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  • Journal article
    Zendle D, Flick C, Cutting J, Deterding S, Petrovskaya E, Drachen Aet al., 2022,

    The Many Faces of Monetisation: Understanding the diversity and extremity of spending in mobile games via massive-scale transactional analysis

    <p>With the rise of microtransactions, particularly in the mobile games industry, there has been ongoing concern that games reliant on these obtain substantial revenue from a small proportion of heavily involved individuals, to an extent that may be financially burdensome to these individuals. Yet despite substantive grey literature and speculation on this topic, there is little robust data available. We explore the revenue distribution in microtransaction-based mobile games using a transactional dataset of USD 4.7bn in in-game spending drawn from 69,144,363 players of 2,873 mobile games over the course of 624 days. We find diverse revenue distribution in mobile games, ranging from a ‘uniform’ cluster, in which all spenders invest approximately similar amounts, to ‘hyper-pareto’ games, in which a large proportion of revenue (~38%) stems from 1% of spenders alone. Specific kinds of games are typified by higher spending: the more a game relies on its top 1% for revenue generation, the more these individuals tend to spend, with simulated gambling products (‘social casinos’) at the top. We find a small subset of games across all genres, clusters, and age ratings in which the top 1% of gamers are highly financially involved– spending an average of USD 66,285 each in the 624 days under evaluation in the most extreme case. We discuss implications for future studies on links between gaming and wellbeing.</p>
  • Journal article
    Leong F, Chow Yin L, Siamak Farajzadeh K, He L, Simon DL, Thrishantha N, mazdak Get al., 2022,

    A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training

    , Bioengineering, Vol: 9, ISSN: 2306-5354

    Robotic patients show great potential to improve medical palpation training as they can provide feedback that cannot be obtained in a real patient. Providing information about internal organs deformation can significantly enhance palpation training by giving medical trainees visual insight based on their finger behaviours. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, thus able to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real-time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANN) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 hrs to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that the ANN has a 92.6% accuracy. We also show that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has potential to be used as a training simulator for trainees to hone their palpation skills.
  • Journal article
    Cursi F, Bai W, Yeatman EMM, Kormushev Pet al., 2022,

    Task Accuracy Enhancement for a Surgical Macro-Micro Manipulator With Probabilistic Neural Networks and Uncertainty Minimization

    , IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, ISSN: 1545-5955
  • Journal article
    Wen L, Li G, Huang T, Geng W, Pei H, Yang J, Zhu M, Zhang P, Hou R, Tian G, Su W, Chen J, Zhang D, Zhu P, Zhang W, Zhang X, Zhang N, Zhao Y, Cao X, Peng G, Ren X, Jiang N, Tian C, Chen Z-Jet al., 2022,

    Single-cell technologies: From research to application

    , INNOVATION, Vol: 3, ISSN: 2666-6758
  • Journal article
    Carboni G, Nanayakkara T, Takagi A, Burdet Eet al., 2022,

    Author Correction: Adapting the visuo-haptic perception through muscle coactivation

    , Scientific Reports, Vol: 12, ISSN: 2045-2322
  • Journal article
    Yu X, Logan I, Sarasola IDP, Dasaratha A, Ghajari Met al., 2022,

