Abstract
In line with the increasing use of sensors and health application, there are huge efforts on processing of collected data to extract valuable information such as accelerometer data. This study will propose activity recognition model aim to detect the activities by employing ensemble of classifiers techniques using the Wireless Sensor Data Mining (WISDM). The model will recognize six activities namely walking, jogging, upstairs, downstairs, sitting, and standing. Many experiments are conducted to determine the best classifier combination for activity recognition. An improvement is observed in the performance when the classifiers are combined than when used individually. An ensemble model is built using AdaBoost in combination with decision tree algorithm C4.5. The model effectively enhances the performance with an accuracy level of 94.04 %.
Highlights
Health applications utilizing the built-in sensors in smartphones or those that are wearable are considered as system to simplify healthcare services such as monitoring
The performance achieved was over 90% most times but the best performance was achieved by combing AdaBoost with C4.5
The new model achieved 94.04% which is the www.ijacsa.thesai.org highest compared with standalone classifiers or other classifiers combination
Summary
Health applications utilizing the built-in sensors in smartphones or those that are wearable are considered as system to simplify healthcare services such as monitoring. It is an efficient and innovative way to deliver healthcare to patients for improving healthcare outcomes and quality of life. There is a huge increase in the use of such technology. There is an increase in the generated data as well. Activity recognition is used for different purposes for a patient such as monitoring of chronic diseases, as well as fitness and wellness [8]
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