A Granular Computing Classifier for Human Activity with Smartphones

Human-Inspired Granular Computing

In two recent papers, J.T. Yao (2007, 2008) presents a ten-year review of granular computing by analyzing published papers collected from the ISI’s Web of Science and the IEEE Digital Library. The objective is to “study the current status, the trends and the future direction of granular computing and identify prolific authors, impact authors, and the most impact papers in the past decade.” The ...

A Public Domain Dataset for Human Activity Recognition using Smartphones

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an A...

A competitive approach for human activity recognition on smartphones

This paper describes a competitive approach developed for an activity recognition challenge. The competition was defined on a new and publicly available dataset of human activities, recorded with smartphone sensors. This work investigates different feature sets for the activity recognition task of the competition. Moreover, the focus is also on the introduction of a new, confidence-based boosti...

Transition-Aware Human Activity Recognition Using Smartphones

This work presents the Transition-Aware Human Activity Recognition (TAHAR) system architecture for the recognition of physical activities using smartphones. It targets real-time classification with a collection of inertial sensors while addressing issues regarding the occurrence of transitions between activities and unknown activities to the learning algorithm. We propose two implementations of...

Human Activity Detection using Smartphones and Maps