Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data Rafael Poyiadzi*1 and Weisong Yang*2 and Yoav Ben-Shlomo 3 and Ian Craddock 4 Liz Coulthard 5 and Raul Santos-Rodriguez 6 and James Selwood7 and Niall Twomey 8 Abstract. There is a pressing need to automatically understand the adequate temporal resolution to understand disease progression and state and progression of chronic neurological diseases such as de- deterioration since AD has high variance across patients. mentia. The emergence of state-of-the-art sensing platforms offers Some symptoms are prevalent across dementias. Confusion can unprecedented opportunities for indirect and automatic evaluation of lead to unpredictable behaviour and activity abandonment during disease state through the lens of behavioural monitoring. This paper normal daily routines. Interestingly, behavioural interactions between specifically seeks to characterise behavioural signatures of mild cog- persons with dementia (PwD) and their carers/family are also affected nitive impairment (MCI) and Alzheimer’s disease (AD) in the early by disease progression and efforts by PwD to allay confusion and stages of the disease. We introduce bespoke behavioural models and uncertainty often result in them persistently seeking the company of analyses of key symptoms and deploy these on a novel dataset of persons whom they trust. In this work we intend to address shadowing longitudinal sensor data from persons with MCI and AD. We present [23] (persistently staying in the company of a trusted carer/family), preliminary findings that show the relationship between levels of sleep wandering [24] (going from room to room with nonspecific intent) quality and wandering can be subtly different between patients in the and disturbed sleep [15] (a common symptom for PwD). early stages of dementia and healthy cohabiting controls. Progression of dementia and MCI are characterised by an increase in symptoms that will affect normal daily activity and behaviour. It is important to remember that these symptoms affect normal daily activ- 1 Introduction ity and behaviour. In understanding the dependence between normal behaviour and the expression of symptoms, an opportunity to under- Dementia is a progressive neurological condition affecting cognition stand and quantify disease state indirectly via behavioural change is and behaviour with a significant impact on activities of daily living. It exposed. Modern sensing technology offers promise in monitoring is one of the major causes of disability and dependency among elderly daily behaviour, extracting symptom expression rates and measure- population with approximately 50 million people with the disease ment of state and progression of the disease [7, 30]. In contrast to world wide. Alzheimer’s disease (AD) is the most common cause of normal clinical evaluation that occur in foreign environments, ours is dementia. Mild cognitive impairment (MCI) refers to a decline that achieved from the patients’ residences. has minimal impact on activities of daily living [13]. Patients with This paper outlines novel computational behavioural analysis al- MCI can continue to live independently [26]. Not all patients with gorithms for modelling disease state and progression. The potential MCI will convert to dementia, but they are at increased risk. MCI has for detecting unseen behavioural bio-markers of the early stages of an annual conversion rate to AD of between 5 and 10% [27]. dementia is exposed by analysis on the longitudinal, in-home sensor Making an accurate, reliable diagnosis of dementia is a challenge. data. Section 2 reviews prior work on smart homes and dementia. Our Patients may find cognitive tests stressful, which impacts their perfor- data collection and modelling procedures are outlined in Section 3. mance. This may be exacerbated in a new, unknown, clinical environ- Results are presented in Section 4 and we conclude in Section 5. ment with an unfamiliar clinician. Performance on cognitive tests also does not give an indication of how someone is managing in the real world. Although