An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical profile and to known 'risky' profiles to support recommendation of evidence-based interventions. Maintaining trust presents an XAI challenge because the recommendations are not easily interpretable. Trust in the system can be improved by making the decision-making process more transparent. We propose a visualisation workflow which presents the data in clear, colour-coded graphs

Craw, Susan

Forbes, Glenn

Massie, Stewart

English

arXiv

Open Access Institutional Repository at Robert Gordon University

FORBES, G., MASSIE, S. and CRAW, S. 2021. Visualisation to explain personal health trends in smart homes. Presented at 1st eXplainable artificial intelligence (XAI) in healthcare international workshop 2021 (XAI-Healthcare 2021), 16 June 2021, co-located with 19th Artificial intelligence in medicine (AIME) international conference 2021 (AIME 2021), 15-17 June 2021, [virtual conference]. Hosted on ArXiv [online], article 2109.15125. Available from: https://arxiv.org/abs/2109.15125  This document was downloaded from https://openair.rgu.ac.uk Visualisation to explain personal health trends in smart homes. FORBES, G., MASSIE, S. and CRAW, S. 2021 Visualisation to Explain PersonalHealth Trends in Smart HomesGlenn Forbes , Stewart Massie , and Susan Craw{g.r.forbes, s.massie, s.craw}@rgu.ac.ukRobert Gordon University, Aberdeen, UKAbstract. An ambient sensor network is installed in Smart Homes toidentify low-level events taking place by residents, which are then anal-ysed to generate a profile of activities of daily living. These profiles arecompared to both the resident’s typical profile and to known “risky” pro-files to support recommendation of evidence-based interventions. Main-taining trust presents an XAI challenge because the recommendations arenot easily interpretable. Trust in the system can be improved by makingthe decision-making process more transparent. We propose a visualisa-tion workflow which presents the data in clear, colour-coded graphs.Keywords: Visualisation · Healthcare · Smart Home1 IntroductionArtificial Intelligence is a powerful technology with many potential applicationsin healthcare. IoT sensor networks combined with long term data collection arestarting to be adopted in Smart Homes as a new approach for health monitoring.As with many of the AI applications used for decision making, Smart Homehealth monitoring systems are black box systems that give little explanation forwhy specific interventions are suggested. Insights generated from these systemsare often unclear or impossible for humans to understand and so introduce thedanger of reducing user trust in a system which aims to help. One approachto inspiring confidence and trust is to improve transparency in the decision-making process so that it is explainable to stakeholders, including residents,family members and carers.Many countries are facing an ageing population which puts additional strainson health and social services with a smaller proportion of working populationavailable to support services. In this changing scenario it is important to helppeople with mobility or social needs to live independently for longer, and soreduce their reliance on more expensive health care solutions. We examine oursolution for Smart Homes to support assisted living environments by identifyingand reporting on resident health trends. Sensors are installed in new-build SmartHomes with the aim of supporting tenants to live independently for longer.Specifically, in this paper, we address one of the main challenges of explanationby visualising the decision process of a real-world health monitoring system thatsenses and predicts the level of risk of falling attributed to Smart Home residents.These predictions can then be relayed through dashboards using simple graphs.2 G. Forbes et al.2 Related WorkActivities of Daily Living (ADLs) are events in daily life which would be con-sidered intrinsic to a person’s ability to live independently. ADLs include beingable to dress, get out of bed, and feed oneself. Katz [4] originally proposed theterm along with a scale for rating a person’s independent ability using theirperformance in simple ADLs. The concept of losing ADLs as we age has in-fluenced future research in the field by identifying that specific ADLs are moreindicative of reduced capability than others. IoT offers an opportunity to providecontinuous behavioural and physiological monitoring of residents.Once activities are being regularly tracked in a home environment, the resi-dent’s routine behaviours emerge [1]. Changes in these routines compared to theresident’s historical performances can be indicative of encroaching health prob-lems. Ongoing aberrant behaviour may require healthcare interventions. Howeverthe direct link between the resident’s behaviours and the relevant interventionsmay not always be immediately clear to an inexperienced observer [6]. Visuali-sations can be used to aid stakeholders in interpreting the complex sensor datainsights [5].3 Smart Home Activity ProfilesFitHomes is an initiative, lead by Albyn Housing Society Ltd to support indepen-dent living with the supply of custom-built Smart Homes fitted with integratednon-invasive sensors. Sixteen houses have been built at Alness near Inverness,Scotland, with an additional eight retrofit houses in Nairn. The initial projectfocused on using sensor data to develop a prototype fall prediction system forthe residents of FitHomes, and now aims to monitor a wider range of long termhealth conditions over time. One aspect is to identify increased risk of falls andso a key focus for monitoring is to identify activity levels, patterns, and speeds.However, monitoring can go beyond just movement to consider other factors thathave been shown to be related to falls, including dehydration, tiredness, and men-tal health. Gaining information on these additional factors requires monitoringto also capture data on more general activities such as eating & drinking be-haviours, sleep patterns, and toileting & grooming habits. With these criteria inmind a range of sensors have been selected including: IR motion sensors; contactsensors to capture room, cupboard, and fridge door opening; pressure sensorsfor bed & chairs; electricity smart meters to identify power usage pattern; floatsensors identifying toilet flushing; and humidity sensors to identify shower use.Most of the sensors chosen have a binary output which simply activateswhen the event they are monitoring takes place e.g. a door opening; howeverothers output continuous readings provided at fixed polling rates. The data fu-sion task across multiple sensors with different output modes becomes one of themain challenges. We employ a two stage process: the first is to generate activityprofiles, see Figure 1, and uses pattern identification to create meaningful