Frame-based analysis of knee extension exercises using a smartphone accelerometer

Daily activities, such as walking or climbing stairs can cause difficulties for people who are diagnosed with knee arthritis. It is an ailment, which causes absence from work and a reduction in life quality for patients who suffer from the condition. To prevent the worsening consequences, health professionals prescribe exercises to restore knee mobility and strength. Nowadays, mass-marketed smartphones incorporate diverse and powerful sensors that create exciting new opportunities that allow application developers to identify and analyze movement. This research developed a method to analyze a knee extension exercise using an accelerometer. The important factors affecting the effectiveness of the exercise to relieve knee pain were recognized. Regression analysis was applied to obtain a suitable equation to compute the knee angle from accelerometer data. Then, by applying signal processing, all knee angles were divided into small frames and used to identify the characteristics of the knee extension exercises. The results showed that the proposed method could efficiently determine: the degree the leg held at, the length of time the leg held for, and the angular velocity the leg lowered and raised at.