Citation: Ali, A.; Samara, W.;
Alhaddad, D.; Ware, A.; Saraereh,
O.A. Human Activity and Motion
Pattern Recognition within Indoor
Environment Using Convolutional
Neural Networks Clustering and
Naive Bayes Classification
Algorithms. Sensors 2022, 22, 1016.
https://doi.org/10.3390/s22031016
Academic Editor: Raffaele Gravina
Received: 25 December 2021
Accepted: 21 January 2022
Published: 28 January 2022
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Article
Human Activity and Motion Pattern Recognition within Indoor
Environment Using Convolutional Neural Networks Clustering
and Naive Bayes Classification Algorithms
Ashraf Ali
1,
* , Weam Samara
2
, Doaa Alhaddad
2
, Andrew Ware
3
and Omar A. Saraereh
1
1
Department of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan;
eloas2@hu.edu.jo
2
Estarta Co., Ltd., Amman 11942, Jordan; weaamsamara@gmail.com (W.S.);
doaa.alhaddad97@gmail.com (D.A.)
3
Faculty of Computing, Engineering and Sciences, University of South Wales, Pontypridd CF37 1DL, UK;
andrew.ware@southwales.ac.uk
* Correspondence: ashraf@hu.edu.jo
Abstract:
Human Activity Recognition (HAR) systems are designed to read sensor data and analyse
it to classify any detected movement and respond accordingly. However, there is a need for more
responsive and near real-time systems to distinguish between false and true alarms. To accurately
determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain
period and used to train the system to recognise features associated with their movements. This
training process is followed by a testing cycle that uses actual data of different patterns of activity
that are either similar or different to the training data set. This paper evaluates the use of a combined
Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly
identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern
recognition can be achieved using either of the two approaches, even when a partial motion pattern
is derived as a subset out of a full-motion path.
Keywords:
human activity recognition; CNN; sensors; machine learning; motion pattern; Naive
Bayes
1. Introduction
Modern smart intruder alarm and Human Activity Recognition (HAR) systems consist
of networks of integrated electronic devices and sensors connected to a centralised control
unit to protect against intruders by distinguishing between legitimate and illegitimate
activity. In contrast to conventional security systems that respond to a single sensor trigger,
the intelligent system uses machine learning techniques and IoT (Internet of Things) sensor
infrastructure to detect and classify complex motion patterns. Different types of sensors
(such as the Passive Infrared Sensor PIR, motion sensor and the ultrasonic sensor) are often
used to detect different movement patterns. Similarly, Human Activity Recognition (HAR)
uses the same sensors and rely on deep data analysis and machine learning algorithms
to classify the movement based on the application needed, this concept can by deployed
in many practical applications, such as early diagnosis of human ageing symptoms with
dementia, and can also be used to define anomalies in employee transitions or motions
within an indoor environment, as well as as part of burglary alarm systems that detect
anomalies in human behaviour.
Regardless of the precise definition of the application, sensors collect data of activity
within a predefined environment, time, and space. Activity learning techniques use this
data to identify normal, routine motion in terms of forecasted activities. Then, when an
abnormal movement is detected, the system will trigger an alarm, notification, or reaction
Sensors 2022, 22, 1016. https://doi.org/10.3390/s22031016 https://www.mdpi.com/journal/sensors
