Blok, Johan

Dijkhuis, Talko

Dol, Aranka

Keywords–virtual coach; personalization; coaching strategy; data science; model based software development.The shift toward prevention and self-management in health hinges upon a massive realization of behavior change, which involves the use of virtual coaches. Increasing availability of data from mobile devices and techniques for real time analysis provide new opportunities for personalizing virtual coaches. We propose an architecture that takes advantage of those developments. We identify the required knowledge and methods to develop a ﬂexible ecosystem for rapid prototyping of personalized virtual coaches

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Toward a Generic Personalized Virtual Coach for Self-management:a Proposal for an ArchitectureJohan Blok, Aranka Dol, Talko DijkhuisSchool for Communication, Media & ITHanze University of Applied SciencesThe Netherlands, Groningene-mail: j.blok@pl.hanze.nl, a.dol@pl.hanze.nl, t.b.dijkhuis@pl.hanze.nlAbstract—The shift toward prevention and self-management inhealth hinges upon a massive realization of behavior change,which involves the use of virtual coaches. Increasing availabilityof data from mobile devices and techniques for real time analysisprovide new opportunities for personalizing virtual coaches. Wepropose an architecture that takes advantage of those devel-opments. We identify the required knowledge and methods todevelop a flexible ecosystem for rapid prototyping of personalizedvirtual coaches.Keywords–virtual coach; personalization; coaching strategy;data science; model based software development.I. INTRODUCTIONSelf-management is widely seen as a viable contributionto sustainable health-care as it allows to prevent health prob-lems. Virtual coaches are important means to support self-management in order to maintain and increase health and well-being.The wide availability of wearables and new techniquesof data science to deal with big data provide many newopportunities for the persuasive design of virtual coaches [1].The variety in health related data is increasing. While motiontrackers have become common, other wearables are becomingavailable, such as stress sensors. Yet, many applications arestill at a stage in which the data is simply presented asgraphs. Mostly, data is merely used as input to a monitoringtool rather than as input for coaching strategies, let alonethat the variety of data is combined in order to personalizethe coaching strategy. While van Gemert-Pijnen et. al. andSiewiorek et. al. identify these opportunities [1][2], we proposean architecture for integrating big data in virtual coaches. Theprototype described by Blaauw et al. combines the execution ofecological momentary assesments (EMA) with the collectionof data from wearables [3]. In our proposal, we embed thosetwo elements within a coaching strategy. Additionally, weenrich the notion of EMA by allowing for automatic execuctionand personalization of EMA’s. Albaina et al. illustrate thedesign process of a persuasive virtual coach for the veryspecific purpose of motivating elderly people to walk by usinga pedometer and goalsetting [4].This paper proposes an architecture for a generic personal-ized virtual coach. The first section introduces the main fieldsof knowledge that are required for developing a personalizedvirtual coach. Subsequently, we propose an architecture in-tegrating knowledge of these disciplines. The third sectionelaborates on the required computer science techniques. Thenext section shortly describes the kind of process managementthat is required in order to take the concept of virtual coachinga step further. A discussion of future work finishes this paper.II. METHODOLOGYWe define a virtual coach as a computational system thatassists the user, either as stand in or merely as suplementaryto a human coach, to support behavior that is desired bythe user to improve health or well-being. This definition iswider than the definition of eCoaching by Lentferink [5],even if it is narrowed down with the adjective ’personalized’as defined below. Contrary to Lentferink’s definition, whichlimits eCoaching to goalsetting, our definition is agnostic tothe nature of the coaching strategy. As a result, we are able todistinguish between the generic nature of the virtual coach anda wide range of coaching strategies. Although the architectureas such does not exclude a wider application, it is developedwithin the context of eHealth. The intended applications rangefrom the treatment of emotional eaters [6][7] to support formaintaining the social network by elderly people. As such, thevirtual coach implements persuasive technology that attemptsto influence the behavior of its users.According to the behavior model of Fogg, behavior is mostlikely to be influenced when the motivation is high and thetriggers to act are easy to execute [8]. Given the intrinsic dif-ficulties of achieving enduring behavior change, our definitiondeliberately limits the virtual coach to changes that are desiredby the user. According to Fogg’s behavior model, triggersbecome easy to execute when they are adapted to the context