3 research outputs found
B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows
Human Activity Recognition (HAR), based on machine and deep learning
algorithms is considered one of the most promising technologies to monitor
professional and daily life activities for different categories of people
(e.g., athletes, elderly, kids, employers) in order to provide a variety of
services related, for example to well-being, empowering of technical
performances, prevention of risky situation, and educational purposes. However,
the analysis of the effectiveness and the efficiency of HAR methodologies
suffers from the lack of a standard workflow, which might represent the
baseline for the estimation of the quality of the developed pattern recognition
models. This makes the comparison among different approaches a challenging
task. In addition, researchers can make mistakes that, when not detected,
definitely affect the achieved results. To mitigate such issues, this paper
proposes an open-source automatic and highly configurable framework, named
B-HAR, for the definition, standardization, and development of a baseline
framework in order to evaluate and compare HAR methodologies. It implements the
most popular data processing methods for data preparation and the most commonly
used machine and deep learning pattern recognition models.Comment: 9 Pages, 3 Figures, 3 Tables, Link to B-HAR Library:
https://github.com/B-HAR-HumanActivityRecognition/B-HA
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey
In the last decade, Human Activity Recognition (HAR) has become a vibrant
research area, especially due to the spread of electronic devices such as
smartphones, smartwatches and video cameras present in our daily lives. In
addition, the advance of deep learning and other machine learning algorithms
has allowed researchers to use HAR in various domains including sports, health
and well-being applications. For example, HAR is considered as one of the most
promising assistive technology tools to support elderly's daily life by
monitoring their cognitive and physical function through daily activities. This
survey focuses on critical role of machine learning in developing HAR
applications based on inertial sensors in conjunction with physiological and
environmental sensors.Comment: Accepted for Publication in IEEE Access DOI:
10.1109/ACCESS.2020.303771