rep-resentations from the raw sensor data that captures the residents’ activities e.g.Visualisation to Explain Personal Health Trends in Smart Homes 3Fig. 1. Identifying activities from sensor activations.sleeping, showering, cooking; and then the second stage is to assemble these ac-tivities into personalised daily profiles. The second stage is the analysis of theseprofiles, see Figure 2. Changes in the resident’s own activity patterns can thenbe used to detect deterioration in health, while comparisons with the patternsof other Smart Home residents can provide benchmark measures. The data thussupports evidence-driven intervention tailored to the resident and their specificcircumstances.4 Reasoning with ADLsIdentifying ADLs in themselves does not give an indication of health. However,it has been shown that functional assessment is an effective way to evaluate thehealth status of older adults [2]; ADLs are lost as we age, and FITsense monitorschanges in ADL activity as an indicator of deteriorating health and increasedrisk of falls. A CBR approach is adopted. In our scenario, a set of ADL templatesis combined with contextual information to form the problem representation toretrieve similar profiles from a case base.Figure 2 presents an overview of our approach. Low-level, time-stampedevents identified by the sensors are transformed into a daily user profile. The pro-files are based on a set of features extracted from ADL sequences, with mixeddata types: some features are binary, e.g. disturbed sleep; some features arecounts, e.g. number of room transitions or stand up from seat count; some arecumulative daily time spans, e.g. time sitting; while others are numeric, e.g. av-erage gait speed. Whatever the data type a similarity measure is associated witheach feature so that comparison can be made between them. A set of daily ADLprofiles for a resident can then be compared with those in the case base, on theright of Figure 2. Retrieval of similar profiles labelled as at risk identifies theneed to recommend intervention, and falling similarity with the user’s own previ-ous profiles identifies changing behaviours. Importance in determining similarityfor FITsense is given to features known to correlate with falls. For other healthconditions the similarity knowledge could be refined to reflect specific conditionse.g. repetitive behaviour for dementia, general physical activity level for obesity,etc.4 G. Forbes et al.Fig. 2. CBR Approach to Identifying ’Risky’ Behaviours5 ExplanationCase-based Reasoning systems can facilitate comparisons between cases, whichis important to explanation because they are real examples. However, in thisform their representation is complex, formed as a time series of behaviours.In order for users to establish meaning from this data, they have to manuallyinterpret it and interrogate differences. While this may be a viable option forhealthcare professionals, it is both not easily accessible, or an efficient use oftime. Furthermore, this approach does not consider explanation for whether anintervention would be necessary. To consider explanation both for behaviouralobservations and recommended healthcare interventions, we need to present ahigh-level summary of aspects of behaviour which could be indicative of fall risk.Six main features were selected for fall risk determination: Sleep, Sleep Dis-turbances, Room Transitions, Activity, Wandering, Toilet. These features beingidentified by investigation of fall risk indicators, which are typically highlightedthrough GP and hospital testing [3]. Each feature is extracted from profiles byidentifying count and duration of specific ADL performances or sequences, fromwhich linear scores can be calculated. Factor scores are generated using an upperand lower threshold initially established using expert domain knowledge and canbe refined as additional data is collected.We present a radar chart visualisation of a resident’s fall risk profile in Fig-ure 3. By linking each of the profile features, we generate a summary as a snap-shot of the current fall risk factors of the resident. This can be compared tohistorical profiles from the same or similar residents, or to a risky profile. A pro-totypical risky profile example is displayed in the figure, which was generatedthrough consultation with healthcare professionals. As the system progressesrisky profiles will be generated using personalised thresholds. In the example,we can see the resident could be considered as at risk based on their Toiletand Sleep scores. This shows that there are issues with the resident’s behaviourwhich need to be addressed, such as low quality sleep and over-frequent toiletuse. These profiles can also be compared over time to view long term trends inresident health factors.Visualisation to Explain Personal Health Trends in Smart Homes 5Fig. 3. Radar Chart visualisation of a resident’s daily fall risk profile.6 ConclusionsAI systems for behavioural-based health monitoring can produce meaningful in-sights, however many are considered faceless, black-box systems. We have devel-oped an explanation and accompanying visualisations which make output froma complex system more transparent and accessible to stakeholders. This shouldincrease confidence and trust in our AI healthcare systems.In future work, we aim to establish feature sets for a wider range of long-term health conditions, such as dementia and cancer. We also plan to continuereworking and refining the existing feature set to ensure robustness as the systemlearns from ongoing data collection.Acknowledgements This work was funded by Albyn Housing Society, and theScottish Funding Council via The Data Lab innovation centre.References1. Aipperspach, R., Cohen, E., Canny, J.: Modeling human behaviour from simplesensors in the home. Proc. Of The IEEE Conf. On Pervasive Computing 6030(783),62–79 (2010). https://doi.org/10.1007/978-3-642-12654-32. Cook, D.J., Schmitter-edgecombe, M.e.a.: Analyzing Activity Behavior and Move-ment in a Naturalistic Environment Using Smart Home Techniques 19(August),1882–1892 (2015). https://doi.org/10.1109/JBHI.2015.24616593. Forbes, G., Massie, S., Craw, S.: Fall prediction using behavioural modelling fromsensor data in smart homes. Artificial Intelligence Review 53(2), 1071–1091 (2020)4. Katz, S., Ford, A.B.e.a.: The index of ADL. Journal of the American Medical As-sociation 185, 914–919 (1963). https://doi.org/10.1001/jama.1963.030601200240165. Massie, S., Craw, S., Wiratunga, N.: Visualisation of case-base reasoning for expla-nation. In: Proceedings of the ECCBR. pp. 135–144 (2004)6. Panigutti, C., Perotti, A., Pedreschi, D.: Doctor xai: an ontology-based approachto black-box sequential data classification explanations. In: Proceedings of the 2020conference on fairness, accountability, and transparency. pp. 629–639 (2020)

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