    The protective performance of modern motorcycle helmets under oblique impacts

    , Annals of Biomedical Engineering, Vol: 50, Pages: 1674-1688, ISSN: 0090-6964

    Motorcyclists are at high risk of head injuries, including skull fractures, focal brain injuries, intracranial bleeding and diffuse brain injuries. New helmet technologies have been developed to mitigate head injuries in motorcycle collisions, but there is limited information on their performance under commonly occurring oblique impacts. We used an oblique impact method to assess the performance of seven modern motorcycle helmets at five impact locations. Four helmets were fitted with rotational management technologies: a low friction layer (MIPS), three-layer liner system (Flex) and dampers-connected liner system (ODS). Helmets were dropped onto a 45° anvil at 8 m/s at five locations. We determined peak translational and rotational accelerations (PTA and PRA), peak rotational velocity (PRV) and brain injury criteria (BrIC). In addition, we used a human head finite element model to predict strain distribution across the brain and in corpus callosum and sulci. We found that the impact location affected the injury metrics and brain strain, but this effect was not consistent. The rear impact produced lowest PTAs but highest PRAs. This impact produced highest strain in corpus callosum. The front impact produced the highest PRV and BrIC. The side impact produced the lowest PRV, BrIC and strain across the brain, sulci and corpus callosum. Among helmet technologies, MIPS reduced all injury metrics and brain strain compared with conventional helmets. Flex however was effective in reducing PRA only and ODS was not effective in reducing any injury metrics in comparison with conventional helmets. This study shows the importance of using different impact locations and injury metrics when assessing head protection effects of helmets. It also provides new data on the performance of modern motorcycle helmets. These results can help with improving helmet design and standard and rating test methods.
  • Journal article
    Picinali L, Katz BFG, Geronazzo M, Majdak P, Reyes-Lecuona A, Vinciarelli Aet al., 2022,

    The SONICOM Project: artificial intelligence-driven immersive audio, from personalization to modeling

    , IEEE: Signal Processing Magazine, Vol: 39, Pages: 85-88, ISSN: 1053-5888

    Every individual perceives spatial audio differently, due in large part to the unique and complex shape of ears and head. Therefore, high-quality, headphone-based spatial audio should be uniquely tailored to each listener in an effective and efficient manner. Artificial intelligence (AI) is a powerful tool that can be used to drive forward research in spatial audio personalization. The SONICOM project aims to employ a data-driven approach that links physiological characteristics of the ear to the individual acoustic filters, which allows us to localize sound sources and perceive them as being located around us. A small amount of data acquired from users could allow personalized audio experiences, and AI could facilitate this by offering a new perspective on the matter. A Bayesian approach to computational neuroscience and binaural sound reproduction will be linked to create a metric for AI-based algorithms that will predict realistic spatial audio quality. Being able to consistently and repeatedly evaluate and quantify the improvements brought by technological advancements, as well as the impact these have on complex interactions in virtual environments, will be key for the development of new techniques and for unlocking new approaches to understanding the mechanisms of human spatial hearing and communication.
  • Journal article
    Dvorkin V, Mallapragada D, Botterud A, Kazempour J, Pinson Pet al., 2022,

    Multi-stage linear decision rules for stochastic control of natural gas networks with linepack

    , ELECTRIC POWER SYSTEMS RESEARCH, Vol: 212, ISSN: 0378-7796
  • Journal article
    Bai L, Pinson P, Wang J, 2022,

    Variable heat pricing to steer the flexibility of heat demand response in district heating systems

    , ELECTRIC POWER SYSTEMS RESEARCH, Vol: 212, ISSN: 0378-7796
  • Journal article
    Han L, Pinson P, Kazempour J, 2022,

    Trading data for wind power forecasting: A regression market with lasso regularization

    , ELECTRIC POWER SYSTEMS RESEARCH, Vol: 212, ISSN: 0378-7796
  • Journal article
    Haridas AK, Sadan MK, Kim J-H, Lee Y, Ahn J-Het al., 2022,

    Electrospun Interconnected Bead-Like P2-Na<sub>x</sub>Co<sub>y</sub>Mn<sub>1-y</sub>O<sub>2</sub> (x=0.66, y=0.1) Cathode Material for Stable Sodium-Ion Storage

    , BATTERIES-BASEL, Vol: 8
  • Journal article
    Sun S, Wang J, Chen X, Ma Q, Wang Y, Yang K, Yao X, Yang Z, Liu J, Xu H, Cai Q, Zhao Y, Yan Wet al., 2022,

