Learning to Self-Manage by Intelligent Monitoring, Prediction and InterventionNirmalie Wiratunga1 , David Corsar1 , Kyle Martin1 , Anjana Wijekoon1 , Eyad Elyan1 , Kay

Cooper1 ,Zina Ibrahim2,3 , Oya Celiktutan2 , Richard J. Dobson2,3 ,

Stephen McKenna4 , Jacqui Morris4 , Annalu Waller4 ,Raed Abd-Alhammed5 , Rami Qahwaji5 ,

Ray Chaudhuri61 Robert Gordon University, Aberdeen, UK

2 King’s College London, London, UK3 University College London, London, UK

4 University of Dundee, Dundee, UK5 University of Bradford, Bradford, UK

6 King’s College Hospital, NHS Foundation Trust, UK{n.wiratunga, d.corsar, k.martin, a.wijekoon, e.elyan, k.cooper}@rgu.ac.uk,

{zina.ibrahim, oya.celiktutan, richard.j.dobson}@kcl.ac.uk,{s.j.z.mckenna, j.y.morris, a.waller}@dundee.ac.uk,

{r.a.a.abd, r.s.r.qahwaji}@bradford.ac.uk,ray.chaudhuri@nhs.net

Abstract

Despite the growing prevalence of multimorbidi-ties, current digital self-management approachesstill prioritise single conditions. The future of out-of-hospital care requires researchers to expand theirhorizons; integrated assistive technologies shouldenable people to live their life well regardless oftheir chronic conditions. Yet, many of the cur-rent digital self-management technologies are notequipped to handle this problem. In this positionpaper, we suggest the solution for these issues isa model-aware and data-agnostic platform formedon the basis of a tailored self-management planand three integral concepts - Monitoring (M) mul-tiple information sources to empower Predictions(P) and trigger intelligent Interventions (I). Here wepresent our ideas for the formation of such a plat-form, and its potential impact on quality of life forsufferers of chronic conditions.

1 IntroductionChronic health conditions currently incur over 80% of allhealthcare spending in the United Kingdom. Living withone or more chronic illnesses almost certainly means majorchanges in one’s life; the latter can be minimised with ef-fective self-management. Studies show that the capability toself-manage (chronic) health conditions effectively promiseslower associated healthcare costs and more efficient use ofprimary and secondary care [Wolff et al., 2002].

Although the number of individuals living with multiplemorbidities is predicted to increase significantly over thecoming years [Barnett et al., 2012], current self-managementsolutions prioritise single conditions. A 2018 study from theUK National Institute of Health Research (NIHR) predictedthat two-thirds of people aged 65 and over will have multi-ple morbidities by 2035, and 17% with four or more condi-tions. One third of these people will have a mental illness(e.g. dementia or depression). Increased life expectancy forboth men and women means people will spend a longer timeliving with multiple morbidities, placing increased demandon the healthcare system.

Integrated assistive technologies promises a to enable peo-ple to live their life well regardless of their chronic condi-tions. Advances in telecommunications and Artificial Intelli-gence (AI) technologies paves the way for personalised vir-tual health companions that provide a intelligently-on con-nection between the patient and those providing their care.Such companions should be an intermediary, supporting pa-tients with confidently managing their condition(s), proac-tively engaging with health care professions only when nec-essary, reducing their workload and replacing the current re-active patient-clinician interaction. This can be achieved byreasoning with data from automated observations of patient’sprogress along their healthcare plan using real-time predic-tions to trigger appropriate proactive interventions. Chronicpatients have to live with their conditions 24/7, so it is naturalthat their care should reflect that.

This position paper presents our framework for multi-morbidity virtual health companions, each tailored to theunique health needs of individuals, assisting them to take

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an assured, active role in managing their health.The frame-work is based on a configurable architecture comprising mul-tiple reasoning components for Monitoring, Prediction andIntervention (MPI). This will provide a plug and play modelenabling the bespoke integration of existing and yet-to-be-created devices, along with modes of reasoning as neces-sary in a library of AI skills. For example, humanoid-robot driven conversational dialogue systems, autonomousimage and video analysis, analysis of real-time wearableand implant-generated data, and advanced telecommunica-tion networks (e.g. 5G) for remote interactions.