ofthe user. Fogg’s characterization of triggers straightforwardlytranslates into a crucial requirement for the virtual coach,namely that it must personalize its feedback to the user aswell as the coaching strategies on which this feedback relies.A particular coaching strategy, or a different focus within acoaching strategy, might be more effective, depending on thedispositions, abilities, preferences, and lifestyle of the user.The architecture proposed in the following section is basedon the integration of three disciplines. The first one belongs tothe therapeutical application of behavioural sciences, sociologyor pscyhology, or a mix of them, depending on the nature of theintervention for which one implements the virtual coach. Theyprovide an evidence based coaching strategy. For example,if an instantiation of the virtual coach needs to support thedevelopment of learning abilities of children, the coachingstrategy will stem from theories about metacognition and themanner in which capabilities can be improved. In the case ofemotional eaters, which comprises our first application of thevirtual coach, the coaching strategy stems from existing face-to-face therapies [9].The second discipline is that of data science. The widespread use of mobile devices and recent developments in thefield of big data and predictive analytics allow to achieve therequired personalization by gathering, analyzing, and interpret-ing personal data. Analysis, both on the level of personal data(n=1) and of aggregated data, provides information to guidethe coaching strategy in adapting its triggers to the specific cir-cumstances of the individual user. Most importantly, streamingdata, predictive analysis, and realtime data processing providesnovel opportunities to achieve an appropriate timing of thetriggers, which is key in the success of persuasive designs [8].Finally, the third discipline comprises a subfield of soft-ware engineering called model driven software development(MDSD). The application of domain models, especially in theform of domain specific languages (DSL), entails the devel-opment of abstractions that capture the crucial elements of adomain, in our case coaching strategies [10]. For the purposeof the virtual coach, a DSL must be designed that allows for anstraightforward implementation of coaching strategies. Such animplementation consists of a translation of (parts of) a coach-ing strategy into rules suitable for a computational system.The virtual coach executes these rules to provide feedback tothe user depending on the outcomes of the analysis of data.Since there are many ways in which even a single coachingstrategy can be implemented, considerable exploration andresearch must be carried out into the most effective ways ofimplementing coaching strategies. The application of MDSDensures that the rules of a coaching strategy can be changedeasily.III. ARCHITECTUREWe propose to combine a health data platform (HDP) witha platform for the implementation of coaching strategies (CSP)in order to establish an ecosystem for personalized virtualcoaches (see Figure 1).A. Health Data PlatformOne of the most import aspects of the required person-alization of the triggers of a coaching strategy consists inthe trigger’s timing [8]. The real time analysis of personaldata streams as well as the real time self-evaluation of thevirtual coach itself provides new opportunities to establish anappropriate timing of the triggers.The HDP employs techniques of data science to gather,store, query, analyze, classify, and predict health and lifestylerelated data. We distinguish between direct and indirect datastreams. The former stem from mobile devices, while the latterconsist of data that is generated by the coaching strategy itself.The indirect data streams allow the virtual coach to consult thecoaching history and adapt itself accordingly, for example toavoid coaching moves that have failed in the past.B. Coaching Strategy PlatformThe other part (CSP) implements the coaching strategy andconsists of the following parts:• user interaction;• data processing unit to combine data generated by userinteraction with the output of the health data platform;• rule engine to provide the user with feedback bypersonalizing scheme’s.First of all, feedback may consist of advice to take educationalmodules to improve skills as required by the coaching strategy.It may also consist of the suggestion to set a certain goal. Whenaccepted by the user, progress will be measured and visualizedin such a way that it motivates the user to achieve this goal.A third important type of feedback consists of an ecologicalmomentary assessment (EMA).EMA’s are often used in eHealth related research. Theyprovide a convenient way to obtain structured real time infor-mation about the behavior and experiences of the user [11][12].EMA’s are used for research purposes as well as treatment. Wepropose a way of using EMA’s within the virtual coach withtwo novel aspects:• automatic execution;• personalization.Depending on the specific state of an individual, the coachingstrategy might involve an EMA at certain stages of the coach-ing. Schematized EMA’s