    Thermally Stable and Dendrite-Resistant Separators toward Highly Robust Lithium Metal Batteries

    , ADVANCED ENERGY MATERIALS, Vol: 12, ISSN: 1614-6832
  • Journal article
    Herre L, Pinson P, Chatzivasileiadis S, 2022,

    Reliability-aware probabilistic reserve procurement

    , ELECTRIC POWER SYSTEMS RESEARCH, Vol: 212, ISSN: 0378-7796
  • Conference paper
    Ge P, Caputo C, Teng F, Cardin M-A, Korre Aet al., 2022,

    A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources

    , Singapore, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
  • Journal article
    Kench S, Squires I, Dahari A, Cooper Set al., 2022,

    MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN

    , Scientific Data, Vol: 9, ISSN: 2052-4463

    3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. This has proven a useful tool for understanding how complex material systems behave under applied stresses, temperatures and chemical conditions. However, 3D imaging of materials is challenging for a number of reasons, including limited field of view, low resolution and difficult sample preparation. Recently, a machine learning method, SliceGAN, was developed to statistically generate 3D microstructural datasets of arbitrary size using a single 2D input slice as training data. In this paper, we present the results from applying SliceGAN to 87 different microstructures, ranging from biological materials to high-strength steels. To demonstrate the accuracy of the synthetic volumes created by SliceGAN, we compare three microstructural properties between the 2D training data and 3D generations, which show good agreement. This new microstructure library both provides valuable 3D microstructures that can be used in models, and also demonstrates the broad applicability of the SliceGAN algorithm.
  • Book chapter
    Picinali L, Katz BFG, 2022,

    System-to-user and user-to-system adaptations in Binaural audio

    , Sonic Interactions in Virtual Environments, Editors: Geronazzo, Serafin, Publisher: Springer, Pages: 115-143

    This chapter concerns concepts of adaption in a binaural audio context (i.e. headphone-based three-dimensional audio rendering and associated spatial hearing aspects), considering first the adaptation of the rendering system to the acoustic and perceptual properties of the user, and second the adaptation of the user to the rendering quality of the system. We start with an overview of the basic mechanisms of human sound source localisation, introducing expressions such as localisation cues and interaural differences, and the concept of the Head-Related Transfer Function (HRTF), which is the basis of most 3D spatialisation systems in VR. The chapter then moves to more complex concepts and processes, such as HRTF selection (system-to-user adaptation) and HRTF accommodation (user-to-system adaptation). State-of-the-art HRTF modelling and selection methods are presented, looking at various approaches and at how these have been evaluated. Similarly, the process of HRTF accommodation is detailed, with a case study employed as an example. Finally, the potential of these two approaches are discussed, considering their combined use in a practical context, as well as introducing a few open challenges for future research.
  • Journal article
    Serban A-I, Soreq E, Barnaghi P, Daniels S, Calvo R, Sharp Det al., 2022,

    The effect of COVID-19 on the home behaviours of people affected by dementia

    , npj Digital Medicine, Vol: 5, ISSN: 2398-6352

    The COVID-19 pandemic has dramatically altered the behaviour of most of the world’s population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including social isolation. The home activity was monitored continuously using unobtrusive sensors for 498 days from 1 December 2019 to 12 April 2021. This period included six distinct pandemic phases with differing public health measures, including three periods of home ‘lockdown’. Linear mixed-effects modelling is used to examine changes in the home activity of PLwD who lived alone or with others. An algorithm is developed to quantify time spent outside the home. Increased home activity is observed from very early in the pandemic, with a significant decrease in the time spent outside produced by the first lockdown. The study demonstrates the effects of COVID-19 lockdown on home behaviours in PLwD and shows how unobtrusive home monitoring can be used to track behaviours relevant to social isolation.
  • Journal article
    Zhang Qiu P, Yongxuan T, Thompson O, Cobley B, Nanayakkara Tet al., 2022,

