Convolutional Neural Network for Elderly Wandering Prediction in

Indoor Scenarios

Rafael F. C. Oliveira

, Fabio Barreto

and Raphael Abreu

Centro Universit

ario Unilasalle do Rio de Janeiro, Niter

oi, Brazil

Keywords:

Machine Learning, Data Generation, Wandering, Alzheimer, Internet of Things.

Abstract:

This work proposes a way to detect wandering activity of Alzheimer’s patients from path data collected from

non-intrusive indoor sensors around the house. Due to the lack of adequate data, we’ve manually generated a

dataset of 220 paths using our own developed application. Wandering patterns in the literature are normally

identiﬁed by visual features (such as loops or random movement), thus our dataset was transformed into

images and augmented. Convolutional layers were used on the neural network model since they tend to have

good results ﬁnding patterns specially on images. The Convolutional Neural Network model was trained with

the generated data and achieved an f1 score (relation between precision and recall) of 75%, recall of 60%, and

precision of 100% on our 10 sample validation slice.

1 INTRODUCTION

Alzheimer is a type of dementia, characterized as

a neurodegenerative disease that affects especially

memory and behaviour. Wandering is one of the

most common behavioural problems of Alzheimer’s

patients as seen in (Teri et al., 1988).

Alzheimer’s patients should be monitored at home

to prevent accidents and moderate changes in the pa-

tient’s behaviour, such as agitation, aggression and

wandering behaviour (Ballard et al., 2009). This task

is usually done by family members or contracted pro-

fessionals. In particular, the family caregiver is more

susceptible to several pathologies due to the impact

that this task has on the deprivation of time and its

activities (Radziszewski et al., 2017).

In the current context, the COVID-19 pandemic

has increased even more the necessity to moni-

tor Alzheimer patients, because they eventually suf-

fer from anxiety and even increased health risks

(Boutoleau-Bretonni

ere et al., 2020). Which in turn,

contributes to the caregiver burden.

Since wandering behaviour is most times coupled

with agitation in Alzheimer patients (Ballard et al.,

2009), a system that automatically and correctly de-

tects wandering behaviour in a smart-home can take

https://orcid.org/0000-0002-0346-3073

https://orcid.org/0000-0003-4842-5238

https://orcid.org/0000-0001-5917-6113

actions to alleviate the agitation of the elderly, or trig-

ger events to inform the caregiver that the elderly is

wandering. There are a variety of interventions to

reduce agitation in elderly people with Alzheimer.

Some of them can be automated inside a smart-home

scenario. Such as aroma, music and lights (O’Neil

et al., 2011). Thus, by utilizing a smart-home to per-

form such tasks, a system can alleviate some of the

caregiver burden.

Therefore a prerequisite to such a system is to cor-

rectly detect a wandering behaviour. This work pro-

poses a machine learning technique for identifying

wandering patterns. The focus of the paper being the

detection of wandering in an indoor scenario.

By observing the difﬁculties faced by caregivers in

monitoring Alzheimer patients, the necessity of con-

tinuous supervision was noticed and it highlights the

importance of this work. By using the Internet Of

Things (IoT) (Karimi and Atkinson, 2013), we could

obtain the movement data of the elderly in a non-

intrusive way, however, due to the massive quantity

of data, it’s hard to identify wandering patterns.

In Figure 1 we have 6 different patterns of move-

ments between point A and a point B. It was identiﬁed

that some of these patterns were more present in wan-

dering than normal activity (Martino-Saltzman et al.,

1991), (Delaunay and Gu

erin, 2017). In Figure 1 (a)

the ﬁrst pattern is present in both wandering and non-

wandering movements, but mostly in non-wandering

movement. So it was not associated as a wandering

Oliveira, R., Barreto, F. and Abreu, R.

Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios.

DOI: 10.5220/0010379902530260

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 253-260

ISBN: 978-989-758-490-9

253

activity, differently from the other two patterns of this

ﬁgure, pacing, and random. Figure 1 (b) shows the

most likely patterns of wandering movements called

lapping, where the movement turns on a loop around

a point.

Due to the lack of data of normal and wander-

ing movement in the same scenario, we’ve reproduced

manually the likely wandering patterns represented in

Figure 1 to create the anomalous activity data and also

normal data mostly composed of direct movements.