This paper is structured as follows. In Section 2 we discussrelated work within digital self-management. In Section 3 wepresent our concepts on requirements for a generic frameworkwith emphasis on reasoning centered around Monitoring, Pre-diction and Intervention (MPI) components, and detail howthese can be expanded to cover multiple morbidities. In Sec-tion 4 we describe several technologies which we expect to bekey players in the future and explore their integration withina data agnostic framework. Finally, in Section 5 we providesome conclusions.

2 Related WorkSelf-management is a set of approaches which aim to enablepeople living with long-term conditions to take control oftheir care and manage their own health. Assistive technologycan support self-management on several levels:

1. Problem-solving (e.g. coping with flare-ups or adaptingplan activities);

2. Decision-making (e.g. when to seek support or helpwith decisions around positive behaviour changes likeimproving diet, reducing alcohol consumption, quittingsmoking or increasing physical activity and social inter-action);

3. Resource utilisation (e.g. making best use of healthcareand other resources, including 3rd sector, peer-support,web-based resources and other sources of information oradvice);

4. Forming patient-healthcare provider relationships andencouraging patients to interact with their healthcareprovider appropriately. This would ideally occur beforeemergency or crisis situations arise to prevent decline inhealth and/or hospital admission.

5. Action planning and self-tailoring (e.g. encouraging pa-tient participation in creating their own self-managementplan (which might include physical activity, specificexercises, relaxation) and tailoring it to their specificneeds, improving patient’s knowledge of their condi-tions.

The goal of self-management is to encourage behaviourchange in sufferers of chronic conditions. A systematic re-view of interventions to promote physical activity [Morris etal., 2014] illustrated that interventions involving behaviourchange strategies are more effective for sustaining longer-term physically active lifestyles than time-limited interven-tions involving structured exercises alone.

These interventions are commonly delivered face to faceby healthcare practitioners. However current studies indicatethat healthcare time is extremely limited and of short dura-tion. Without ongoing support, patient physical activity lev-els decline as maintaining motivation is difficult [Morris etal., 2012]. Innovative, person-centred strategies to monitorand predict physical activity and exercise behaviours, to scanand anticipate environmental barriers to activity, and to pro-vide social and motivation support are required. These mustsupport evidence-based, personally tailored behaviour changestrategies by monitoring and providing feedback on perfor-mance; provide virtual real-time social support for activity;provide feedback on physical performance and evaluation ofenvironmental barriers to physical activity.

2.1 Case-based reasoning for self-managementPrevious work has demonstrated the effectiveness of apply-ing decision support and reasoning systems to the manage-ment of a specific chronic disease. For instance Case-basedreasoning (CBR) which is an AI approach that solves newproblems using specific knowledge extracted from previouslysolved problems, has been successfully used to incorporateevidence-base practices. Here, reasoning is facilitated by acollection of cases, a unique set of past experiences storedin a case base. However to the best of our knowledge CBRhas only been applied in self-management of single chronicdiseases.

CBR has been applied to managing diabetes types 1 and2, using records that provide details about periodical vis-its with a physician in a case consisting of features thatrepresent a problem (e.g. weight, blood glucose level),its solution (e.g. levels of insulin) and the outcome (e.g.hyper/hypo(glycemia)) observed after applying the solu-tion [Marling et al., 2012; Montani et al., 2000]. More re-cent work [Chen et al., 2017], explored the management ofdiabetes type 1 to support monitoring of blood glucose levelsbefore, during and after exercises. Interventions recommendcarbohydrate intake based on similar cases retrieved from thecase base. In related work on self-management of low-backpain (LBP) [Bach et al., 2016], CBR recommends care plansfrom similar patients. Management involves a human activityrecognition (HAR) component to monitor the patient activityusing sensor data that is continuously polled from a wearabledevice. Patient reported monitoring is used by the SelfBACKsystem to manage exercise adherence. Monitoring allows thesystem to detect periods of low activity behaviour, at whichpoint a notification is generated to nudge the user to be moreactive - the intervention. An important contribution of thiswork is the integration of behaviour change techniques suchas goal setting to focus the expected level of activity. There-after comparison of expected and actual behaviours analysegoal achievement.

Evidence for self-management of patients with multimor-bidities is limited, despite the prevalence of co-occurringconditions and its impact on patients and healthcare sys-tems [Smith et al., 2012]. Interventions tend to have mixedeffects requiring careful design underpinned by evidence-based practice. Personalisation is important to ensure thatcare plans are tailored to the needs of the individual. Al-

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though there has been recent work on personalised learningusing state-of-the-art learning architectures (e.g. matchingnetworks) more work is needed when applying them to in-dividuals with multimorbidities [Sani et al., 2018].

