This paper explores the possibility of using low-level activity spotting for daily routine
recognition. Using occurrence statistics of low-level activities and simple classifiers based …

High-level and longer-term activity recognition has great potentials in areas such as medical
diagnosis and human behavior modeling. So far however, activity recognition research has …

Activity recognition has received increasing attention from the machine learning community.
Of particular interest is the ability to recognize activities in real time from streaming data, but …

We investigate how incremental learning of long-term human activity patterns improves the
accuracy of activity classification over time. Rather than trying to improve the classification …

Activity recognition has gained a lot of interest in recent years due to its potential and
usefulness for context-aware computing. Most approaches for activity recognition focus on …

In the past decades, activity recognition had aroused great interest for the community of
context-awareness computing and human behaviours monitoring. However, most of the …

Activities of daily living are good indicators of the health status of elderly. Therefore,
automating the monitoring of these activities is a crucial step in future care giving. However …

Many intelligent systems that focus on the needs of a human require information about the
activities that are being performed by the human. At the core of this capability is activity …

Personalized activity recognition usually has the problem of highly biased activity patterns
among different tasks/persons. Traditional methods face problems on dealing with those …

Activity recognition approaches have shown to enable good performance for a wide variety
of applications. Most approaches rely on machine learning techniques requiring significant …