TELKOMNIKA Telecommunication Computing Electronics and Control
Real-time human activity recognition from smart phone using linear support vector machines

TELKOMNIKA Telecommunication Computing Electronics and Control
Real-time human activity recognition from smart phone using linear support vector machines

Apache Kafka
HAR
Linear support vector machine
Machine learning
Real-time
Support vector machines

The recognition of human activity (HAR) the use of cell devices embedded
in its exten sively disbursed sensors affords guidance, instructions, and take
care of citizens of smart cities. Consequently, it became essential to analyze
human every day sports. To examine statistical models of human conduct,
synthetic intelligence strategies such as machine studying can be used. Many
studies have not studied type overall performance in real-time due to
statistics series. To remedy this trouble, this paper proposes a structure
primarily based on open supply technology and platforms consisting of
Apache Kafka, for messages to flow over the internet, method them and
provide shape for existing facts in real-time and formulates the trouble of
identifying human pastime by using a smartphone tool as a type hassle using
statistics collection by telephone sensors. The proposed version is skilled by
some machine learning algorithms. The algorithm that has proven superior
and quality results helps a linear vector machines.

Kamel Maaloul, Lejdel Brahim, Nedioui Med Abdelhamid

http://telkomnika.uad.ac.id

Oct 26, 2022

peri irawan

pdf

english

text