Human Activities Recognition in Android
Smartphone Using WSVM-HMM Classifier
M’hamed Bilal Abidine
(&) and Belkacem Fergani
Laboratoire d’Ingénierie des Systèmes Intelligents et Communicants,
LISIC Lab., Electronics and Computer Sciences Department,
University of Science and Technology Houari Boumediene (USTHB),
Algiers, Algeria
abidineb@hotmail.com
Abstract. Being able to recognize human activities is essential for several
applications such as health monitoring, fall detection, context-aware mobile
applications. In this work, we perform the recognition of the human activity
based on the combined Weighted SVM and HMM by taking advantage of the
relative strengths of these two classification paradigms. One significant advantage in WSVMs is that, they deal the problem of imbalanced data but his
drawback is that, they are inherently static classifiers - they do not implicitly
model temporal evolution of data. HMMs have the advantage of being able to
handle dynamic data with certain assumptions about stationary and independence. The experiment results on real datasets show that the proposed method
possess the better robustness and distinction.
Keywords: Activity recognition Á Classification Á Weighted SVM Á HMM
1 Introduction
The advancement of technologies has facilitated the monitoring of human activities
through the embedded sensors in a smartphone. Recently, smart phones, equipped with
a rich set of sensors, are explored as alternative platforms for human activity recognition (HAR) [1, 2]. HAR technology aims at recognizing the behavior and activities of
users through a series of observations, which has wide application [3, 4] in different
areas, such as healthcare and military monitoring.
With smartphones becoming an integral part of daily human life [5], they are being
preferred as the most usable appliances that could recognize human activities due to its
powerful in terms of mobility, user-friendly interface, network capability, strong CPU,
memory, and battery. They contain a large number of hardware sensors such as
accelerometer, gyroscope, temperature, humidity, light sensor, and GPS receiver.
The human sensor based activity recognition is a combination of sensor networks
hand-in-hand with the data mining and machine learning techniques [6]. The smartphones provide enormous amount of sensor data for one to understand the daily activity
patterns of an individual.
The basic procedure for mobile activity recognition involves i) collection of
labelled data, i.e., associated with a specific class or activity from users that perform
sample activities to be recognized ii) classification model generation by using collected
© The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 386–394, 2020.
https://doi.org/10.1007/978-3-030-51517-1_35
Smartphone Using WSVM-HMM Classifier
M’hamed Bilal Abidine
(&) and Belkacem Fergani
Laboratoire d’Ingénierie des Systèmes Intelligents et Communicants,
LISIC Lab., Electronics and Computer Sciences Department,
University of Science and Technology Houari Boumediene (USTHB),
Algiers, Algeria
abidineb@hotmail.com
Abstract. Being able to recognize human activities is essential for several
applications such as health monitoring, fall detection, context-aware mobile
applications. In this work, we perform the recognition of the human activity
based on the combined Weighted SVM and HMM by taking advantage of the
relative strengths of these two classification paradigms. One significant advantage in WSVMs is that, they deal the problem of imbalanced data but his
drawback is that, they are inherently static classifiers - they do not implicitly
model temporal evolution of data. HMMs have the advantage of being able to
handle dynamic data with certain assumptions about stationary and independence. The experiment results on real datasets show that the proposed method
possess the better robustness and distinction.
Keywords: Activity recognition Á Classification Á Weighted SVM Á HMM
1 Introduction
The advancement of technologies has facilitated the monitoring of human activities
through the embedded sensors in a smartphone. Recently, smart phones, equipped with
a rich set of sensors, are explored as alternative platforms for human activity recognition (HAR) [1, 2]. HAR technology aims at recognizing the behavior and activities of
users through a series of observations, which has wide application [3, 4] in different
areas, such as healthcare and military monitoring.
With smartphones becoming an integral part of daily human life [5], they are being
preferred as the most usable appliances that could recognize human activities due to its
powerful in terms of mobility, user-friendly interface, network capability, strong CPU,
memory, and battery. They contain a large number of hardware sensors such as
accelerometer, gyroscope, temperature, humidity, light sensor, and GPS receiver.
The human sensor based activity recognition is a combination of sensor networks
hand-in-hand with the data mining and machine learning techniques [6]. The smartphones provide enormous amount of sensor data for one to understand the daily activity
patterns of an individual.
The basic procedure for mobile activity recognition involves i) collection of
labelled data, i.e., associated with a specific class or activity from users that perform
sample activities to be recognized ii) classification model generation by using collected
© The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 386–394, 2020.
https://doi.org/10.1007/978-3-030-51517-1_35