(a) Direct, pacing and random.

(b) Lapping.

Figure 1: Wandering and non-wandering movement pat-

terns (Delaunay and Gu

erin, 2017).

Thus, in this work is proposed a method that can

based on the estimated recent position with IoT and a

neural network model for the deep learning approach,

infer the patient stress so that we can act in trying to

mitigate the negative effects of it. It is hoped that this

can be an important tool to help caregivers and rela-

tives of Alzheimer’s patients.

Even though this work reaches especially the ma-

chine learning part of the project, to understand it is

crucial to have an overview of the entire system’s ar-

chitecture. The system’s architecture represented in

Figure 2 is divided into seven components, each one is

part of the ﬂow from the client to data capturing. The

Client is the interface of access and system’s conﬁg-

uration, REST API (Masse, 2011) is a double way to

communicate with other components. With that, the

client can request to the API, data from the machine

learning engine and NoSQL Database (Han et al.,

2011). Temporal data from the database is analyzed

and used to create a predictive model that can infer

stress activities in Machine Learning Engine, which

is the one that was approached in more depth by this

work. Treatment of the sensors data, data fusion be-

tween sensors, and intermediate system communica-

tions are the roles of the controller. The controller

treats the data, by analyzing the raw simple sensors

activations around the house and, with data aggre-

gation, generates the path of the elderly that will be

used to identify anomalies. Raw and treated data are

MQTT

Broker

Actuators Sensors

Control

DatabaseClient API Rest

Machine Learning Engine

Figure 2: Architecture of the system.

stored in the Database. The data is captured through

non-intrusive sensors such as distance, presence, pres-

sure sensors. It is the sensor part of the IoT module,

in which direct communication protocol between de-

vices is done with the Message Queuing Telemetry

Transport(MQTT) interface (Light, 2017). Also in

the IoT module, we have the actuators which are re-

sponsible to help to mitigate the elderly stress, being

able to produce aromas, and turn on and off the lights.

The work shown in this paper is based on this entire

architecture. With this, our work presents a method

to utilize machine learning to detect wandering using

data coming only from non-intrusive sensors.

This work is organized as follows, the related

work of IoT monitoring and the use of machine learn-

ing will be shown in Section 2. Section 3 overviews

the application developed for the data generation and

the composition of the data used in our learning struc-

ture. Section 4 approaches the data pre-processing

and the construction of our neural network model. In

Section 5 we discuss the performance of our model

and experiments. Finally, in Section 6 we show fu-

ture applications and conclude our work.

2 RELATED WORK

As stated in (Ord

nez and Roggen, 2016), human ac-

tivity recognition (HAR) is based on the assumption

that speciﬁc body movements translate into charac-

teristic sensor signal patterns, which can be sensed

and classiﬁed using machine learning techniques. The

use of machine learning was already present in activ-

ity tracking, an example is (Akbar et al., 2019) that

uses data from wearable sensors such as a wristband

for heart rate monitoring and neural networks to learn

sleep patterns of the users, in order to turn off the

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254

lights after they fall asleep. And as well in (Oniga

and S

o, 2014), IoT data is used to train an artiﬁcial

neural network model that, given the information of

the arm and body pose of the patient, it could track

activities such as the walking movement or even pre-

dict if the user was sitting down.

(Khan et al., 2018) surveys and compare the liter-

ature about agitation and aggression detection. The

tracking modalities presented was by using wear-

able devices, computer vision and multimodal, it also

presents a correlation between agitation and aggres-

sion in dementia patients and actigraphy. Relating this

overview of papers to our work, we focus on the well-

being of the patient by assuming that the ambient only

employs sensor-tracking. That the elderly do not need

to wear, neither feel the hostility of a camera watching

him.

In (Vogado et al., 2020) CNN (Convolutional

neural network) is used to help the diagnostic of

leukemia, using images of blood smears, comple-

mented with the application of transfer learning and

data augmentation techniques, reaching a result of

98.28% of accuracy. Compared to our work, we use

CNNs that can detect anomalies in images and also

the use of data augmentation to generate more data.

In (Amin et al., 2020a) and (Amin et al., 2020b),

machine learning is used to detect brain tumors.

