Ambulatory Monitoring of Physical Activity Based on Knee Flexion/Extension Measured by Inductive Sensor Technology

We developed a knee brace to measure the knee angle and implicitly the flexion/extension (f/e) of the knee joint during daily activities. The goal of this study is to classify and validate a limited set of physical activities on ten young healthy subjects based on knee f/e. Physical activities included in this study are walking, ascending and descending of stairs, and fast locomotion (such as jogging, running, and sprinting) at self-selected speeds. The knee brace includes 2 accelerometers for static measurements and calibration and an inductive sensor for dynamic measurements. As we focus on physical activities, the inductive sensor will provide the required information on knee f/e. In this study, the subjects traversed a predefined track which consisted of indoor paths, outdoor paths, and obstacles. The activity classification algorithm based on peak detection in the knee f/e angle resulted in a detection rate of 95.9% for walking, 90.3% for ascending stairs, 78.3% for descending stairs, and 82.2% for fast locomotion. We conclude that we developed a measurement device which allows long-term and ambulatory monitoring. Furthermore, it is possible to predict the aforementioned activities with an acceptable performance. 1. Introduction Physical activity is healthy as it prevents or reduces chronic health issues like obesity, diabetes, heart diseases, and depression. Therefore, physical activity as well as encouraging physical activity is gaining importance in the therapy and prevention of these diseases. The last decades witnessed a lot of engineering and research efforts is put into developing small, cheap, and accurate wearable sensing devices. Today, these wearable sensors have reached the point that they can be used for clinical application [1, 2]. In gait analysis wearable sensors are used to measure motion and they are worn at different parts of the subject’s body. Commonly used sensors are accelerometers, gyroscopes, force sensors, strain gauges, inclinometers, and electrogoniometers [3]. The signals measured by these wearable sensors can be used to monitor movement and perform gait analysis. However, before actual gait analysis can be performed the raw sensor signals must be processed. Mathematical and statistical methods such as curve sketching [4, 5], fuzzy logic algorithms [6–8], neural networks [9–11], and support vector machines [12, 13] are often used to extract features and classify them into certain gait phases or activities [3]. Next to the sensors itself, a lot of wearable devices for measuring physical activity are already available,