2.2 Pervasive and ubiquitous self-managementPervasive and ubiquitous AI enabled devices are arguablybest placed to continuously monitor a person’s adherenceto self-management plans, make real-time predictions aboutthe likelihood of adherence and the impact of that. How-ever intervention requires a good understanding of human be-haviours and direct inspection by health providers, which al-though valuable, cannot be scaled to large and diverse groupsof people. Wearables, such as smart watches or phones, arethe most common form of physical activity monitoring de-vices and sources of delivering digital interventions. Theseare embedded with inertial measurement devices (e.g. ac-celerometers or gyroscopes) that generate time-series datawhich can be exploited for human activity recognition of am-bulatory activities, activities of daily living, gait analysis andpose recognition [Sani et al., 2018; Reiss and Stricker, 2012;Chavarriaga et al., 2013].

Although commercial wearable activity trackers such asFit-Bit are increasingly being used to monitor levels of phys-ical activity and provide feedback to users, their utility is lim-ited. Accuracy in determining activity in people who walk atslow ambulatory speeds in free living conditions is low [Fee-han et al., 2018], and evidence of effects on physical activ-ity levels are uncertain [Lynch et al., 2018]. They have lim-ited interactive and personalisation options, due in part to areliance upon text notifications as the intervention method.As a direct result, current wearable technologies struggle tounderstand and address individual barriers to promoting be-haviour change in individuals with complex disabilities, pro-vide insufficient information to determine specific rehabilita-tion activities (such as exercises), and are not adapted to peo-ple with communication impairments. Importantly, provisionof real-time information on outdoor environmental hazards(e.g. stairways) and mental health issues (such as anxiety orlack of confidence) which are likely to impact physical activ-ity behaviours in patients is limited.

Another promising solution is to develop and employ so-cial robots that can be designed to offer psychological sup-port. In this context, Socially Assistive Robotics (SAR) is arapidly growing domain that aims to enhance psychologicalwell-being through human interactions with a robot. Robotscan be programmed to perceive and interpret human actionsand nonverbal cues, and provide assistance both at the levelof goal setting and tracking and socio-emotional communi-cation through personalised conversational dialogues. Theusefulness of social robots in mental healthcare contexts hasbeen investigated by a number of previous works [Rabbitt etal., 2015]. However, most of the available therapeutic roboticplatforms target supporting either children with special condi-tions such as autism or assisting elderly people in their dailylives. In particular, robotic platforms for patient educationand self-management interventions are still scarce. There area few lines of work focusing on self-management and aware-ness of type 1 diabetes in children [Kruijff-Korbayova et al.,

2014; van der Drift et al., 2014] and motivation coaching forhealthy living, weight loss and exercise [Leme et al., 2019].To the best of our knowledge, the application of social robotsin supporting individuals suffering multiple conditions has re-mained an unexplored area.

Computer vision can be used to detect, track, and re-identify patients without the need for any specific sensors ormarkers to be worn or carried. In their place, technical re-quirements include the need for the computer to infer the lo-cation, pose and movement of the trainee (and other people inits vicinity). Person following [Honig et al., 2018] is a key ca-pability for the machine to be able to observe and guide a pa-tient. For this purpose, unmanned aerial vehicles (commonlyreferred to as drones) may present a flexible solution; ratherthan fast-flying quad-copters, blimps may offer a more stableand safer platform [Yao et al., 2019]. Their use in related ap-plications such as monitoring older people in care homes hasbeen suggested (but not yet developed) [Srisamosorn et al.,2016]. However, the computer vision systems used need to bemade more robust and reliable before a flying socially-awarerobot for monitoring patients could be deployed and trialled,especially in less controlled environments outside the clinic.

A further potential solution in this field is Ambient As-sisted Living (AAL), which targets the use of multiple de-vices and sensors around the home to support personal health-care monitoring. AAL offers an opportunity for non-intrusivetracking of patient condition through smart home technol-ogy [Forbes et al., 2019]. Though traditionally difficult toapply this for accurate patient monitoring (particularly inopen areas), recent advancements demonstrate detailed activ-ity profiling can be gained from non-intrusive RFID chips sit-uated in locations around an individual’s home [Oguntala etal., 2019], even in large rooms [Obeidat et al., 2019]. Thoughwork has targeted AAL to support assisted living and fall pre-diction for the elderly [Massie et al., 2018], we are unawareof any current AAL approaches which comprehensively sup-port multimorbidities in every age group.