diagnosis may include bio-markers measurement, 2 Related Work in addition to this being invasive and expensive, these do not have Numerous smart systems have been designed and developed to moni- 1 University of Bristol, England, email: rp13102@bristol.ac.uk tor the well-being and health status of elders, such as the GATOR Tech 2 University of Bristol, England, email: ws.yang@bristol.ac.uk Smart House [14], the AWARE home [1], the Microsoft’s EasyLiving 3 University of Bristol, England, email: y.ben-shlomo@bristol.ac.uk 4 University of Bristol, England, email: ian.craddock@bristol.ac.uk project [4] and the MavHome Project [6]. In the dementia domain, 5 University of Bristol, England, email: elizabeth.coulthard@bristol.ac.uk [22] introduced a support system to aid doctors diagnosing dementia. 6 University of Bristol, England, email: enrsr@bristol.ac.uk Participants only needed to perform a selection of Instrumental Activ- 7 University of Bristol, England, email: james.selwood@bristol.ac.uk ities of Daily Living (IADL) in a smart home environment. However, 8 Cookpad Ltd, and University of Bristol, England (honorary), email: niall- the main limitation is that this approach relies on data collected in a twomey@cookpad.com laboratory environment and not in a real world setting. * Authors contributed equally Along these lines, [3] undertook a study on data acquired by The Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). This volume is ORegon Centre for Aging and TECHnology (ORCATECH) at the published and copyrighted by its editors. Advances in Artificial Intelligence Oregon Health and Science University, that investigated the walking for Healthcare, September 4, 2020, Virtual Workshop. speed and general activity in the homes of participants in order to detect dementia. The extension presented in [2] modelled the pres- mathematical terms we assume that training and testing distribu- ence of the participants in different rooms using Poisson processes to tions differ, i.e. 𝑃𝑡𝑟 (𝑥) ≠ 𝑃𝑡𝑒 (𝑥), but that conditional distributions demonstrate statistical differences between different states of cogni- remain the same 𝑃𝑡𝑟 (𝑦|𝑥) = 𝑃𝑡𝑒 (𝑦|𝑥). MMD offers an elegant ap- tion. Localisation has been found to be a common ground for different proach to quantify and match distributional shift and we follow the works. For instance [18] and [29] use an Ultra Wideband device to try methodology outlined by [17] in our work. to measure wandering behaviours. However, Radio Frequency (RF) identification may be limited by its detection range since unpowered 3.2.2 Semi-Supervised Learning RF devices operate only over a meter’s distance. Also, [10] uses a relatively complex sensor network consisted of RF tracking and mo- Since only a small portion of labelled data are available, we leverage tion and heading sensors for monitoring and studying behavioural supervised (L 𝑆𝐿 ) semi-supervised (L 𝑆𝑆𝐿 ) objectives to make effi- patterns of patients with dementia. cient use of data. The supervised objective (L 𝑆𝐿 ) is the traditional We build upon the existing literature by focusing on data collected conditional log-likelihood that one optimises for in Conditional Ran- in the wild that, while more challenging to analyse, also provides dom Fields (CRF) [31]. The data and labels for this are obtained from a richer and pervasive perspective on the subjects’ behaviour. Addi- the walkthrough that was introduced in the previous section. CRF tionally, our aim is to automatically detect behavioural symptoms that model sequence probabilities as: may suggest early dementia. Finally, we want go beyond the analysis ! 