Background. Physical activity classification is an objective approach to assess levels of physical activity, and indicates an individual's degree of functional ability. It is significant for a number of the disciplines, such as behavioural sciences, physiotherapy, etc. Accelerometry is found to be a practical and low cost method for activity classification that could provide an objective and efficient measurement of people's daily activities.

Methods. This paper utilises a mobile phone with a built-in tri-axial accelerometer sensor to automatically classify normal physical activities. A rule-based activity classification model, which can recognise 4 common daily activities (lying, walking, sitting, and standing) and 6 transitions between postural orientations, is introduced here. In this model, three types of statuses (walking/ transition, lying, and sitting/standing) are first classified based on the kinetic energy and upright angle. Transitions are then separated from walking and assigned to the corresponding type using upright angle algorithm. To evaluate the performance of this developed application, a trial is designed with 8 healthy adult subjects, who are required to perform a 6-minute activity routine with an iPhone fixed at the waist position.

Results. Based on the evaluation result, our application measures the length of time of each activity accurately and the achieved sensitivity of each activity classification exceeds 90% while the achieved specificity exceeds 96%. Meanwhile, regarding the transition identification, the sensitivities are high in stand-to-sit (80%) and low in sit-to-stand (56%).