3 Generic self-management framework

Generic self-management frameworks need to be capable ofcovering a wide range of devices and conditions in orderto support personalisation, if trained models are to moveaway from the current one-size-fits all systems driven by cen-tralised datasets. Further they must ensure patient privacyand data security despite the large volumes of data neededfor training machine learning models that will provision edgecomputing devices and intelligent decision support systems.Frameworks must also be flexible, able to react to changesin the environment and adapt reasoning appropriately. Weargue the key to addressing these requirements is treatingself-management as an AI planning problem; where AI meth-ods support and recommend interventions centred around thelikely achievement of goals and actions recorded in plans. Toachieve this we need constructs that can be configured to agiven self-management plan; and a generic architecture thatcan support the reuse of these constructs to enable evidence-based community care.

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Monitor

Robot

Wearable device

Drone

Smart phone

Questionnaire

Medical record

...

Predict

HAR

Gait analysis

Exercises recognition

Mood analysis

Social interaction analysis

...

responses

angle of movement

Observe individual Compare Observed versus Expected

Reduce difference between Observed and Expected

therapy

Intervene

Educational material

Notifications

Haptic feedback

Conversation

...

Clinical Help

Figure 1: Instance of a blueprint for stroke rehabilitation configured with two Monitor-Predict-Intervene (MPI) component paths.

3.1 Reasoning with the MPI CycleWe propose inclusion of three components: digital Moni-toring (M) to track patient condition(s) and underpin antic-ipatory Predictions (P) to trigger real-time Interventions (I)for additional support when it is required. We refer to thecombination of these components as an MPI. Reasoning isfacilitated by a self-management plan consisting of guide-lines, health recommendations, patient goals, decisions anda trace of previous lessons learned. These lessons learneddata supports tailoring of plans to an individual. Given a self-management plan (e.g. level and frequency of exercise, tar-geted levels of anxiety), the MPI cycle monitors adherenceby a combination of patient self-reported outcome measures(e.g. pain) and automated real-time monitoring (e.g. of phys-iological response) by a variety of existing and yet-to-be-created devices (e.g. wearables, implants, in-home sensors,drones, robots). Differences in observed and expected adher-ence, combined with environmental and personal factors canbe used to predict likely trends and outcomes. Thereafter, au-tonomous and remotely supported proactive interventions byhealth professionals are initiated and plans are adapted col-laboratively, replacing the current reactive patient-clinicianinteraction.

An evidence-based approach to self-management is facili-tated by having access to previous plans and lessons learnedas well as reusing care-plans from similar patients that havebeen successful in relation to outcome measures. The rea-soning capability of the MPI increases with increasing adop-tion of the framework. As the richness of the evidence basegrows (from initial guidelines to personalised plans) the im-pact on the community and their common self-managementconditions will be improved.

Thus MPIs form the building blocks for a multi-facetedself-management plan - a plan that can cater for multimor-bidities. For example, consider Figure 1, which featurestwo MPIs created to support stroke rehabilitation. Strokesurvivors commonly struggle with freedom of movement intheir joints and have a tendency to develop depression dur-

ing treatment. In this image, a patient is managing their jointstrengthening exercises using an exercise-MPI (consisting ofthe components shaded orange), while managing their moodthrough an emotion-MPI (shaded blue). The exercise-MPImonitors bending of the joint via machine vision technologyand a smart phone camera. Reasoning on the monitored dataallows the system to predict whether the exercises are beingperformed correctly (e.g. the angle of movement is satisfac-tory). The system can then intervene by actuating haptic feed-back through a patient’s wearable sensor, guiding the patientto perform the exercise correctly. Similarly, the emotion-MPImonitors mood by reasoning on the patient’s responses toquestionnaires. If mood is predicted to be below the thresholddetermined by a clinician, an intervention can organise clini-cal help before this evolves into depression. However if moodis above this threshold, no intervention is necessary. The goalof each MPI is to Observe (O) the patient through monitor-ing to predict a comparison with the Expected (E) outcomeas established by their clinician. If the observed actions de-viate sufficiently from the expected, then an intervention isnecessary to minimise the difference (min(δ(O,E))).

