This paper presents a technique of restricted knee gait analysis and classification based on electrostatic sensing, which can be applied in home nursing of patients with exercise rehabilitation. The technique is used to monitor the electrostatic signals generated during the movement of the human body, extract the gait features under restricted knee conditions, and classify the limited angles of the knee joint to evaluate the condition of rehabilitation of the knee in family environment. In this paper, the electrostatic mechanism of biped movementis analyzed first, which is the establishment of the human gait electrostatic signal detection equation. In the research of gait frequency domain information, a gait electrostatic signal acquisition system is set up. And we design the gait simulation experiment of knee joint disease patients and obtain the gait electrostatic signal with the knee joint restricted in normal, limited and semi confinement condition, with which a small gait electrostatic signal database was established. The frequency domain information of signals is obtained by the fast Fourier transform. By using principal component analysis (PCA), the dimension of frequency domain information is reduced and characteristic parameters are got. We select k nearest neighbor algorithm for classification and recognition and get a high recognition rate. Results show that the frequency domain information of the human gait electrostatic signal can reflect the activity of the knee joint. This may provide a theoretical basis for the treatment of diseases of the knee rehabilitation. This paper is helpful to establish a new technique to evaluate the rehabilitation degree of knee joint disease. It has the advantages of small size, easy to use and suitable for family promotion. It has a good application prospect in family wisdom medical field.
Journal: TechConnect Briefs
Volume: 3, Biotech, Biomaterials and Biomedical: TechConnect Briefs 2017
Published: May 14, 2017
Pages: 250 - 253
Industry sector: Sensors, MEMS, Electronics
Topic: Sensors - Chemical, Physical & Bio