    Soft tissue characterisation using a novel robotic medical percussion device with acoustic analysis and neural networks

    , IEEE Robotics and Automation Letters, Vol: 7, Pages: 11314-11321, ISSN: 2377-3766

    Medical percussion is a common manual examination procedure used by physicians to determine the state of underlying tissues from their acoustic responses. Although it has been used for centuries, there is a limited quantitative understanding of its dynamics, leading to subjectivity and a lack of detailed standardisation. This letter presents a novel compliant two-degree-of-freedom robotic device inspired by the human percussion action, and validates its performance in two tissue characterisation experiments. In Experiment 1, spectro-temporal analysis using 1-D Continuous Wavelet Transform (CWT) proved the potential of the device to identify hard nodules, mimicking lipomas, embedded in silicone phantoms representing a patient's abdominal region. In Experiment 2, Gaussian Mixture Modelling (GMM) and Neural Network (NN) predictive models were implemented to classify composite phantom tissues of varying density and thickness. The proposed device and methods showed up to 97.5% accuracy in the classification of phantoms, proving the potential of robotic solutions to standardise and improve the accuracy of percussion diagnostic procedures.
  • Journal article
    Planella FB, Ai W, Boyce AM, Ghosh A, Korotkin I, Sahu S, Sulzer V, Timms R, Tranter TG, Zyskin M, Cooper SJ, Edge JS, Foster JM, Marinescu M, Wu B, Richardson Get al., 2022,

    A continuum of physics-based lithium-ion battery models reviewed

    , PROGRESS IN ENERGY, Vol: 4
  • Journal article
    Ai W, Wu B, Martínez-Pañeda E, 2022,

    A coupled phase field formulation for modelling fatigue cracking in lithium-ion battery electrode particles

    , Journal of Power Sources, Vol: 544, ISSN: 0378-7753

    Electrode particle cracking is one of the main phenomena driving battery capacity degradation. Recent phase field fracture studies have investigated particle cracking behaviour. However, only the beginning of life has been considered and effects such as damage accumulation have been neglected. Here, a multi-physics phase field fatigue model has been developed to study crack propagation in battery electrode particles undergoing hundreds of cycles. In addition, we couple our electrochemo-mechanical formulation with X-ray CT imaging to simulate fatigue cracking of realistic particle microstructures. Using this modelling framework, non-linear crack propagation behaviour is predicted, leading to the observation of an exponential increase in cracked area with cycle number. Three stages of crack growth (slow, accelerating and unstable) are observed, with phenomena such as crack initialisation at concave regions and crack coalescence having a significant contribution to the resulting fatigue crack growth rates. The critical values of C-rate, particle size and initial crack length are determined, and found to be lower than those reported in the literature using static fracture models. Therefore, this work demonstrates the importance of considering fatigue damage in battery degradation models and provides insights on the control of fatigue crack propagation to alleviate battery capacity degradation.
  • Journal article
    Yang S, Zhou C, Wang Q, Chen B, Zhao Y, Guo B, Zhang Z, Gao X, Chowdhury R, Wang H, Lai C, Brandon NP, Wu B, Liu Xet al., 2022,

    Highly‐aligned ultra‐thick gel‐based cathodes unlocking ultra‐high energy density batteries