3.2 Reusing self-management plansWe propose the idea of a blueprint through which an indi-vidual’s self-management plan for multimorbidities can beformulated. Essentially a blueprint is a combination of oneor more MPIs and their relationships (e.g. contraindica-tions) which has been configured jointly with a clinician. Asan example, the aforementioned Figure 1 shows a patient’sblueprint for the self-management of stroke rehabilitation, asit combines the exercise-MPI and the emotion-MPI.

The reasoning necessary when combining multiple differ-ent MPIs is an important area of research; it must ensure ad-herence to a patient’s collective self-management plans andsuggest interventions that are suitable for all the multimor-bidities, whilst maintaining knowledge of any contraindica-tions between those conditions. Central to this is a knowl-edge structure, an MPINet ontology, where known MPI re-

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lationships can be recorded and used to form generalisedblueprints. These can be refined by co-occurrences inferredfrom collected data.

A shared community of blueprints is formed from individ-uals who share the same or a similar set of co-occurring con-ditions. Configurations embedded in blueprints can then bereused and adapted to suit new individuals joining that com-munity (see Figure 2). In this image, blueprints have beenextracted from members of the community who are similarto the new individual, where ϕ is a reuse function containingadaptation knowledge. The output is a blueprint configuredto the individual’s needs founded on evidence-based practice.This can be formalised as:

O′, E′ = ϕ[(O,E)1, (O,E)2, ...] (1)

where O′ the configuration for what is to be observed and E′

represents the modified expectations in this new blueprint.

, …=

,M P I M P I M P I

ϕ

Figure 2: Reuse of blueprints from similar individuals.

Personalisation of blueprints is achieved through the rec-ommendation of contextually-relevant MPIs and customis-ing general community blueprints. Similarities in self-management goals within a community can be used for per-sonalisation and to anticipate known common complicationsof a condition, which can be extended to enable forecastingfor healthcare service demand. Metric learning algorithmsthat are suited for evidence-based reasoning lend well tolearning personalised models on the basis of similarity com-putations, and also lend themselves well to few-shot learningapproaches. Recent advances in similarity driven attentionmechanisms currently use simplistic metrics, hence in futureresearch we must establish how similarity on the basis of theuser contexts can be computed in a differentiating manner.

Configuration of blueprints shares many similarities withOn-Line Case-Based Planning (OLCBP). Both require con-tinuous monitoring of expected versus observed and actingupon deviations in a timely manner. Much like the snippetsthat exist within a plan [Ontanon et al., 2010], MPIs act asthe basic constituent pieces of a blueprint and are configuredwith individual goals. Though failure to achieve individualMPI goals does not necessarily define the failure of a self-management plan, we suggest this is an early warning of theneed for additional care. Importantly recording such failuresand the follow-on adaptation of both the self-managementgoal and agreed activities are useful experiential content thatare crucial when developing evidence-based practices for thecommunity.

3.3 Role of federated machine learningIdeally, MPI models will learn from the data generated by allusers of the system, i.e. in a distributed manner. This requiresus to adopt ideas from Federated Learning (FL) [McMahan etal., 2017] and meta-learning. Specifically maintaining con-trol of device data, where training involves a shared global

model under the coordination of a central server acting as acurator, selecting which participating devices (i.e. the fed-eration) to incorporate when training models. This form oflearning is ideal for community health care ensuring privacyby default, respecting data ownership, and maintaining lo-cality of data (without centralising data) for application de-ployment at scale. An interesting direction of research hereis to the use of data provenance records combined with met-rics evaluating quality and trust of individuals to influencethe global computational curator’s decisions on sampling ofMPIs on devices. With complex model architectures there isalso a need to share and describe the architectural propertiesso as to inform the curator about compatibility. This perhapscalls for a meta-language for architectural descriptors. Learn-ing from few labelled data is important for technology to op-erate at scale. It will be useful to extend the FL paradigmto evidence-based reasoning methods such as matching net-works [Vinyals et al., 2016] to enable few-shot learning whileusing a federated strategy.