𝑇 of a single participant and incorporate the monitoring of interactions −1 Õ 𝑃 𝜃 0 ( 𝒚| 𝑿) = 𝑍 ( 𝑿) · exp 𝜙𝑡 (𝑥 𝑡 , 𝑦 𝑡 , 𝑦 𝑡−1 ) (1) within the home environment as a key element to understand the 𝑡=1 progression of the disease. where 𝜙𝑡 are the log-potentials and 𝑍 ( 𝑿) is the partition function. In 3 Methods our case the log-potentials decompose, to emission function and tran- sition function, as follows: 𝜙𝑡 (𝑥 𝑡 , 𝑦 𝑡 , 𝑦 𝑡−1 ) = 𝑢 (𝑥 𝑡 , 𝑦 𝑡 ) · 𝜏(𝑦 ˜ 𝑡 , 𝑦 𝑡−1 ), This section introduces our data collection and modelling pipelines. where: 3.1 Data collection  𝜏(𝑦 𝑡 , 𝑦 𝑡−1 ), 𝛼(𝑡) >= 𝜖 0 ˜ 𝑡 , 𝑦 𝑡−1 ) = 𝜏(𝑦 (2) Ethical approval was secured from the Carmarthen Research Ethics I 𝑦𝑡 =𝑦𝑡−1 , otherwise Committee and the Health Research Authority (HRA) to record sensor where 𝛼(𝑡) is the activity level of the participant at time 𝑡 (calculated data from the homes of people who have received a diagnosis of as the average absolute jerk of the tri-axial signals) and 𝜖 0 denotes a dementia or have MCI for up to 12 months using the SPHERE [36, threshold, below which we assume there is no movement. This forbids 35, 8] system. Informed consent was obtained to analyse this data to location transitions unless a certain activity potential is surpassed. determine links between behavioural patterns, residents of the home The emission potentials are modelled with a 4-layer fully- and disease state. Participants are recruited from North Bristol NHS connected neural network, 𝑢(𝑥, 𝑦) = ℎ(𝑥) 𝑦 , i.e. the 𝑦-th element Trust, the Dementia Wellbeing Service in Bristol and the Research of ℎ(𝑥). The semi-supervised objective (L 𝑆𝑆𝐿 ) constructed by itera- Institute for the Care of Older People (RICE) Centre in Bath. The tively using the most likely label sequence as temporary targets, i.e. SPHERE sensor system is installed into their homes [36, 35]. 𝒚 ∗ = arg max𝒚 ∈Y 𝑃(𝒚| 𝑿) [28]. The key sensors that we use in the analysis of this work are ac- celerometers [11, 12, 34]. These are set record at a rate of 20 Hz. This data is broadcast to gateways by means of radio links. Six gateways are 4 Findings installed in every house. These simultaneously record accelerometer In this section we present findings relevant to sleeping disturbance, readings and the Received Signal Strength Intensity (RSSI). wandering and shadowing. We illustrate the analysis using 2 different In-home localisation is a key task in this work that leverages the houses that are inhabited by either a PwD, or by someone who has RSSI data [20, 5, 19]. Labelled data are acquired from a ‘walkthrough’ been founds to have MCI, and one partner. Data collection is ongoing, script that is performed by the technician upon installation. Each room so these are preliminary findings and some houses have significantly is visited while holding the wearables and RSSI and wearable data more data than others. The analyses below result from localisation are simultaneously recorded. The real-time location of the technician and activity level predictions that were trained and validated on the is recorded with an annotation app. walkthrough data. To regress from sensor data to localisation and activity predictions, we extract basic features from windowed data (5 seconds length with overlap of 2.50 seconds following [33]). Features include: mean, stan- 4.1 Shadowing dard deviation (std), max, min, diff (first-order difference operator) The mutual information (MI) between the location of the two resi- and the count of missing values. dents in each house is measured to estimate shadowing, following a We can complement labelled data by introducing domain knowl- similar approach to that described in [32]. Additionally, in our analy- edge into the analysis by suggesting reasonable locations based on sis we stratify MI by time of the day in order to understand whether calculable context. For example, if predicting ‘sleep’ at 3AM it is significant MI is confined to particular intervals, e.g. morning Fig.1. likely that the resident is in the bedroom. Temporal lags were introduced in order to allow for short delays in shadowing, with no significant change, and are therefore not included 3.2 Modelling considerations in this work. This is likely due to the fact that the lag interval is significantly shorter than the analysis window. Looking at Fig.1, in the 3.2.1 Covariate Shift case of house B there is no trend of increasing/decreasing correlation. We account for our expectations of covariate shift (due to disease Even though in the case of house A, there is a slight increase over progression) with Maximum Mean Discrepancy (MMD) [17]. In time, stronger conclusions will be drawn when more data arrives. 