scholarly journals A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 38670-38687 ◽  
Author(s):  
Ali Chelli ◽  
Matthias Patzold
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Daily Living ◽  
Fall Detection ◽  
Learning Approach ◽  
Daily Living Activity ◽  
Machine Learning Approach

scholarly journals Fall Detection and Daily Living Activity Recognition using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 328-332
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Angular Velocity ◽  
Activity Recognition ◽  
Daily Living ◽  
Fall Detection ◽  
Classification Algorithm ◽  
Support Vector ◽  
Velocity Data ◽  
Daily Living Activity

Elder people are increasing all over the world as a result certain fall occur in their daily life. This fall lead to several severe problems. The fall may often causes injuries and in many cases it result in death of the individual. The problem should be addressed to reduce the fall. By using some Machine Learning(ML) algorithm the fall and daily living activities are recognized. The acceleration and angular velocity data obtained from the dataset are used to detect the fall and daily living activity. Body movement of the person are collected and stored in the dataset. Acceleration and angular velocity data are used to extract the time and frequency domain feature and provide them to classification algorithm. Here, Logistic regression algorithm is used for detecting the fall and living activity. It is very effective algorithm and does not require too many computational resources. It is easy to regularize and provide well calibrated predicted probabilities as output. The sensitivity, accuracy and specificity of fall detection and activity recognition is obtained as a result. The performance evaluation is made with three classification algorithm. The three classification algorithm are Artificial neural network (ANN), K-nearest neighbours (KNN), Quadratic support vector machine (QSVM). Logistic regression provides highest accuracy compared with other three algorithm.

Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset

2021 ◽  
Vol 191 ◽  
pp. 367-372
Author(s):  
Ariza-Colpas Paola ◽  
Oñate-Bowen Alvaro Agustín ◽  
Suarez-Brieva Eydy del Carmen ◽  
Oviedo-Carrascal Ana ◽  
Urina Triana Miguel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Sensors ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 20 ◽  
Author(s):  
I Putra ◽  
James Brusey ◽  
Elena Gaura ◽  
Rein Vesilo
Keyword(s):  
Machine Learning ◽  
Fall Detection ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Event Triggered
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