coast stretches as far as the eye can see but, out of the sun, the artificially-lit tunnels of Bowers Mine resemble a deserted underground city, criss-crossed with roads and flanked with towering slabs of limestone. The tunnels are cut from the same Portland stone whose embedded fossils inspired the first theories of extinction in the 17th century. The stone, formed 145 million years ago, has been used by stonemasons since at least Roman times and can be found on thousands of buildings across the UK and around the world, from the Tower of London and St Paul's Cathedral in London, to the United Nations Headquarters in New York.

• Client: Eden Project Portland
• Category: Education

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Tag With Me

Tag in the Park & Tag 4 Active Lives

The ubiquity of mobile devices make them ideal platforms for exercise games (exergames) to promote physical activities. Advances in Internet of Things (IoT) technologies including Bluetooth Low Energy (BLE) beacons can be utilised for proximity detection to promote physical activities and the use of Artificial Intelligence (AI) in the form of object recognition can accelerate engagement with location-based pervasive games.

We are currently implementing a casual exergame, Tag With Me, in the form of a treasure hunt that provides the approximate location of nearby points of interest in real-time within the vicinity of Bluetooth beacons. The system exploits the signal strength of the BLE beacons to measure proximity which makes it suitable for outdoor and indoor functioning where GPS signals are not accessible. Once the player walks towards a point of interest one of the interactive challenges is activated such as the AI and camera challenges where the player must scan the nearby object with their smartphone camera, the tag challenge where the player must search for a tag and tap it or an education quiz.

• Client: Rufford Abbey Country Park, Highbury Hospital
• Category: Health and Wellbeing

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DigitalExposome

Multimodel Sensor Fusion Approach to study the impact of Environment Pollutants on Mental Wellbeing

The short and long term exposure to environmental urban factors (such as air pollution, gases, particulates and noise) can significantly impact an individual’s wellbeing and mental health. The World Health Organisation (WHO) found that 91% of people are living in places where the air quality guidelines are not met and the use of non-clean fuels and household emissions in the atmosphere are causing over 4.2 million deaths each year. In addition, those living in some locations in the UK have a higher risk of developing serious health conditions such as higher heart rate, asthma and cardio-cerebrovascular disease where a lifetime of exposure to high-levels of pollution can result in reduced life expectancy.

Repeated and continuous human exposure to the environment and highconcentrated air pollutants have been found to increase the risk of developing serious conditions such as respiratory and cardiovascular diseases or even death. Research recently has began focusing towards how the environment can impact physical health but it also is necessary to explore how the environment can impact mental wellbeing. Pollution within the urban environment is a continual problem contributing to rising health and mental wellbeing challenges. The ability to monitor air pollutants, physiology and mental wellbeing will enable the relationship between repeated environment exposures and mental wellbeing to be established.

The the term ’DigitalExposome’ as a framework to quantify an individual’s exposure to the environment by utilising a range of technological, mobile-sensing and digital devices. The concept aims to measure multiple environmental factors using mobile technologies and then quantify them in real-life settings. Combining multiple data collection methods helps to support DigitalExposome and gain a better understanding into how exposures to the environment can impact mental wellbeing.

Voronoi visualisations have given an indication of how changes within the environment can have an impact on mental wellbeing. Typically, it was found that where air pollution such as PM1, 2.5, 10 and Noise was increasing, participants labelled their wellbeing as very negative. This demonstrates consistent results with previous studies in this area. This form of spatial analysis, greatly helps in understanding the degree to which a place is similar to other nearby places.

Statistical analysis including PCA, Multi variant Linear Regression, Voronoi and data spatial visualisations were implemented to explore the variation in data and the factor importance. We found that physiological (on-body) sensor data is directly correlated to pollution (PM in particular) within the environment. In addition, DBNs have helped successfully classify five states of wellbeing with up to 80.8% accuracy using the fused physiological and pollution data.

