Knee joints play an indispensable role in the activities of daily living. In particular, the knee joints of the elderly and the physically challenged require continuous care in order to ensure a healthy daily life. This study proposes a health monitoring system for knee joints, which is able to classify lower extremity movements using the angle and acceleration components of these joints. The proposed monitoring system consists of a wearable frame placed on the knee joint, consisting of a sensor part for monitoring the knee joint angle and acceleration and a wireless communication part for transferring bio signals to a smart device. Knee joint angles and accelerations are measured using potentiometers installed at the hinges of the upper and lower parts of the wearable frame and an inertial sensor (IMU) attached to the thigh. Data thus measured are transferred via Bluetooth to an application on a smart device. The proposed system incorporates a classification algorithm for lower extremity movements, which can distinguish users’ actions such as sitting, lying, and standing by using real-time measurements of knee joint angles and accelerations. This study shows that the proposed monitoring system detects postures that negatively affect knee joints and informs a user when these postures are adopted, thereby helping to maintain healthy knee joints.
Smart Device Monitoring System Based on Multi-type Inertial Sensor Machine Learning
