Footprint-Based Health Monitoring Database using Raspberry PI
C. Lavanya1, S. Christy2

1C. Lavanya, UG Scholar, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Thandalam, Chennai, India.
2S. Christy, Associate Professor, Faculty of Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, India.
Manuscript received on February 02, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on March 30, 2020. | PP: 851-854 | Volume-8 Issue-6, March 2020. | Retrieval Number: E6857018520/2020©BEIESP | DOI: 10.35940/ijrte.E6857.038620

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Abstract: The health monitoring of the person can be done in the different ways. The health of the patient can be determined by the image processing technique. The biometric parameters can gather the details of the health condition of the patient. The digital image processing can be applied in the various filed such as medical, geology, research etc., In this paper they proposes the foot print technology this can capture the foot print of the patient by using the web cam. The captured image can be analyzed by using the shape and the dimension analysis. The foot print can reads the each person identity. Based upon the identity and the numbers the image processing system is implemented. It uses the raspberry pi as the main part. The data which is captured by the web cam can be stored in the SD card. The data allocation is done in the memory path. The classification of the data is takes place by using the data separation algorithm. The color analysis can takes place a significant place based upon the color we can able to classify the foot print and makes it for further analysis. There are several steps can be took place the image acquisition, edge detection, feature extraction, pattern recognition, pattern matching. The matched image can be provided as the better result. Based upon the result the health condition can be predicted. This method is highly effective and accurate when compared to other method.
Keywords: Pattern Recognition, Pre processing, Signal to Noise Ratio, Pixel Matching.
Scope of the Article: IoT Application and Communication Protocol.