David Plans

Dr David Plans

Lecturer in Entrepreneurship and Innovation
+44 (0)1483 683524
56 MS 03



David is an adaptive media researcher and entrepreneur. In his early career and doctoral work, he looked to computational intelligence, in particular genetic co-evolution, to create reflexive media systems that imitated and queried the nature of human creativity.He created the first European merger for Open Source startups and helped the National Health Service deploy the first mobile application to let users self-report in chronic illness.He has given papers and performances at the International Computer Music Conference, the European Conference on Artificial Life, IRCAM, the Darwin Symposium, and the Computer Arts Society in London, and more recently, has published on human emotion in play (affective modelling for games) in IEEE Computational Intelligence transactions and the International Conference on Creative Computing proceedings.His teaching focuses on software and hardware practices for digital media and core practices in Open Source and Open Innovation within startup culture.His industrial work, which has focused on creating mobile applications for self-reporting and behaviour change in mental health, is now focused on biobeats.com, a startup in San Francisco/London/Pisa that builds artificial intelligence solutions for large health platforms, including projects that address how decentralized digital currencies could offer derivative exchange value in health insurance for an individual's biometric data.

Research interests

  • Adaptive media algorithms as healthcare industry disruptors (SXSW'13 Accelerator)
  • Better media discovery through biometric algorithms (won Echo Nest prize)
  • Large-scale content-based retrieval for audiovisual archives (achieved 500K funding so far)
  • Procedural audiovisual content generation for games engines (published IEEE journal papers)
  • Cryptocurrency-based value exchange systems in healthcare data