(Amin et al., 2020a) uses CNNs and (Amin et al.,

2020b) autoencoders to approach the problem. They

were trained and evaluated using BRATS challenges

databases and had good results in comparison to the

literature. In relation to our work, we similarly have

an image classiﬁcation problem. However, in the

work of (Amin et al., 2020a), the CNN has 14 lay-

ers whereas our networks have only 4 layers.

(Doughty et al., 1998) proposes a system to mon-

itor patients with dementia using non-intrusive sen-

sors to track their activities. The way used to detect

wandering was by counting event activations such as

sitting down, moving to the chair, opening and clos-

ing drawers, and then calculating a score based on re-

peated events 5 minutes. The advantage of the Ma-

chine Learning approach, proposed in our work, is the

easy adaptability to the patient. Our method is based

not only on repetitions but on any data pattern in the

movement. With the advantage of adapting to new

patterns if needed.

Contrasting to related work, our method focus on

predicting wandering using sensing information col-

lected in a non-intrusive way. Similar to (Doughty

et al., 1998), but coupled with a machine learning ap-

proach which makes possible the detection of known

patterns in the literature such as (Martino-Saltzman

et al., 1991). Also, being able to detect other patterns

by the data distribution with the machine learning en-

gine and having a lightweight architecture, capable of

running on an IoT device.

3 DATASET

Due to the lack of indoor datasets containing normal

and anomalous activity, the data generation strategy

was adopted similar to the one presented in (Arifoglu

and Bouchachia, 2017). Therefore, an application to

simulate these paths was developed. With our tool,

the user can manually insert, annotate and visualize

movement data point-by-point, without the need of a

real patient. With this tool, we generated movement

patterns of wandering and normal activity.

In order to make easier the work of data gener-

ation, we’ve created a web application, as shown in

Figure 3. In the application, we can upload a ﬂoor

plan image and set the points in the ground ﬂoor plan.

Every point represents a sensor reading indicating the

position of the patient. In this application we deﬁned

the interval of analysis of every path created to be

hourly, since a path is a group of points with this de-

limiter, we can classify the entire hour path as wan-

dering or normal activity. Is important to note that

although the generated data of the datasets were syn-

thetic and don’t represent the real world, they were

generated to simulate the real-world scenario. Since

the architecture of the neural network is simple and

robust, it is expected that it generalizes in the real

world with real data in a similar manner as the work

of (Arifoglu and Bouchachia, 2017) does.

As seen in Figure 3, on the left we have the ground

ﬂoor plan and on the right the statistics created to help

to monitor the data balance, such as the number of

hours with wandering and normal activity. The visual

part of the application was developed using Vue.js,

Vue Material, and bootstrap libraries. The ground

ﬂoor plan and the points mechanism P5.js library was

used, which simpliﬁes the JavaScript canvas. The ap-

plication is available at https://github.com/Unilasalle-

SmartCare/smartcare-frontend.

The created data using our application was ex-

ported in JavaScript Object Notation (JSON) (Sever-

ance, 2012) format. The ﬁle structure is based on an

array of objects, where each object represents a lo-

cation point in the environment, each point consists

of four attributes. ’x’ and ’y’ represents it’s respec-

tive coordinates in the ground ﬂoor plan image start-

ing from the upper left. The ’date’ attribute, which

date and hour the point belongs. Each point has the

attribute stress that indicates if the elderly is wander-

ing. Even though each point has the attribute stress,

Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios

255

Figure 3: Developed application for data generation. The user interacts with the ﬂoor-plant to place movement points, deﬁne

the hour of the movement and if it was wandering. On the right side of the page shows the statistics created to visualize the

generated data. In the upper right a menu that shows all the registered movement dates and hour.

the application considers a time interval to account for

a wandering activity. It is not able to have two points

in the same time interval with different stress values.

In order to know where the movement starts and ends,

the points of the same data and interval are inserted in

the order as the elderly movement.

With the application, we developed two datasets.

