Patient History Tracking using Local Binary Pattern Histogram(LBPH) Algorithm
D. Muruga Radha Devi, P. Leela Jancy, P. R. Tamilselvi, V Aishwarya · 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) · 2022
Currently hospitals maintain the patient details either manually or digitally. History of patients like disease caused, treatment undergone earlier, current status and details of each and every review are maintained for future reference. But in emergencies retrieving information of patients leads to waste of time and even this searching process delays remedial treatment and causes uncertain death to patients. Since in health care time is very critical, identifying patients and tracking patient details in emergencies can be done using efficient mechanisms. Even though existing systems use plenty of security identification solutions like patient-id, biometric methods like fingerprint and two factor authentication none of the method is as strong as facial recognition. This may also reduce the medical errors while identifying patients even if the patient is in unconscious state and to track the information and to reduce the time taken at each counter. Thus by integrating health care with Technology lots of solutions are given since technology can ease the heavy workload, the way the disease can be diagnosed and treated. Our proposed method will provide an efficient technique for patient identification and tracking the patient's history. Though the proposed method is based on a math pattern, it will keep the data safe and sound. The system will correctly identify if a person is allowed in certain floors or other restricted areas, or if they move out of their pre-defined territory. This method is suitable for nursing homes or Supporting Living Facilities to keep the patients in safe condition. By adding cameras along with facial recognition algorithms in patient rooms used to verify who was it would be easy to identify who were in the room before recording patient health information on video screens. Thus the proposed method uses facial recognition to track the patient history even if they are in unconscious state by using Local Binary Pattern Histogram (LBPH) algorithm. This algorithm describes original image in an enriched form by enhancing facial features obtained from intermediate images.. Thus by using the proposed method the time taken to identify patients and tracking their records can be reduced and so it can be used to revolutionize the way the patients are treated in the hospitals.