• Category: Digital Monitoring

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Tangible Fidgeting Interfaces

Tangible Fidgeting Interfaces for Mental Wellbeing Recognition using Deep Learning applied to Physiological Sensor Data

The momentary assessment of an individual's affective state is critical to the monitoring of mental wellbeing and the ability to instantly apply interventions. This research introduced the concept of tangible fidgeting interfaces for affective recognition from design and development through to evaluation. Tangible interfaces expand upon the affordance of familiar physical objects as the ability to touch and fidget may help to tap into individuals' psychological need to feel occupied and engaged. Embedding digital technologies within interfaces capitalises on motor and perceptual capabilities and allows for the direct manipulation of data, offering people the potential for new modes of interaction when experiencing mental wellbeing challenges.

Tangible interfaces present an ideal opportunity to digitally enable physical fidgeting interactions along with physiological sensor monitoring to unobtrusively and comfortable measure non-visable changes in affective state. This opportunity initiated the investigation of factors that would bring about the designing of more effective intelligent solutions using participatory design techniques to engage people in designing solutions relevant to themselves.

Adopting an artificial intelligence approach using physiological signals created the possibility to quantify affect with high levels of accuracy. However, labelling is an indispensable stage of data pre-processing that is required before classification and can be extremely challenging with multi-model sensor data. LabelSens introduced new techniques for labelling at the point of collection using five custom built tangible labelling interfaces.

When classifying labelled physiological sensor data, individual differences between people limit the generalisability of models. To address this challenge, a transfer learning approach has been developed that personalises affective models using few labelled samples. This approach to personalise models and improve cross-domain performance is completed on-device, automating the traditionally manual process, saving time and labour. Furthermore, monitoring trajectories over long periods of time inherits some critical limitations in relation to the size of the training dataset. This shortcoming may hinder the development of reliable and accurate machine learning models. A second framework has been developed to overcome the limitation of small training datasets using an image-encoding transfer learning approach.

This research offered the first attempt at the development of tangible interfaces using artificial intelligence towards building a real-world continuous affect recognition system in addition to offering real-time feedback to perform as interventions. This exploration of affective interfaces has many potential applications to help improve quality of life for the wider population.

• Category: Wellbeing Monitoring

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DigitalPPE

The COVID-19 Pandemic brought about closure to work spaces, public venues and academic environments. These works investigated a solution that enables a safe exit strategy out of restrictions. Through the use of an IoT based Bluetooth Low Energy (BLE) device, individuals were reminded of the social distancing measures, facilitated through visual and haptic feedback. The device also enabled individuals to monitor close contacts, recorded on a database. The technology developed provides the additional benefit of no further equipment for contract tracing being provided. It also provides real-time alerts for larger gatherings, something that alternative systems of the time lacked in.

• Category: Health and Safety

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Prof. Eiman Kanjo

eiman.kanjo@ntu.ac.uk

Professor of Pervasive Sensing

Prof. Kanjo is currently a Professor of Pervasive Sensing at the Computer Science Department. She conducts research in Mobile Sensing, Pervasive Computing, Affective Computing and Data Science. Eiman currently leads the Smart Sensing and IoT Lab/team(Winner of the Vice Chancellor's Outstanding Research Team Award 2020/2021).

Prof. Kanjo was first to coin the phrase 'Mobile Sensing' and wrote some of the earliest papers on the subject ( GeoMobSens and Mobsens). She also built the first noise monitoring system using the phone-based microphone (NoiseSpy). Her current work (EnvBodySens) on studying and quantifying the impact of Environment on wellbeing using Mobile Sensing, Deep Learning, Data Science and AI, complements her work on Urban Computing in order to make sense of a place ( NeuroPlace, ShopMobia).

Eiman is also an expert in developing digital technologies for Mental Health and she has been involved in a wide range of projects in this area, including (NotiMind). She works closely with Mental Health networks and charities and currently developing novel Fidgeting Interfaces to reduce Anxiety and Stress among adult and school children. She often employs data science and on-device processing (on Edge Computing) to create privacy preserving pervasive tools that can transform wellbeing.

Eiman's team is active in developing tools and solutions to minimise the risk of COVID19, and recently developed a low-cost wearable for social distancing and contact tracing.

Awards:
• Vice Chancellor's Outstanding Research Team Award 2020/2021
• Top 50 Women in Engineering

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Thomas Johnson

thomas.johnson@ntu.ac.uk

Lecturer

Thomas is an Academic Associate (part-time lecturer) and PhD candidate in Pervasive Computing and Data Science within the Smart Sensing Lab.

