dc.description.abstract |
Smart devices like smartphones, smartwatches have made this world smarter than any other
time at every scale. A lot of facilities can be taken from these devices. Proper use of built-in sensors such as accelerometer, gyroscope, GPS is a few of them. In everyday life, people
do a lot of physical activities which can be important for analysis like health state
prediction, how much exercise they do etc. by using those sensors based on Artificial
Intelligence. In this paper we have implemented both machine learning and deep learning
to detect and recognize eight activities with a maximum of 99.3% accuracy. Of those activities
few are similar in physical movements and actions like sitting in a chair at home, standing, and sitting in a car. These are almost similar and difficult to distinguish. Going upstairs and
downstairs are also almost similar to separate. So we showed that with more sensors and data collection points a wide range of activities can be recognized and the
accuracies can be increased. We proved our point by comparing the results of using
fewer sensors and again using data of only one position of either pocket or wrist. Then
finally we showed that by putting all the sensors and data of pocket, wrist together, we can
recognize those activities accurately and in this way, a wide range of activities can be
recognized with precision. |
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