    , Energy & Environmental Materials, Vol: 5, Pages: 1332-1339, ISSN: 2575-0356

    Increasing electrode thickness can substantially enhance the specific energy of lithium-ion batteries, however ionic transport, electronic conductivity and ink rheology are current barriers to adoption. Here a novel approach using a mixed xanthan gum and locust bean gum binder to construct ultra-thick electrodes is proposed to address above issues. After combining aqueous binder with single walled carbon nanotubes (SWCNT), active material (LiNi0.8Co0.1Mn0.1O2) and subsequent vacuum freeze drying, highly-aligned and low tortuosity structures with a porosity of ca. 50% can be achieved with an average pore size of 10 μm, whereby the gum binder-SWCNT-NMC811 forms vertical structures supported by tissue-like binder/SWCNT networks allowing for excellent electronic conducting phase percolation. As a result, ultra-thick electrodes with a mass loading of about 511 mg·cm-2 and 99.5 wt% active materials have been demonstrated with a remarkable areal capacity of 79.3 mAh·cm−2, which is the highest value reported so far. This represents a >25x improvement compared to conventional electrodes with an areal capacity of about 3 mAh·cm-2. This route also can be expanded to other electrode materials, such as LiFePO4 and Li4Ti5O12, and thus opens the possibility for low-cost and sustainable ultra-thick electrodes with increased specific energy for future lithium-ion batteries.
  • Journal article
    Cursi F, Bai W, Yeatman EM, Kormushev Pet al., 2022,

    Optimization of surgical robotic instrument mounting in a macro-micro manipulator setup for improving task execution

    , IEEE Transactions on Robotics, Vol: 38, Pages: 2858-2874, ISSN: 1552-3098

    In Minimally Invasive Robotic Surgery (MIRS),the surgical instrument is usually inserted inside the patient’sbody through a small incision, which acts as a Remote Centerof Motion (RCM). Serial-link manipulators can be used asmacro robots on which micro surgical robotic instruments aremounted to increase the number of degrees of freedom (DOFs)of the system and ensure safe task and RCM motion execution.However, the surgical instrument needs to be placed in anappropriate configuration when completing the motion tasks.The contribution of this work is to present a novel frameworkthat preoperatively identifies the best base configuration, interms or Roll, Pitch, and Yaw angles, of the micro surgicalinstrument with respect to the macro serial-link manipulator’send-effector in order to achieve the maximum accuracy anddexterity in performing specified tasks. The framework relieson Hierarchical Quadratic Programming (HQP) for the control,Genetic Algorithm (GA) for the optimization, and on a resilienceto error strategy to make sure deviations from the optimum donot affect the system’s performance.Simulation results show that the mounting configuration ofthe surgical instrument significantly impacts the performanceof the whole macro-micro manipulator in executing the desiredmotion tasks, and both the simulation and experimental resultsdemonstrate that the proposed optimization method improves theoverall performance.
  • Journal article
    Pinson P, Han L, Kazempour J, 2022,

    Regression markets and application to energy forecasting

    , TOP, Vol: 30, Pages: 533-573, ISSN: 1134-5764
  • Journal article
    Xiang Y, Cao Y, Yang W, Hu R, Wood S, Li B, Hu Q, Zhang F, He J, Yavari M, Zhao J, Zhao Y, Song J, Qu J, Zhu R, Russell TP, Silva SRP, Zhang Wet al., 2022,

    Laser-Induced Recoverable Fluorescence Quenching of Perovskite Films at a Microscopic Grain Scale

    , ENERGY & ENVIRONMENTAL MATERIALS, Vol: 5, Pages: 1189-1199
  • Journal article
    Wen H, Pinson P, Ma J, Gu J, Jin Zet al., 2022,

    Continuous and Distribution-Free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach

    , IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol: 13, Pages: 2250-2263, ISSN: 1949-3029
  • Journal article
    Bi J, Zhang J, Giannakou P, Wickramanayake T, Yao X, Wang M, Liu X, Shkunov M, Zhang W, Zhao Yet al., 2022,

    A Highly integrated flexible photo-rechargeable system based on stable ultrahigh-rate quasi-solid-state zinc-ion micro-batteries and perovskite solar cells

    , ENERGY STORAGE MATERIALS, Vol: 51, Pages: 239-248, ISSN: 2405-8297
  • Journal article
    Cacciarelli D, Kulahci M, Tyssedal JS, 2022,

    Stream-based active learning with linear models

    , Knowledge-Based Systems, Vol: 254, Pages: 109664-109664, ISSN: 0950-7051
  • Conference paper
    Nguyen QT, Mougenot C, 2022,