can be used to personalize EMA’s.Coaching rules enable the automatic execution of EMA’s thatare tailored to individual situations.Let us take a simple example to illustrate how this globalarchitecture supports the implementation of a virtual coach.Suppose a wearable is used for tracking the movements ofusers. Let us take as a very simple coaching strategy that thevirtual coach must be able to support existing good habits.Pattern detection and predictive analytics learns for examplethat a particular user A is usually running on each Fridaymorning. Let us assume that the user has entered his holidaysvia the user interface or that the virtual coach has access to thisinformation by other means. and that he spends his Saturdayssuch that it is no option to improve movements. This extremelysimplified coaching strategy can be implemented in the ruleengine as follows:if running is predicted within 5 minutes,remember the user unless he has a holidayThe user will receive notice in accordance with her or hispreferences (SMS, whatsapp, etc.). An improved version mighttake into account the weather and only remind the user whenit has stopped raining.IV. COMPUTER SCIENCE TECHNIQUESThis section identifies the required techniques of the mostinnovative aspects of the proposed architecture.A. Data ScienceWidely available mobile devices provide diverse datastreams that are useful for understanding, and possibly pre-dicting, the behavior of the user. The nature of the virtualcoach requires the analysis of these data streams to be auto-mated. Patterns, features, and anomalies need to be discoveredautomatically (knowledge discovery). Knowledge discoveryrequires:• the automation of the data analysis process;Lifestyle & Health Data PlatformCoaching Strategy PlatformTime SeriesClusteredBig Data StorageFeedback/EMA TemplatesPersonalizedFeedback(Advice,EMA,Goal)LOGBOOKBig Data Analysis(Predictive, Change, Classification,…)AnalysisQuery Engine(MapReduce)Coaching FeedbackRulesCollectCompareFilterSummarizeDataStorageFigure 1. Architecture Personalized Virtual Coach• the handling of Big Data in combination with Analyt-ics.The automation of data analysis involves the preparation,transformation, and modeling of data in order to become inthe position to discover ’knowledge’, that is, useful information[13]. The diversity, realtime, and streaming nature of the dataimpose Big Data aspects [14] on the automation of dataanalysis [15]. Accordingly, the health data platform requiresthe latest techniques and frameworks to implement the storageand real time analysis of data streams, such as Apache Storm.B. Model Driven Software EngineeringThe implementation of a coaching strategy requires atleast some techniques that are developed under the label ofartificial intelligence (AI). A common distinction between twoapproaches in AI is that between rule based expert systemsand data based machine learning systems. Currently the latteris quite popular, because of success in applying this approachto previously unsuccessful cases, such as machine translationand computer vision. To a large extent, this success is the resultof the availability of a lot of data of sufficient quality, such aslabeled images for computer vision.Unfortunately, in the case of coaching strategies, this data ishardly available (or not in the language of the intended users)and is not likely to become available in the foreseeable future.Moreover, experts on coaching strategies aim at improvements,which requires a conceptualization of psychological and/orsocial processes. In the case of machine learning, however,correctly labeled data is used to produce rules for computationindirectly. These automatically produced rules not only remainimplicit, but also remain almost incomprehensible for humancognition. Thus, a machine learning solution to the coachingstrategy as a whole tends to obfuscate an understanding ofthese processes, which is precisely what domain experts aimto unravel. Although machine learning is invaluable to analyzethe data streams, and can be used in several other parts, suchas natural language processing, we do not regard it as thefirst choice for implementing the coaching strategy as a whole.Nevertheless, once sufficient virtual coaches, using sufficientlyrich data sets, have been used for some time, they mightbe used to train machine learning modules to improve thevirtual coaches. Currently, however, we are not yet at thisstage, but have to start by implementing coaching strategiesas understood by domain experts.The field of MDSD provides convenient means, such asthe concept of a DSL, to implement a rule based system. Sucha DSL based system allows to formulate the coaching strategyin terms that are close to the coaching domain. In the caseof dialectal behavior therapy for emotional eaters a simpleexample of such a rule could be [9]:if Anger or Fear dominatesfollow Dialectical Coaching StrategyObviously, such a rule presupposes several software modules,such as modules that allow to determine the dominatingemotions, for example by means of EMA’s, a stress sensor,or the analysis of expressions in natural language. The DSLprovides an abstraction