This type of environment is potentially suitable for im-plementing a multi-agent framework [Moreno A, 2003],whereby autonomous, adaptive and interactive software com-ponents, provide notions that specifically meet the MPIchallenges to form the base of a robust and scalable self-management infrastructure. A multi-agent framework willalso enable the collection of patient-reported well-being in-formation (as done in [Ibrahim et al., 2015]), integratingthem with sensor and device-generated data via API-basedreal-time data collection and streaming platforms such as thein-house build RADAR-base platform [Ranjan et al., forth-coming 2019]. The combination will yield an intelligent andreal-time framework for schematised, secure and role-baseddata collection, harmonisation and integration based on uni-fied temporal intervals from all devices, sensors and individ-uals.

4 The Future of Digital Self-ManagementTackling self-management of multimorbidities requires amodel-aware and data-agnostic machine learning platform -a modular digital health ecosystem, where multiple monitor-ing technologies enable comprehensive predictions that trig-ger the most appropriate intervention strategy from a broadrange of possibilities. With that in mind, we suggest the pro-posed MPI architecture could answer many of the researchchallenges discussed for individual technologies in Section 2and greatly improve the self-management experience from auser perspective. However, there is also a need to considerhow this architecture could be practically utilised at scalewhile preserving patient privacy.

4.1 Wearables and Haptic FeedbackWe aim to develop a framework to allow wearable technolo-gies to provide real-time haptic feedback to support users ef-fectively perform rehabilitation exercises and physical activ-ities. Haptic feedback uses digital cues to stimulate the feel-ing of touch in users. Whereas current wearable technolo-gies are reliant on the user reacting to textual notificationsor messages, haptic feedback provides an opportunity to cor-rect execution of exercises on-the-fly. The result would be

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a decreased reliance on the external technology of a smartphone and less expectation for users to continually refer totheir phone throughout an exercise session.

There are two primary challenges to provisioning hapticfeedback remotely; (1) currently, haptic feedback for therapyis performed one-to-one, either with a healthcare professionalactivating the necessary feedback while locally observing thepatient or the skeleton following a pre-programmed set of in-structions; and (2) current telecommunication networks donot have the throughput required to enable such feedback re-motely at scale. We believe that the former research chal-lenge could be answered through development of a learnedmodel which can provide activation of the necessary compo-nents to actuate haptic feedback. The latter challenge may beanswered with effective advanced networking infrastructures,such as 5G.

4.2 Robots and Conversational DialogueThe use of therapeutic robots to support community-dwellingindividuals participate in rehabilitation, fitness exercises, so-cial engagement and outdoor physical activities are not onlya viable solution to the problem of shortage in care resources,but also potentially address loneliness in the elderly. How-ever, current SAR systems are limited in their capability to of-fer truly personalised support. They do not take the user stateinto consideration, beyond verbal and interaction logs, sufferfrom low engagement, and uni-directional information flow.Expanding this to exchange more information between theuser and the system through multiple modalities will enable amore versatile and natural interaction, ensuring that the usermaximally benefits from the system. In addition, it provides ameans to build a richer user-context that can help to improve,structure and personalise a robot’s behaviours and conversa-tional dialogue. To address these issues, novel methods areneeded for semantically integrating various data sources re-lating to (1) mood evaluations based on visual cues (i.e., fa-cial, bodily cues); (2) physical performance, psychologicalvariables and environmental variables collected from wear-ables; and (3) adaptive activity programmes and behaviourchange strategies in real-time. Integrated information can bethen used to build effective reasoning and intervention mech-anisms for the interactive robot companion that can supportcitizens to lead active and social lives.

4.3 Autonomous Computer VisionComputer-vision models can provide visual feedback to sup-port patients with rehabilitation activities. This involves theanalysis of large volumes of imagery data, recognition offine-grained human movement details and generation of vi-sual feedback interventions. Drones could offer a rich sourceof information for on-going monitoring and observation ofpatients in different contexts, including both indoor self-management activities and outdoor exercises. Utilising a vi-sual source that captures patient’s movement and behavioursfrom different angles provides a unique opportunity for learn-ing and improving practices, but at the same time poses somekey challenges. These are due to the inherent vision prob-lem, in particular, performing accurate detection, trackingand classification under different poses and light conditions,

which can be exacerbated by the freedom of movement af-forded to a drone. Additionally, the anticipated skewed dis-tribution of the data will pose another challenge that needsto be addressed. In particular, having enough representativetraining examples that captures the different scenarios of aspecific case study or patient can potentially be very diffi-cult, meaning that few-shot learning strategies are likely to bevery relevant here. The benefits for patients go beyond sup-port with performing exercises and documenting their self-management progress; for example, people with movementdifficulties are often anxious about walking in unfamiliar en-vironments as they are unaware of any obstacles they mayface - this can lead them to withdrawing from social activitiesand an increased feeling of loneliness. Being accompaniedby a drone that advises them of upcoming obstacles and howthey can be avoided, may reduce anxiety associated with suchsituations.

