The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two...
We have compared the performance of different machine learning techniques for human activity recogni...
Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical acti...
This study examined the feasibility of a non-laboratory approach that uses machine learning on multi...
The evaluation of the effectiveness of different machine learning algorithms on a publicly available...
Effective classification of physical exercises allows individuals to assess their levels of physical...
Human activity recognition (HAR) is vital in a wide range of real-life applications such as health m...
Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT)...
International audienceThe world is getting older by the minute due to rising life expectancy, leadin...
The use of on-body wearable sensors is widespread in several academic and industrial domains. Of gre...
Physical activity plays an important role in controlling obesity and maintaining healthy living. It ...
International audienceHere we propose a new machine learning algorithm for classification of human a...
In recent years, there is a growing interest in Human Activity Recognition (HAR) systems applied in ...
This paper presents an analysis of several machine learning classification models and their effectiv...
This study presents the application of machine learning techniques for physical activity recognition...
The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has g...
We have compared the performance of different machine learning techniques for human activity recogni...
Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical acti...
This study examined the feasibility of a non-laboratory approach that uses machine learning on multi...
The evaluation of the effectiveness of different machine learning algorithms on a publicly available...
Effective classification of physical exercises allows individuals to assess their levels of physical...
Human activity recognition (HAR) is vital in a wide range of real-life applications such as health m...
Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT)...
International audienceThe world is getting older by the minute due to rising life expectancy, leadin...
The use of on-body wearable sensors is widespread in several academic and industrial domains. Of gre...
Physical activity plays an important role in controlling obesity and maintaining healthy living. It ...
International audienceHere we propose a new machine learning algorithm for classification of human a...
In recent years, there is a growing interest in Human Activity Recognition (HAR) systems applied in ...
This paper presents an analysis of several machine learning classification models and their effectiv...
This study presents the application of machine learning techniques for physical activity recognition...
The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has g...
We have compared the performance of different machine learning techniques for human activity recogni...
Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical acti...
This study examined the feasibility of a non-laboratory approach that uses machine learning on multi...