This paper presents an human sensing (HS) system based on Hidden Markov Models
(HMMs) for classifying physical activities: walking, walking-upstairs, walking-downstairs …

This paper describes the development of a Human Activity Recognition and Segmentation
(HARS) system based on Hidden Markov Models (HMMs). This system uses inertial signals …

When developing a fully automatic system for evaluating motor activities performed by a
person, it is necessary to segment and recognize the different activities in order to focus the …

Abstract This paper describes Hidden Markov Model Based (HMM) human activity
recognition system (HAR). 31 participants were used to track various aggressive activities …

Automated recognition of human daily activities from wearable sensor signals has attracted
a great deal of interests in many applications including health care, sports and aged care. In …

Abstract Human Activity Recognition (HAR) is an essential area of research related to the
ability of smartphones to retrieve information through embedded sensors and recognize the …

We are currently using smart phone sensors to detect physical activities. The sensors which
are currently being used are accelerometer, gyroscope, barometer, etc. Recently, smart …

Recognizing human activities and monitoring population behavior are fundamental needs of
our society. Population security, crowd surveillance, healthcare support and living …

Smartphones are among the most popular wearable devices to monitor human activities.
Several existing methods for Human Activity Recognition (HAR) using data from …

Recognizing human activities from temporal streams of sensory data observations is a very
important task on a wide variety of applications in context recognition. Especially for time …