The ﬁrst is a train/test dataset and the second is a val-

idation dataset. The generated train/test dataset con-

sists of a total of 200 hours, being 100 hours of nor-

mal activity and 100 hours of stress. As seen in table

1, they are sliced into training and test part being re-

spectively 75% and 25%. The validation dataset to be

used in the evaluation of our machine learning model

is consisted of 20 hours, being 50% anomalous activ-

ity and the other 50% normal activity. The composi-

tion can be seen in more depth in table 2.

For the purpose of generating consistent anoma-

lous data, patterns present in (Martino-Saltzman et al.,

1991), (Delaunay and Gu

erin, 2017) were reproduced

manually using our developed data generation ap-

plication. We assumed normal movement as being

not anomalous or random. The patterns classiﬁed as

anomalous are Lapping and Pacing, seen in (Martino-

Saltzman et al., 1991). Random patterns are described

by (Martino-Saltzman et al., 1991) as roundabout or

haphazard travel to many locations within an area

without repetition and is mostly composed of direct

movements.

Table 1: Distribution of the train/test dataset.

Movement Type # Samples

Lapping 59

Random 11

Pacing 30

Normal 100

Table 2: Distribution of the validation dataset.

Movement Type # Samples

Lapping 7

Random 2

Pacing 1

Normal 10

4 IMPLEMENTATION

Data Processing. With the generated dataset, it’s

still needed to do some processing in order to use our

artiﬁcial neural network model (ANN). First, all the

points were grouped by their date and hour. Then, the

points were converted to grayscale images. To create

such images, each point is converted and placed in the

corresponding location on the image and a straight

line is plotted between two adjacent points. As seen

in Figure 4, the resulting image shows a stroke where

the patient moved in each hour.

Each pixel of an image consists of a value between

0 and 255. To make the values smaller and faster for

our model, we normalized the pixels to be between

0 and 1, maintaining the distribution and ratio of the

data. Since the images have the resolution of the

ground ﬂoor plan, it would be a waste of resources

to give to the model big images with very small

features and it would impair the generalization of

the model. To account for the aforementioned prob-

lems, the images were rescaled to 128x128 resolution.

Data Augmentation. Even with the generated dataset

we do not have much data. In order to prevent overﬁt-

ting and help the model generalization, we used data

augmentation techniques. As seen in Figure 5, each

train image generates more training images.

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Figure 4: Samples of the path data plotted as images after rescaling.

Original Image

Augmentated Images

Figure 5: Data augmentation technique applied in movement path.

Differently from (Vogado et al., 2020) where

zoom and shearing were applied to the new images,

we only rotated each image to a maximum interval of

3 degrees and randomly ﬂipped vertically and hori-

zontally.

Artiﬁcial Neural Network Model. By using path

images, it was seen that the Convolutional Neural

Network (CNN) approach could be an interesting

idea. Since it could identify the anomalous movement

activities by learning the shapes and pixel distribution

of the paths. The CNN architecture is presented in

Figure 6. In the Figure, the processed image is given

as input and goes into a convolutional layer (conv

)

with a ﬁlter size of 32, ﬁltering is used to highlight

features of the input. Max pooling is applied to re-

duce the dimensionality and make the neural network

training faster, and then the output is ﬂattened into an

array. After that, we have three fully connected lay-

ers (FC

,FC

, and FC

). Being the last one the out-

put of our network between 0 and 1, which represents

whether the input path is predicted as wandering or

not. To avoid overﬁtting, we randomly dropped 25%

of the hidden units between the fully connected layers

(FC

and FC

) (Srivastava et al., 2014).

For the prediction, it is used the backpropagation

algorithm that on training, a process where the net-

work is fed with training data to learn. The algo-

rithm constantly passes on each layer and by calcu-

lating predictions and measuring errors, it adjusts the

the weights of the neurons to reduce errors (Gron,

2017). From the convolutional to the fully connected

layers, we opted to use Rectiﬁer activation function

(ReLU) in all of them since it reduces the probabil-

ity of vanishing gradient problem (Talathi and Vartak,

2015). The loss function was binary cross-entropy

that is good with binary classiﬁcation problems.

The architecture was implemented using Keras

(Chollet et al., 2018) with TensorFlow (Abadi et al.,

2015) as backend. To train the network, we used

150 epochs and trained in mini batches of 64 samples

Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios

257

Figure 6: Architecture of our neural network model.

with a learning rate of 0.001. The model and dataset

will be available at https://github.com/Unilasalle-

SmartCare/smartcare-machinelearning.

