Activity Recognition Model Based on GPS Data, Points of Interest and User Profile

2017 ◽  
pp. 358-367 ◽  
Author(s):  
Igor da Penha Natal ◽  
Rogerio de Avellar Campos Cordeiro ◽  
Ana Cristina Bicharra Garcia
Keyword(s):  
Activity Recognition ◽  
User Profile ◽  
Gps Data ◽  
Recognition Model ◽  
Model Based ◽  
Points Of Interest ◽  
Data Points

scholarly journals An Approach towards Position-Independent Human Activity Recognition Model based on Wearable Accelerometer Sensor

2020 ◽  
Vol 177 ◽  
pp. 196-203
Author(s):  
Naima Qamar ◽  
Nasir Siddiqui ◽  
Muhammad Ehatisham-ul-Haq ◽  
Muhammad Awais Azam ◽  
Usman Naeem
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Accelerometer Sensor ◽  
Recognition Model ◽  
Model Based

scholarly journals Human Behavior Recognition Model Based on Feature and Classifier Selection

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7791
Author(s):  
Ge Gao ◽  
Zhixin Li ◽  
Zhan Huan ◽  
Ying Chen ◽  
Jiuzhen Liang ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensor ◽  
Rapid Development ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Support Vector ◽  
Recognition Model ◽  
Model Based ◽  
Classifier Selection

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

Adaptive Fuzzy Clustering Model Based on Internal Connectivity of All Data Points

2010 ◽  
Vol 36 (11) ◽  
pp. 1544-1556 ◽  
Author(s):  
Cheng-Long TANG ◽  
Shi-Gang WANG
Keyword(s):  
Fuzzy Clustering ◽  
Adaptive Fuzzy ◽  
Model Based ◽  
Clustering Model ◽  
Data Points
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