  Human physical motion activity identification has many potential applications
in various fields, such as medical diagnosis, military sensing, sports
analysis, and human-computer security interaction. With the recent advances in
smartphones and wearable technologies, it has become common for such devices to
have embedded motion sensors that are able to sense even small body movements.
This study collected human activity data from 60 participants across two
different days for a total of six activities recorded by gyroscope and
accelerometer sensors in a modern smartphone. The paper investigates to what
extent different activities can be identified by utilising machine learning
algorithms using approaches such as majority algorithmic voting. More analyses
are also provided that reveal which time and frequency domain based features
were best able to identify individuals motion activity types. Overall, the
proposed approach achieved a classification accuracy of 98 percent in
identifying four different activities: walking, walking upstairs, walking
downstairs, and sitting while the subject is calm and doing a typical
desk-based activity.