Prior to this he was awarded a BSc (Hons) Information and Communications Technology in 2016 and a MSc. Computing Systems in 2017 both from Nottingham Trent University. He went on to be awarded in 2018 a PGCE in Primary Education within the Wider Curriculum with Qualified Teaching Status (QTS) from the University of Derby.

He has recently coined the phrase ' DigitalExposome ' as a method of digitalising the Exposome Concept. Within his PhD studies, he has designed, developed and evaluated Environmental Monitoring Stations (fixed and portable) and wellbeing wristbands to observe physiological changes. Thomas has also presented his work at the International Conference IEEE Smart Cities 2021 .

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Dr. Kieran Woodward

kieran.woodward@ntu.ac.uk

Research Associate with a PhD in the field of Pervasive Computing and Data Science

Kieran has recently completed his PhD in pervasive computing and data science at Nottingham Trent university. He is currently a Research Associate in Pervasive Computing within the Smart Sensing Lab, researching the use of IoT technologies and artificial intelligence for pervasive games including ‘Tag in the Park’ as part of the 5G Connected Forest. Kieran has also previously explored trust within Navy AI systems and developing low-cost wearable devices to promote social distancing and aid contact tracing to help tackle COVID-19.

Kieran’s PhD researched the use of tangible edge computing interfaces to infer mental wellbeing states using novel deep learning approaches. During his PhD he designed, developed and evaluated tangible fidgeting interfaces that combined real-time physiological sensor data collection with on-device processing. During this time, he conducted studies and experiments with users to co-design tangible interfaces and collect real-world user data while also developing transfer learning classification approaches to personalise affective models and improve performance with scarce data. Kieran gained numerous journal publications including a review paper in Transactions on Affective Computing exploring mental wellbeing technologies. Kieran has also presented his work at international conferences including Chi 2019 , UbiComp 2020 and IEEE Smart Cities 2021 .

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William Parker

william.parker@ntu.ac.uk

Research Assistant

Will is a member of the Smart Sensing Lab and is currently part of the 5G Connected Forest team, researching the use of IoT technologies for pervasive games, including an alternative version of the ‘Tag in the Park’ app created by Kieran Woodward.

Will graduated NTU in June 2021 with a degree in Digital Media Technologies. His final piece of work for the degree was working closely with Thomas Johnson, developing a website to display data being retrieved by his Environmental Monitoring Stations. He continues to work with Tom, further enhancing the websites capabilities and visualisations ready for deployment.

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Bradley Patrick

bradley.patrick@ntu.ac.uk

Academic Associate

Bradley is an Academic Associate at Nottingham Trent University and started his PhD in January 2021.

Bradley has recently just completed a Master’s degree from Nottingham Trent University in MSc Computing Systems where his dissertation was based on one of the Smart Sensing Labs projects, 'Tag in the Park'. Before this, Bradley had completed an undergraduate course in BSc (Hons) Computing Systems Networks also at Nottingham Trent University. Bradley has joined the Smart Sensing team for his PhD to explore the uses of Machine Learning within Mobile Applications.

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Amna Anwar

amna.anwar@ntu.ac.uk

PhD Student

Amna is a PhD student in Pervasive Computing and Data science at Nottingham Trent University, after graduating with a BSc (Hons) Computing degree.

Amna’s interests are in Natural Language Processing (NLP) and ubiquitous technologies for crime prevention. The focus is on how technology fusion can be used to improve the safety of vulnerable individuals. Amna applies novel sentiment analysis used with audio sensors on edge computing devices, which will be used to detect violent or harmful language; commonplace in domestic violence cases.

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Michael Gibbs

michael.gibbs@ntu.ac.uk

PhD Student

Michael’s interests are in Artificial Intelligence (AI) and Autonomous Robotics, with his Final Year Project based on Autonomous Robot Navigation Algorithms.

He is pursuing a PhD in AI Processing in Edge Computing, after graduating with a BSc (Hons) Computer Science and Mathematics. Michael received the course prize for achieving the highest classification in his course and one of the highest in his cohort.

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