Smart Homes have come a long way: From research laboratories in the early days, through
(almost) neglect, to their recent revival in real-world environments enabled through the …

With the rise of artificial intelligence, sensor-based human activity recognition (S-HAR) is
increasingly being employed in healthcare monitoring for the elderly, fitness tracking, and …

The behavior of a time series may be affected by various factors. Changes in mean,
variance, frequency, and auto-correlation are the most common. Change-Point Detection …

With the growing interest in smart home environments and in providing seamless
interactions with various smart devices, robust and reliable human activity recognition (HAR) …

Manufacturing process monitoring is crucial to ensure production quality. This paper
formulates the detection problem of abnormal changes in the manufacturing process as the …

The fixed sliding window is the commonly used technique for signal segmentation in human
activity recognition (HAR). However, the fixed sliding window may not produce optimal …

Forecasting time series data is a big challenge due to the temporal and multivariate
dependencies in the data. In this paper, we present a new approach named as TPPM25 (T …

Extracting valuable activity segments from continuously received sensor data is a key step
for many downstream applications such as activity recognition, trajectory prediction, and …

Activity prediction aims to predict what activities will occur in the future. In smart homes, to
facilitate the daily living of the residents, automated or assistive services are provided. To …

Behavior modeling has multiple applications in the intelligent environment domain. It has
been used in different tasks, such as the stratification of different pathologies, prediction of …