14 10 12 8 10 6 8 4 6 4 2 2 0 0 10 14 12 8 10 6 8 4 6 4 2 2 0 0 9 0 1 2 3 4 5 6 7 1 5 9 1 5 9 3 7 1 9-0 7-1 9-0 7-2 9-0 7-2 9-0 7-2 9-0 7-2 9-0 7-2 9-0 7-2 9-0 7-2 9-0 7-2 9-2 9-2 9-2 0-0 0-0 0-0 0-1 0-1 0-2 201 201 201 201 201 201 201 201 201 9-0 9-0 9-0 9-1 9-1 9-1 9-1 9-1 9-1 201 201 201 201 201 201 201 201 201 00:00:00- 08:00:01- 14:00:01- 20:00:01- 00:00:00- 08:00:01- 14:00:01- 20:00:01- 06:00:00 10:00:00 18:00:00 22:00:00 06:00:00 10:00:00 18:00:00 22:00:00 06:00:01- 10:00:01- 18:00:01- 06:00:01- 10:00:01- 18:00:01- 08:00:00 14:00:00 20:00:00 08:00:00 14:00:00 20:00:00 (a) House A (b) House B Figure 1: MI derived for wandering, separated in different segments of the day. 120 100 80 00:00:00- 06:00:00 60 40 20 0 400 300 06:00:01- 10:00:00 200 100 0 8 9 0 1 2 3 4 5 6 7 8 8 9 0 1 2 3 4 5 6 7 8 7-1 7-1 7-2 7-2 7-2 7-2 7-2 7-2 7-2 7-2 7-27-1 7-1 7-2 7-2 7-2 7-2 7-2 7-2 7-2 7-2 7-2 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-09-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 201 201 201 201 201 201 201 201 201 201 201201 201 201 201 201 201 201 201 201 201 201 MCI Control bedroom 1 kitchen 1 total hall 1 living room 1 (a) House A 140 120 100 00:00:00- 06:00:00 80 60 40 20 0 500 400 06:00:01- 300 10:00:00 200 100 0 1 5 9 1 5 9 3 7 1 1 5 9 1 5 9 3 7 1 9-2 9-2 9-2 0-0 0-0 0-0 0-1 0-1 0-2 9-2 9-2 9-2 0-0 0-0 0-0 0-1 0-1 0-2 9-0 9-0 9-0 9-1 9-1 9-1 9-1 9-1 9-1 019-0 019-0 0190-019-1 019-1 019-1 019-1 019-1 019-1 201 201 201201 201 201 201 201 201 2 2 2 2 2 2 2 2 2 Dementia Control bedroom 2 kitchen 1 total dining room 1 living room 1 (b) House B Figure 2: The total activity per room for House A (upper) and House B (lower). 0.030 0.05 0.025 0.04 00:00:00- 00:00:00- 06:00:00 06:00:00 0.020 0.03 0.015 0.02 0.010 0.01 0.005 0.00 0.030 0.05 0.025 0.04 06:00:01- 06:00:01- 10:00:00 10:00:00 0.020 0.03 0.015 0.02 0.010 0.01 0.005 0.00 7- 19 7- 20 7- 21 7- 22 7-23 7-24 7-25 7-2 6 7-2 7 9-2 1 9-2 5 9 1 9-2 0-0 0-0 5 0-0 9 0-1 3 0-1 7 0-2 1 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-0 9-1 9-1 9-1 9-1 9-1 9-1 201 201 201 201 201 201 201 201 201 201 201 201 201 201 201 201 201 201 MCI Control Dementia Control (a) House A (b) House B Figure 3: The complexity of localisation. 50 60 70 RSSI 80 90 100 110 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 0 :00 0 :30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 25 0 MCI Control kitchen_1 bedroom_1 living_room Figure 4: Visualisation of sleep disturbance for House A, and PwD (left), partner (right) 00:00:00-06:00:00 0.773 06:00:01-08:00:00 0.773 0.015 0.06 0.010 0.04 0.005 0.02 08:00:01-10:00:00 10:00:01-14:00:00 0.15 0.667 0.06 0.550 0.10 0.04 0.05 0.02 0.00 0.00 14:00:01-18:00:00 18:00:01-20:00:00 0.08 0.773 0.100 0.870 0.06 0.075 0.04 0.050 0.02 0.025 0.00 0.000 20:00:01-22:00:00 22:00:01-23:59:59 0.03 0.864 0.020 0.682 0.02 0.015 0.010 0.01 0.005 20 28 09 14 19 20 28 09 14 19 09- 09- 10- 10- 10- 09- 09- 10- 10- 10- Dementia Control Figure 5: House B - Comparing complexities of localisation. Our data collection is ongoing, and as it grows we will identify finer, compared to that of the other participant during 0-6 am. Although more informative signals of disease state. Time lags are likely to there is not a significant difference between activity levels in bedroom become more relevant with finer and more targeted analysis that may 1 of the two inhabitants during 0-6 am in Fig. 2a, it is because that the be motivated by changes in behaviour as the disease progresses. person with MCI got up earlier than 6 am, especially on July 23rd, 24th and 25th, and started walking in the house while the partner was 4.2 Wandering sleeping, which may suggest the increased sleep disturbance in the person with cognitive disorder from another angle. The approach for modelling wandering is motivated by its antonym: For reference, we also include a segment of the RSSI data in Fig. 4, non-wandering is typified by low activity levels and persistent loca- from House A, for both participants, from the early hours