4.4 Ambient Assisted LivingWe aim to use Ambient Assisted Living (AAL) technology tosupport non-intrusive monitoring of patients within their ownhome. Several interesting research challenges arise from thisfield. In particular, it will be interesting to see how the con-stant monitoring of AAL devices allows the identification ofself-management activities as they take place within aspectsof daily living. For example, if a patient has climbed the stairs5 times in an afternoon as part of cleaning their home, thisaction may cover some of the ambulatory physical activityscheduled as part of managing their lower-back pain condi-tion. Understanding the situations in which daily living activ-ities can replace self-management exercises (or alternatively,the situations in which they cannot) is an intriguing avenuefor exploration. Furthermore, there is the technical challengeof being able to seamlessly change monitoring devices thatfeed into a single MPI e.g. as the patient transitions fromone room to the next. A comprehensive self-managementsolution involving AAL devices should maximise comfortof the patient and ensure that they are able to relax withintheir home without feeling like they are being continuallyobserved. Developing an individual’s trust requires furtherunderstanding of human machine interfaces by bringing to-gether behavioural psychologists and computer scientists.

4.5 A Comprehensive Self-Management SolutionThe distributed MPI model comprises a number of devicescollaborating autonomously, and a substantial amount of real-time data generated passively by sensors and devices or ac-tively reported by the users (e.g. well-being reports) for a sin-gle non-fragmented self-management solution. As such, theMPI framework constitutes a decentralised, heterogeneousand open environment that operates on multiple computingsystems to manipulate, share and analyse strictly-confidentialpatient records. Moreover, to build a platform to continu-ously inform patients using their own records, requires mech-anisms to mine the ‘big’ and heterogeneous data for relevantknowledge, and learn the adapted and personalised recom-mendations and possible interventions in real-time to aid inself-management. These methods are underpinned by thegrowing availability of advanced networking technology (e.g.

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5G), where the faster connection speeds promise huge bene-fits to future healthcare systems. Such advances allow high-intensity data processing to be supported by near real-timedecision-making. We believe these will play a crucial role insupporting at-home care (e.g. wearable sensors applicationsand online consultations) and remote treatments (e.g. digitaldiagnosis). In particular, 5G will facilitate the transfer of ex-pertise over a great distance in real-time using technologiessuch as robotics and haptic feedback.

To give an example, a stroke survivor patient may use AALsensors for non-intrusive tracking of physical activity and in-teract with a SAR to self-report on their pain and mentalcondition while in the home. When journeying outside thehouse, the patient initially uses a drone with computer vi-sion capabilities to monitor their journey, before transitioningto use of a wearable technology as their walking ability im-proves. The combination of monitoring technologies to em-power interventions is a comprehensive solution to their self-management. We believe that not only will this greatly im-prove adherence to the various aspects of a self-managementplan, it will also have an irreplicable effect on patient qualityof life. Furthermore, integration with the MPI framework willbring AI power to off-the-shelf hardware rather than buildingexpensive devices, thus encouraging socially-inclusive care.

5 ConclusionIn conclusion, in this position paper we have discussed theneed for an affordable comprehensive self-management solu-tion to improve quality of life for patients living with multiplemorbidities. We have presented our ideas towards the cre-ation of MPI components - constituent pieces of wider-scaleframework which allows a configurable and personalised self-management plan for individual patients, while encouragingreuse plans from within a community of similar patients andmorbidities. The platform and MPIs potentially offer sev-eral interesting avenues of research, not only around how newand existing technologies can be integrated, but also aroundareas such as comparing the similarity of MPIs and under-standing the ramifications of using them for reuse and adap-tation. Most importantly, we believe that a wide-scale inte-grated framework of devices is necessary for supporting pa-tients with confidently managing their condition(s), improv-ing their quality of life in the future.

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