{"title":"获取和识别个人的Vata, Pitta和Kapha","authors":"S. G C, S. T. Veerabhadrappa, Abhishek Gosh","doi":"10.1109/CCIP57447.2022.10058666","DOIUrl":null,"url":null,"abstract":"With the ever-increasing number of diseases in today's world, there is a need for a system to provide early diagnosis and the root cause of human health. Indian and Chinese traditional medicine system provides natural and simple solutions to detecting health issues. Nadi-Nidan is an ancient medical technique, traced back to ancient Indian traditional health monitoring, known to indicate all the health features of a human body. In Nadi Nidan, Wrist pulses or arterial pulses are sensed to diagnose the health status. The study was carried out to design a non-invasive system for wrist pulse analysis that gives us the heartbeat, IBI (Inter-Beat-Interference) and the body type, to support doctors in routine diagnostic procedures and provide detailed procedure for obtaining the complete set of the Nadi signals as a time series. An Ayurveda practitioners and physicians can use this prototype for pulse reading and uniformate in analysis. The proposed model specifically deals with data acquisition of three Nadi signals Vata, Pitta and Kapha. Signals are obtained by using PPG sensors. Arduino is used as the data acquisition hardware. Identification of Prakruthi of the subject was carried out based on the amplitude of Vata, Pitta and Kapha signal acquired at the wrist and achieved 83% accurarcy. Vata, Pitta and Kapha of diabetic and normal subject were analyzed.","PeriodicalId":309964,"journal":{"name":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Acquisition and Identification of Vata, Pitta and Kapha of an individual\",\"authors\":\"S. G C, S. T. Veerabhadrappa, Abhishek Gosh\",\"doi\":\"10.1109/CCIP57447.2022.10058666\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the ever-increasing number of diseases in today's world, there is a need for a system to provide early diagnosis and the root cause of human health. Indian and Chinese traditional medicine system provides natural and simple solutions to detecting health issues. Nadi-Nidan is an ancient medical technique, traced back to ancient Indian traditional health monitoring, known to indicate all the health features of a human body. In Nadi Nidan, Wrist pulses or arterial pulses are sensed to diagnose the health status. The study was carried out to design a non-invasive system for wrist pulse analysis that gives us the heartbeat, IBI (Inter-Beat-Interference) and the body type, to support doctors in routine diagnostic procedures and provide detailed procedure for obtaining the complete set of the Nadi signals as a time series. An Ayurveda practitioners and physicians can use this prototype for pulse reading and uniformate in analysis. The proposed model specifically deals with data acquisition of three Nadi signals Vata, Pitta and Kapha. Signals are obtained by using PPG sensors. Arduino is used as the data acquisition hardware. Identification of Prakruthi of the subject was carried out based on the amplitude of Vata, Pitta and Kapha signal acquired at the wrist and achieved 83% accurarcy. Vata, Pitta and Kapha of diabetic and normal subject were analyzed.\",\"PeriodicalId\":309964,\"journal\":{\"name\":\"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCIP57447.2022.10058666\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIP57447.2022.10058666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Acquisition and Identification of Vata, Pitta and Kapha of an individual
With the ever-increasing number of diseases in today's world, there is a need for a system to provide early diagnosis and the root cause of human health. Indian and Chinese traditional medicine system provides natural and simple solutions to detecting health issues. Nadi-Nidan is an ancient medical technique, traced back to ancient Indian traditional health monitoring, known to indicate all the health features of a human body. In Nadi Nidan, Wrist pulses or arterial pulses are sensed to diagnose the health status. The study was carried out to design a non-invasive system for wrist pulse analysis that gives us the heartbeat, IBI (Inter-Beat-Interference) and the body type, to support doctors in routine diagnostic procedures and provide detailed procedure for obtaining the complete set of the Nadi signals as a time series. An Ayurveda practitioners and physicians can use this prototype for pulse reading and uniformate in analysis. The proposed model specifically deals with data acquisition of three Nadi signals Vata, Pitta and Kapha. Signals are obtained by using PPG sensors. Arduino is used as the data acquisition hardware. Identification of Prakruthi of the subject was carried out based on the amplitude of Vata, Pitta and Kapha signal acquired at the wrist and achieved 83% accurarcy. Vata, Pitta and Kapha of diabetic and normal subject were analyzed.