5 RESULTS

The accuracy of the model, which represents the

model performance on the train/test data, is presented

Figure 7. The loss, that is the distance between the

true target and the prediction, is presented in Fig-

ure 8. The model was trained for 100 epochs with

the train/test dataset. The optimizer algorithm used

was the Adaptive Moment Estimation (adam).

The tuning of the model hyperparameters based

on the train/test set and evaluating on the same set

would leak information of the validation set on the

model that may inﬂuence the results (Chollet, 2017).

Based on that, besides the test set used in the training

to calculate the model accuracy and loss seen in Fig-

ures 7 and 8, we’ve generated separately a validation

dataset, consisted of 10 hours of stress and 10 hours

of normal activity, an equivalent of 10% the size of

the data used for training with the objective to evalu-

ate our model and avoid information leaks.

To measure accuracy of the models, we utilized

the following evaluation metrics: precision, recall and

f1 score. In Equations 1,2 and 3 we present each one.

Precision =

truepositives

truepositives + f alsepositives

(1)

Recall =

truepositives

truepositives + f alsenegatives

(2)

F1 = 2 ×

precision × recall

precision + recall

(3)

With the validation dataset, we could evaluate our

ﬁnal model and had a precision of 100% followed by

a recall of 60% and the f1 score of 75%. The hyperpa-

rameters were chosen empirically by adjusting based

on the two curves.

The trained model was able to identify all the

movement patterns presented in Table 2. Figure 9

shows a sample of random path generated from the

validation dataset that was identiﬁed by our model

correctly as wandering.

The model obtained a satisfactory result consider-

ing the small amount of data. Besides that, the pro-

posed architecture is lightweight and can be embed-

ded into small scale devices. Therefore it is expected

that the machine learning approach proposed in this

work can predict not only patterns as the ones present

in the literature (Martino-Saltzman et al., 1991), (De-

launay and Gu

erin, 2017), (Lin et al., 2012) but also

non-perceived ones. As raised by (Lin et al., 2014) it

is important to perform a personalized user wander-

ing recognition model, since those wandering activi-

ties might have different characteristics depending on

the person and even environmental conditions. Our

method paves the way for a reinforcement learning

approach that can be used for the network to adapt

itself to the speciﬁc elderly movement in that house.

Figure 7: Accuracy per epoch of the model.

Figure 8: Learning curve representation of the model.

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258

Figure 9: Sample of a random path generated with wander-

ing activity.

6 CONCLUSIONS

In this paper, data preparation and lightweight CNN

architecture were proposed to detect the wandering

of Alzheimer’s patients using data from non-intrusive

sensors. By transforming all the data paths into im-

ages, we could use techniques such as data augmen-

tation to expand the simulated dataset and reach bet-

ter results with a convolutional neural network. Con-

ventional tracking such as wearables and cameras en-

ters into a deep discussion about the privacy of the

users. Therefore, one of the challenges of this work

was to propose a predictive method that could ﬁt the

environment with non-intrusive sensors. Another big

challenge was the lack of data to develop the machine

learning model, so we’ve generated using known pat-

terns to simulate the real world, although the data be-

ing synthetic, it is expected that the machine learning

architecture also works with real-world data.

For future work, collecting real patient’s data is

vital to validate our model in the real world and also

learn even more wandering patterns. The conversion

from the path to images as done in this work, can be

expanded to other disease movement anomalies and

also outside the health ﬁeld. For example, the same

method can be used from classifying potential cus-

tomers of a store based on the movement. This comes

with as much needed privacy concern, as the only

input our method needs is a path of movement in-

side an indoor scenario. In the future, this idea can

also be applied to identify wandering in an outdoor

scenario. The use of reinforcement learning is a fu-

ture challenge to bring interesting comparative results

with this work. Since reinforcement learning tends to

choose actions to maximize a reward based on the en-

vironment information (Chollet, 2017), these actions

could be something to mitigate the patient stress such

as aromatherapy or music.

ACKNOWLEDGEMENTS

The authors would like to thank Unilasalle-RJ for en-

couraging and ﬁnancially supporting this work.

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