Activity recognition from inertial sensors on a smartphone - Chee, Kok Hao (2015)
In healthcare sector, getting to track the activity patterns of an individual is vital when providing assistance in healthcare. In this project, unobtrusive finding of a person’s activities is implemented through a smart phone. Decision of using android-based smart phone is chosen to monitor and derive recognition over the activities performed. There are certain issues when it first comes in raw signal, but it can be rectified via either artifact rejection which is known as filtering or by calibration. Smart phones nowadays have calibrated at most efficient way, but only up to 80-90% in terms of accuracy. An algorithm is developed to assist the activity recognition process. The effective method was to use the accelerometer sensor and gyroscope sensor’s reading to compute necessary threshold required to be categorized as activities recognizable which is walking, running, climbing staircase or in a vehicle. Test cases were conducted to retrieve results for three categories of activities namely walking, running, climbing staircase in single step and double step. These test cases are conducted over 3 individuals and average results are recorded. Evaluation results of the testing shows that the climbing staircase has the best accuracy among others.