International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7792 IDENTIFICATION AND ANALYSIS OF FOOT ULCERATION USING LOAD CELL TECHNIQUE Ms. D .Sudarvizhi 1 , M. Nivetha 2 , P. Priyadharshini 3 , J.R. Swetha 4 1 Professor, Dept of electronics and communication, KPR institute of engineering and technology, Tamilnadu, India 2 3 4 Students, Dept of electronics and communication, KPR institute of engineering and technology, Tamilnadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Diabetes Mellitus is one of the most serious non communicable diseases. It may cause severe health issue such as amputation. This paper presents the foot pressure analysis using load cell sensors which are inserted inside the mat. The accuracy and precision can be achieved by employing machine learning technique and applying support vector machine classifier of more than 94.6% and 95.2% respectively. Many other techniques include a thin sheet pressure pad, to capture the plantar pressure of the foot. Additionally, the 3D trajectories of the center of pressure (COP) can be obtained with the average recognition rate of 94%. Foot pressure analysis is a helpful tool to detect the abnormal high pressure point area and predict future risk of ulceration. On comparing all these techniques, the results which are produced using load cell will be more effective. Key Words: Keywords: COP, Plantar surface, SCU, MCU, machine learning, risk factors. 1. INTRODUCTION A load cell is a transducer that measures force, and outputs this force as an electrical signal. By using load cell we can identify the location of foot ulcer in diabetic patients. The long term sequel leads to diabetic foot ulcer that may include motor neuropathy to clawing of toes and metatarsal heads. Mainly motor neuropathy involves the enter pathogenic factor results in high foot pressure. This neuropathy may cause the deformity and decreased joint mobility. Depending on this high foot pressures in the metatarsal head and loss of toe function mainly great toe. Onsorimotor neuropathy is responsible for anhydrosis and denervation of foot. Due to this it may lead to atrophic skin, callous formation and fissure. This make the increased blood stagnation and swelling in foot predisposes. Due to high pressure, tissue breakdown occur and growth of ulceration takes place. Mainly the infection is due to both the peripheral vascular disease and neuropathy. Most people are affected by amputation. Jeremy rich says that in forefoot the pressure is at peak and in rare foot it is not to that peak. The prediction may lead to about 36 months. The increased level of glucose in the blood is hyperglycemia which has many serious complications that leads to an alteration in the distribution of the plantar pressure. These complications lead to the ulceration and causes amputation. This proposed method includes the prevention of DFU and also helps in the identification of the risk factors and predictive factors of DFU. The high pressure or moderate pressure foot regions can be identified with the help of plantar pressure measurements. The elevation in plantar pressure or pain would not be felt by the people who are having sensory neuropathy or loss of protective sensation. In order to classify diabetic patients into normal diabetic type 2 and diabetic with neuropathy, many automated methods using static plantar pressure measurement such as neural network, Classifies discrete wavelet transform and principal component analysis .With the help of wireless foot pressure insoles, dynamic pressure managements are noted. FWHM, maximum gradient and minimum gradient, peak plantar pressure, pressure time integral features are introduced. Support vector machine helps in the classification between different groups accurately by using machine learning and data mining techniques. This data can help to prevent from chronic foot pain and foot injury. The characteristic of foot differs from person to person due to a range of factors such as variations in walking speed, body weight, age, etc. The plantar surface is divided into eight anatomical regions as shown in fig 4. It has been reported that during normal stance, each foot carries about half of the body weight at the heel, forefoot and big toe whereas lowest plantar load is located under the midmost foot[15]. In this