level that does not require knowledgeabout the way these software modules and the analysis of dataare realized.V. DEVELOPMENT PROCESSNowadays, agile approaches dominate the development ofsoftware. These approaches ensure a crucial role for all stake-holders at each stage of the development. An agile approach,especially in the form of scrum, is particularly suitable for a de-velopment process that requires the integration of quite diversedisciplines. By means of an, preferably daily, participation inthe scrum process, the expert on coaching strategies is imme-diately confronted with the the (im)possibilities of engineeringtechniques Conversely, the engineers get immediate feedbackon their interpretation of the coaching strategy. In addition, theagile approach prepares the virtual coach for adoption by usersand the healthcare sector [16].VI. CONCLUSIONWe have identified how knowledge of diverse disciplinesshould be integrated, and proposed an architecture for a genericpersonalized virtual coach that utilizes recent developments inwearables and data science. Focusing on the implementationof the coaching strategy required by the treatment of emotionaleaters, several software modules for both the HDP and CSPare currently under development. Future work will consist ofthe integration of the software modules to show the viability ofour architecture, including the design of an appropriate DSLfor implementing coaching strategies. Planned expansions toother coaching cases will warrant and demonstrate the genericnature of our solution.ACKNOWLEDGMENTNo acknowledgments are required.REFERENCES[1] L. V. Gemert-Pijnen, F. Sieverink, L. Siemons, andA. Braakman-Jansen, “Big data for personalized andpersuasive coaching via self-monitoring technology,”presented at the The Eighth International Confer-ence on eHealth, Telemedicine, and Social Medicine(eTelemed), 2016.[2] D. Siewiorek, A. Smailagic, and A. Dey, “Architectureand applications of virtual coaches,” Proceedings of theIEEE, vol. 100, no. 8, pp. 2472–2488, 2012.[3] F. J. Blaauw, H. M. Schenk, B. F. Jeronimus, L. van derKrieke, P. de Jonge, M. Aiello, and A. C. Emerencia,“Let’s get physiqual - an intuitive and generic method tocombine sensor technology with ecological momentaryassessments,” Journal of Biomedical Informatics, vol.63, pp. 141–149, 2016.[4] I. M. Albaina, T. Visser, C. v. d. Mast, and M. H.Vastenburg, “Flowie: A persuasive virtual coach tomotivate elderly individuals to walk,” presented at the3rd International Conference on Pervasive ComputingTechnologies for Healthcare, 2009, pp. 1–7.[5] A. Lentferink, H. Oldenhuis, O. Kulyk, M. D. Groot,L. Polstra, H. Velthuijsen, H. Hermens, and L. V.Gemert-Pijnen, “Self-tracking and persuasive eCoach-ing in healthy lifestyle interventions: Work-in-progressscoping review of key components,” presented at thePersuasive Technology 2016, Apr. 5, 2016.[6] A. Dol, O. Kulyk, H. Velthuijsen, L. V. Gemert-Pijnen,and T. V. Strien, “Denk je ze`lf! developing a person-alised virtual coach for emotional eaters using per-sonas,” presented at the The Eighth International Con-ference on eHealth, Telemedicine, and Social Medicine(eTelemed), 2016.[7] ——, “Developing a personalised virtual coach ’denkje ze`lf!’ for emotional eaters through the design ofemotion-enriched personas,” International Journal OnAdvances in Life Sciences, vol. 8, no. 3 & 4, 2016,accepted.[8] B. Fogg, “A behavior model for persuasive design,”in Proceedings of the 4th International Conference onPersuasive Technology, ACM, 2009, 40:1–40:7.[9] A. Dol, “Translating dialectic dialogues into persuasivecoaching strategies for personalized virtual coach: Aresearch protocol for emotional eaters case study,” forth-coming.[10] M. Voelter, DSL engineering: Designing, implementingand using domain-specific languages. CreateSpace In-dependent Publishing Platform, 2013, 558 pp.[11] S. Shiffman, A. A. Stone, and M. R. Hufford, “Ecolog-ical momentary assessment,” Annual Review of ClinicalPsychology, vol. 4, pp. 1–32, 2008.[12] I. Myin-Germeys, M. Oorschot, D. Collip, J. Lataster, P.Delespaul, and J. v. Os, “Experience sampling researchin psychopathology: Opening the black box of dailylife,” Psychological Medicine, vol. 39, no. 9, pp. 1533–47, 2009.[13] J. Shyr and D. Spisic, “Automated data analysis,” WileyInterdisciplinary Reviews: Computational Statistics, vol.6, no. 5, pp. 359–366, 2014.[14] A. Gandomi and M. Haider, “Beyond the hype: Big dataconcepts, methods, and analytics,” International Journalof Information Management, vol. 35, no. 2, pp. 137–144,2015.[15] A. Labrinidis and H. V. Jagadish, Proceedings of theVLDB Endowment, vol. 5, no. 12, pp. 2032–2033, 2012.[16] J. Balje´, A. Carter, and H. Velthuijsen, “Agile develop-ment as a change management approach in healthcareinnovation projects,” presented at the UnderstandingSmall Enterprises, 2015.

Toward a generic personalized virtual coach for self-management: a proposal for an architecture

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Toward a generic personalized virtual coach for self-management: a proposal for an architecture

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