My publications


Plans D, Morelli D (2012) Experience-Driven Procedural Music Generation for Games, Computational Intelligence and AI in Games, IEEE Transactions on4(3)pp. 192-198
Plans Casal D (2010) Co-evolution and MPEG7 matching in creating Artificial Music Improvisors, In: Miranda E (eds.), Music as It Could Be: New Musical Worlds from Artificial Life A-R Editions
Plans Casal DAM (2007) Remembering the Future : Towards and Application of Genetic Co-Evolution in Music Improvisation,
Plans Casal D (2005) Apache Cocoon: an applications framework for the JISC IE, The Journal of Information and Knowledge Management Systems35(1-2)pp. 70-77
Plans Casal D (2008) Time After Time : Short-circuiting The Emotional Distance Between Algorithm And Human Improvisors,
Plans E, Morelli D, Plans D (2015) AudioNode: Prototypical Affective Modelling in Experience-driven Procedural Music Generation, 1st Computational Creativity and Games Workshop Brigham Young University
Abstract This paper presents current work under development that furthers previous research from the authors in Experience-Driven Procedural Music Generation for games. Recent work is explored in context in affective modelling and biosignal-driven evaluation. ...
Font F, Brookes TS, Fazekas G, Guerber M, La Burthe A, Plans D, Plumbley M, Shaashua M, Wang W, Serra X (2016) Audio Commons: bringing Creative Commons audio content to the creative industries,
Significant amounts of user-generated audio content, such as sound effects, musical samples and music pieces, are uploaded to online repositories and made available under open licenses. Moreover, a constantly increasing amount of multimedia content, originally released with traditional licenses, is becoming public domain as its license expires. Nevertheless, the creative industries are not yet using much of all this content in their media productions. There is still a lack of familiarity and understanding of the legal context of all this open content, but there are also problems related with its accessibility. A big percentage of this content remains unreachable either because it is not published online or because it is not well organised and annotated. In this paper we present the Audio Commons Initiative, which is aimed at promoting the use of open audio content and at developing technologies with which to support the ecosystem composed by content repositories, production tools and users. These technologies should enable the reuse of this audio material, facilitating its integration in the production workflows used by the creative industries. This is a position paper in which we describe the core ideas behind this initiative and outline the ways in which we plan to address the challenges it poses.
Plans Casal D (2010) Decomposing Autumn : A Component-Wise Recomposition,
Plans Casal D (2007) Remembering the Future : and Overview of Co-Evolution in Musical Improvisation,
Plans Casal D (2005) Advanced software development for web applications, Techwatch Reports
Morelli D, Bartoloni L, Colombo M, Plans D, Clifton D (2017) Profiling the Propagation of Error from PPG to HRV Features in a Wearable Physiological-Monitoring Device,Healthcare Technology Letters5(2)pp. 59-64 Institution of Engineering and Technology
Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology of ambulatory patients. In this paper, we investigate the reliability of the heartrate sensor in an exemplar ?wearable" wrist-worn monitoring system (the Microsoft Band 2); our experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of heart rate (HR), and (iii) subsequent calculation of heart rate variability (HRV) features. Our experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of heart rate data can be removed, using the accelerometer sensor from the wearable device. Our experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. We finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features.
Plans Casal D (2011) Crowdsourcing the Corpus: Using Collective Intelligence as a Method for Composition, Leonardo Music Journal-(21)pp. 25-28
Cropley Mark, Plans David, Morelli Davide, Sütterlin S, Inceoglu Ilke, Thomas Geoff, Chu Chris Wai Lung (2017) The Association between Work-Related Rumination and Heart Rate Variability: A Field Study,Frontiers in Human Neuroscience1127 Frontiers Media
The objective of this study was to examine the association between perseverative cognition in the form of work-related rumination, and heart rate variability (HRV). We tested the hypothesis that high ruminators would show lower vagally mediated HRV relative to low ruminators during their leisure time. Individuals were classified as being low (n = 17) or high ruminators (n = 19), using the affective scale on the work-related rumination measure. HRV was assessed using a wrist sensor band (Microsoft Band 2). HRV was sampled between 8 pm and 10 pm over three workday evenings (Monday to Wednesday) while individuals carried out their normal evening routines. Compared to the low ruminators, high affective ruminators demonstrated lower HRV in the form of root mean square successive differences (RMSSDs), relative to the low ruminators, indicating lower parasympathetic activity. There was no significant difference in heart rate, or activity levels between the two groups during the recording periods. The current findings of this study may have implications for the design and delivery of interventions to help individuals unwind post work and to manage stress more effectively. Limitations and implications for future research are discussed.
The work confronts two approaches to realize preference learning using Extreme Learning Machine networks, relaying on limited and subject-dependant information concerning pairwise relations between data samples. We describe an application within the context of assessing the effect of breathing exercises on heart-rate variability, using a dataset of over 19K exercising sessions. Results highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation.
Inceoglu Ilke, Thomas Geoff, Chu Chris Wai Lung, Plans David, Gerbasi Alexandra (2018) Leadership behavior and employee well-being: An integrated review and a future research agenda,The Leadership Quarterly29(1)pp. 179-202 Elsevier
Leadership behavior has a significant impact on employee behavior, performance and well-being. Extant theory and research on leadership behavior, however, has predominantly focused on employee performance, treating employee well-being (typically measured as job satisfaction) as a secondary outcome variable related to performance, rather than as an important outcome in and of itself. This qualitative state of the science review examines the process by which leadership behavior (i.e., change, relational, task, passive) affects employee well-being. We identify five mediator groupings (social-cognitive, motivational, affective, relational, identification), extend the criterion space for conceptualizing employee well-being (i.e., psychological: hedonic, eudaimonic, negative; and physical), examine the limited evidence for differential processes that underlie the leader behavior-employee well-being relationship and discuss theoretical and methodological problems inherent to the literature. We conclude by proposing a theoretical framework to guide a future research agenda on how, why and when leadership behavior impacts employee well-being.
Jackson Philip, Plumbley Mark D, Wang Wenwu, Brookes Tim, Coleman Philip, Mason Russell, Frohlich David, Bonina Carla, Plans David (2017) Signal Processing, Psychoacoustic Engineering and Digital Worlds: Interdisciplinary Audio Research at the University of Surrey,
At the University of Surrey (Guildford, UK), we have brought together research groups in different disciplines, with a shared interest in audio, to work on a range of collaborative research projects. In the Centre for Vision, Speech and Signal Processing (CVSSP) we focus on technologies for machine perception of audio scenes; in the Institute of Sound Recording (IoSR) we focus on research into human perception of audio quality; the Digital World Research Centre (DWRC) focusses on the design of digital technologies; while the Centre for Digital Economy (CoDE) focusses on new business models enabled by digital technology. This interdisciplinary view, across different traditional academic departments and faculties, allows us to undertake projects which would be impossible for a single research group. In this poster we will present an overview of some of these interdisciplinary projects, including projects in spatial audio, sound scene and event analysis, and creative commons audio.
Plans David, Morelli Davide, Sütterlin Stefan, Ollis Lucie, Derbyshire Georgia, Cropley Mark (2019) Use of a Biofeedback Breathing App to Augment Poststress Physiological Recovery: Randomized Pilot Study,JMIR Formative Research3(1) JMIR Publications

Background: The speed of physiological recovery from stress may be a marker for cardiovascular disease risk. Stress management programs that incorporate guided breathing have been shown to moderate the stress response and augment recovery.

Objective: The aim of this study was to examine the effectiveness of an app-based brief relaxation intervention (BioBase) for facilitating physiological recovery in individuals exposed to a brief psychological stressor.

Methods: A total of 75 participants (44 women) completed a stressor speech task and were randomly assigned to one of three conditions: control, rumination, or an app-based relaxation breathing (BioBase) conditions. Heart rate variability (HRV) was assessed as a measure of autonomic function at baseline (6 min), during stress (6 min), and during recovery (6 min).

Results: There was a significant increase in subjective stress following stress exposure, but the ratings returned to baseline after recovery in all three groups. In addition, there was a significant decrease in vagally mediated HRV in the poststress period. During recovery, the root mean square of successive differences (PÂ.001), the percentage of successive interbeat (RR) intervals that differ by Ã50 ms (pNN50; PÂ.001), and high-frequency (PÂ.02) HRV were significantly higher in the BioBase breathing condition than the rumination and control conditions. There was no difference in HRV values between the rumination and control conditions during recovery.

Conclusions: App-based relaxed breathing interventions could be effective in reducing cardiovascular disease risk. These results provide additional utility of biofeedback breathing in augmenting physiological recovery from psychological stress.