    We’re still people and not only emails that we’re sending - shared cognition in distributed design collaboration: A qualitative study on distributed creative teams and the relation of communication ecology on virtual collaboration shared understanding

    , 2022 4th International Electronics Communication Conference (IECC), Publisher: ACM, Pages: 40-46

    To identify challenges for future design collaborative systems, we conducted a qualitative study, interviewing expert design practitioners working in creative, multidisciplinary distributed teams The development of shared mental models, previously not examined through the construct of the CSCW ecology, presented four dimensions: task-specific knowledge, task-related knowledge, knowledge of teammates and attitudes/beliefs, where the latter one being the most vulnerable. The study informs the design of future CSCW tools for virtual collaboration tools to fully support remote creative teams.
  • Journal article
    Burge T, Jones G, Jordan C, Jeffers J, Myant Cet al., 2022,

    A computational tool for automatic selection of total knee replacementimplant size using x-ray images

    , Frontiers in Bioengineering and Biotechnology, Vol: 10, Pages: 1-11, ISSN: 2296-4185

    Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total kneereplacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or guidingsoftware tools via the adoption of convolutional neural networks, time and resource requirements for pre-operative assessmentand surgery could be reduced, the risk of human error minimized, and patients could see improved outcomes.Methods: The tool utilizes a machine learning-based 2D – 3D pipeline to generate accurate predictions of subjects’ distal femur andproximal tibia bones from X-ray images. It then virtually fits different implant models and sizes to the 3D predictions, calculatesthe implant to bone root-mean-squared error and maximum over/under hang for each, and advises the best option for thepatient. The tool was tested on 78, predominantly White subjects (45 female/33 male), using generic femur component and tibiaplate designs scaled to sizes obtained for five commercially available products. The predictions were then compared to the groundtruth best options, determined using subjects’ MRI data.Results: The tool achieved average femur component size prediction accuracies across the five implant models of 77.95% in termsof global fit (root-mean-squared error), and 71.79% for minimizing over/underhang. These increased to 99.74% and 99.49% with ±1size permitted. For tibia plates, the average prediction accuracies were 80.51% and 72.82% respectively. These increased to99.74% and 98.98% for ±1 size. Better prediction accuracies were obtained for implant models with fewer size options, howeversuch models more frequently resulted in a poor fit.Conclusion: A fully automatic tool was developed and found to enable higher prediction accuracies than generally reported formanual templating techniques, as well as similar computational methods.
  • Conference paper
    Chappell D, Yang Z, Son HW, Bello F, Kormushev P, Rojas Net al., 2022,

    Towards instant calibration in myoelectric prosthetic hands: a highly data-efficient controller based on the Wasserstein distance

    , International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE

    Prosthetic hand control research typically focuses on developing increasingly complex controllers to achieve diminishing returns in pattern recognition of muscle activity signals, making models less suitable for user calibration. Some works have investigated transfer learning to alleviate this, but such approaches increase model size dramatically—thus reducing their suitability for implementation on real prostheses. In this work, we propose a novel, non-parametric controller that uses the Wasserstein distance to compare the distribution of incoming signals to those of a set of reference distributions,with the intended action classified as the closest distribution. This controller requires only a single capture of data per reference distribution, making calibration almost instantaneous. Preliminary experiments building a reference library show that, in theory, users are able to produce up to 9 distinguishable muscle activity patterns. However, in practice, variation whenrepeating actions reduces this. Controller accuracy results show that 10 non-disabled and 1 disabled participant were able to use the controller with a maximum of two recalibrations to perform 6 actions at an average accuracy of 89.9% and 86.7% respectively. Practical experiments show that the controller allows users to complete all tasks of the Jebsen-Taylor HandFunction Test, although the task of picking and placing small common objects required on average more time than the protocol’s maximum time.

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