Analysis and Prediction of the Effect of Surya Namaskar on Pulse of Different Prakruti Using Machine Learning
01 Jan 2018-pp 547-556
TL;DR: The changes that Surya Namaskar causes in the Pulse are studied and used to predict Pulse a fter performing Suryanamaskar, and a framework to predicted Pulse after SuryA Namasksar is proposed.
Abstract: “Surya Namaskar” is the key for Good health! Today’s social life can be made easier and healthier using the mantra of “YOGA”. Nadi Parikshan is a diagnostic technique which is based on the ancient Ayurvedic principles of Wrist Pulse analysis. Nadi describes the mental and physical health of a person in great depth. This information can be used by practitioners to prevent, detect as well as treat any ailment. Surya Namaskar is a Yoga exercise which has multiple health benefits and a direct impact on Pulse. Prakruti of a person is a metaphysical characteristic and a combination of the three doshas in Ayurveda viz. Vatta, Pitta, and Kapha which remains constant for the lifetime. Experimentation was carried out to analyze the effect of Surya Namaskar exercise on the Pulse of different Prakruti. The Pulse was recorded for a group of young students aged between 19 and 23 years with different Prakruti before Surya Namaskar and after Surya Namaskar for a period of 4 days. This paper analyzes the effect of Surya Namaskar on human Pulse and proposes a framework to predict Pulse after Surya Namaskar. The changes that Surya Namaskar causes in the Pulse are studied and used to predict Pulse a fter performing Surya Namaskar. This analysis helps understand how Surya Namaskar benefits the health of a person. Performing Surya Namaskar in our daily routine would improve the health of the society as a whole making the subjects energetic and active.
Citations
More filters
01 Jan 2022
TL;DR: In this article , the authors have developed a diagnostic system for the analysis of the effect of gender on the wrist pulse, which consists of various steps namely data acquisition using pulse sensors that have been interfaced with the microcontroller, signal pre-processing using MATLAB and detection of pulses.
Abstract: AbstractThe Ayurvedic philosophy says that everything in our Universe can also be found in the human body which can be examined. For examination, there are eightfold technique; in this paper, the authors have studied Nadi Pariksha or Pulse diagnosis or pulse examination. Nadi Pariksha is the science of observing the pulse for diagnosis of the subconscious and human body. The wrist pulse signal is known to examine the health status of a person. Indian Medical Science uses the wrist pulse to predict the emotional and physical status of an individual. In Pulse Examination, the palpation of adjoining three points of radial artery is sensed with the help of three fingers namely ring, middle and index finger. This palpation gives the sensation of energies (doshas) in terms of Vata, Pitta and Kapha which are accumulatively known as bio-elements (Tridosha). A human being has a network of nerves and sensory organs that perceive the physical world around them. In this paper, the authors have developed a diagnostic system for the analysis of the effect of gender on the wrist pulse. The diagnostic system consists of various steps namely data acquisition using pulse sensors that have been interfaced with the microcontroller, signal pre-processing using MATLAB and detection of pulses. The detection of different pulses leads to the diagnosis of different diseases.KeywordsAyurvedaPulse examinationDoshasSystolicDiastolic
1 citations
References
More filters
TL;DR: A unifying framework is introduced to understand existing approaches to investigate the universal approximation problem using feedforward neural networks, and two training algorithms are introduced which can determine the weights of feedforward Neural Network, with sigmoidal activation neurons, to any degree of prescribed accuracy.
530 citations
TL;DR: This paper presents a wavelet-based cascaded adaptive filter (CAF) to remove the baseline wander of pulse waveform and demonstrates the power of CAF filter both in removing baseline wander and in preserving the diagnostic information of pulseWaveform.
97 citations
22 Oct 2007
TL;DR: The procedure for obtaining the complete spectrum of the nadi pulses as a time series is provided, and the waveforms obtained have been compared with these other similar equipment developed earlier, and is shown to contain more details.
Abstract: Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.
73 citations
28 Aug 2008
TL;DR: A HR prediction model based on the relationship between HR and PA has the potential to be used in various areas, such as: cardiopathy research and diagnosis, heart attack warning indicator, sports capability measure and mental activity evaluation.
Abstract: The technique of combining heart rate (HR) and physical activity (PA) has been adopted in a number of research areas, such as energy expenditure measurement, autonomic nervous system assessment, sports research, etc. However, there have been few studies on the direct relationship between HR and PA. This paper proposes a HR prediction model based on the relationship between HR and PA. The predictor has the potential to be used in various areas, such as: cardiopathy research and diagnosis, heart attack warning indicator, sports capability measure and mental activity evaluation. The method has the following steps: first, the recorded HR and PA signals are preprocessed as two synchronized time sequences: HR(n) and PA(n). The inputs of the predictor are HR(n) and PA(n) in the current time step, and the output is the predicted sequence HR(n + 1) in the next time step. The feed forward neural network (FFNN) was chosen as the mathematical model of the predictor. Experiments was conducted based on the real-life signals from a healthy male. A set of 90 minute signals were collected. One half of the signal set was used to train the FFNN and the other half to validate the training. The mean absolute error of the predicted heart rate was restricted inside 5. The result shows the potential of the proposed method.
39 citations
03 Aug 2014
TL;DR: It is shown how deeper layers can be utilized to model the observed sequence using a sparser set of sinusoid units, and how non-uniform regularization can improve generalization by promoting the shifting of weight toward simpler units.
Abstract: We present a method for training a deep neural network containing sinusoidal activation functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then trained with regularization to improve generalization. A simple dynamic parameter tuning method is employed to adjust both the learning rate and regularization term, such that stability and efficient training are both achieved. We show how deeper layers can be utilized to model the observed sequence using a sparser set of sinusoid units, and how non-uniform regularization can improve generalization by promoting the shifting of weight toward simpler units. The method is demonstrated with time-series problems to show that it leads to effective extrapolation of nonlinear trends.
27 citations