of the day. tion predictions. Thus we jointly compare the activity levels and the It highlights the difference in sleeping quality, as observed through location complexity of residents in this section. Specifically, in Fig. RSSI. The step changes are a result of rotations during sleep. It also 2 we track the total activity per room, as well as the total activity of shows how different sleeping positions can lead to missing values in the day, over the span of the data, and in Fig. 3 we track the complex- certain gateways, e.g. kitchen_1 at 02:00 and at 03:15. ity of the localisation predictions, derived from the Lempel-Ziv [21] complexity measure. It is clear in Fig. 2b that the activity levels of the patient are higher than that of the other participant in this house, and 5 Conclusion and Future Work Fig. 3b gives similar results, although in Fig. 2a and Fig. 3a it is less This paper presents our preliminary results and shows the poten- obvious, for which there might be several reasons. One explanation tial of detecting early symptoms of cognitive disorders automatically could be that the data we have so far for house A spans only a short by utilizing data acquired from patients’ daily activities and signal time period (less than 10 days). A second cause might be that the processing methods equipped with machine learning techniques. We patient in house A only has MCI, whose daily life only gets affected build a machine learning pipeline that considers the difference in to a mild degree compared to the participant with dementia in house training and testing distributions using MMD. We account for the se- B. quential nature of data using a CRF, and use our domain knowledge Additionally, Fig. 5 shows in detail the complexity of localisation to enhance our training data with data coming from the participants. predictions in different time intervals over the experiment span in One of the limitations of this study is that our localisation preci- house B. On every subplot, the number at the top-right is the average sion can only reach room-level for now due to the configuration and number of times the patient has a higher complexity than the partner. the deployment of our sensor system. Finer-grained localisation will In this analysis, we only include house B, as the span is longer. In expose behavioural reactions to furniture placements across rooms. the case of house A these averages are not so much in favour of the Potential directions for improvements include taking into consid- patient - 5/8 segment as compared to 8/8 for house B. eration environmental sensors, such as Passive Infrared and Video, through machine learning techniques such as multi-modal data fusion 4.3 Sleep Disturbance [9]. Moreover, we will enhance our training data using clinical data through our domain knowledge of activities of daily living. In both houses, we observe that during the early hours of the day, The paper as it stands establishes baseline activity, sleep and co- 0-6 am, the participants have a higher activity levels, while the con- localisation dependence that will be used after data collection com- trol/carers have a more steady, and at most times, lower activity lev- pleted. That we see signals here is promising for our future data els. This might be an indication of the patients having a more uneasy collection, analysis and research. sleep. As a number of studies suggest [25, 15], people have MCI or dementia will suffer from different levels of sleep disturbance. And a community-based study [16] indicates that nearly one-third of their Acknowledgements subjects were reported as having been inappropriately active at night. This research was funded by CUBOID (UK MRC Momentum grant As can be seen from Fig. 2b, the line representing the activity MC/PC/16029) and the SPHERE IRC (grant EP/K031910/1). levels in bedroom 2 of the participant with dementia is quite unstable REFERENCES in 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 302–307, (2018). [20] Michal Kozlowski, Raul Santos-Rodriguez, and Robert J. 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