Activity recognition aims to provide accurate and opportune information on people's
activities by leveraging sensory data available in today's sensory rich environments …

Over the past decade, multivariate time series classification has received great attention. We
propose transforming the existing univariate time series classification models, the Long …

Human activity recognition (HAR) is currently recognized as a key element of a more
general framework designed to perform continuous monitoring of human behaviors in the …

With a widespread of various sensors embedded in mobile devices, the analysis of human
daily activities becomes more common and straightforward. This task now arises in a range …

In recent years, human activity recognition has become a hot topic inside the scientific
community. The reason to be under the spotlight is its direct application in multiple domains …

Multivariate Time Series (MTS) classification has gained importance over the past decade
with the increase in the number of temporal datasets in multiple domains. The current state …

Sensor-based human activity recognition is an essential task for automatic behavior analysis
for sports player, senior citizens, and IoT applications. The traditional approaches are based …

This study aims to investigate the performance of Machine Learning (ML) techniques used in
Human Activity Recognition (HAR). Techniques considered are Naïve Bayes, Support …

The emergence of IoT and AI has brought revolutionary change in various application
domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to …

Time series data mining deals with extracting useful and meaningful information from time
series data. Recently, the increasing use of temporal data, in particular time series data, has …