Paper Info
Title
Machine Learning Algorithms For Activity-Intensity Recognition Using Accelerometer Data
Abstract
In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.
Year
DOI
Venue
2021
10.3390/s21041214
SENSORS
Keywords
DocType
Volume
pervasive healthcare monitoring, activity and intensity recognition, mobile computing, machine learning, accelerometers
Journal
21
Issue
ISSN
Citations 
4
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Eduardo Gomes101.69
Luciano Bertini200.34
Wagner Rangel Campos300.34
Ana Paula Sobral400.34
Izabela Mocaiber500.34
Alessandro Copetti600.34