Frequency analysis feedback systems and methods
A health status modulator analyzes frequencies emitted by a person to select and implement improvement frequencies at the person. The health status modulator detects frequency information generated at the person, for example a voice sample or a vibrational frequency, and determines which significant frequencies exist within that sample. The modulator could then seek to modify the person's state my implementing alternative frequencies that reinforce detected ideal frequencies, introduce missing ideal frequencies, or counter and eliminate negative frequencies.
1. A method for improving a health status of a person comprising:
extrapolating a first set of significant frequencies from a first set of bio-acoustic information comprising sonic information embedded within the person's voice;
deriving a first set of correlations based on the first set of significant frequencies;
using at least a portion of the derived first set of correlations to develop a first protocol that implements a first frequency at a corresponding first duration;
implementing at least a portion of the first protocol at the person's body;
comparing the first set of significant frequencies against a library of frequencies having a plurality of frequencies that are related to at least one of emotion, health status, and physiology;
tagging each of the plurality of frequencies in the library of frequencies as a positive or negative significant frequency, weighing the tagged frequencies in the library of frequencies in accordance with an algorithm; and
identifying the first frequency in the first set of significant frequencies that is the heaviest positively weighed in the library of frequencies.
2. The method of claim 1 , further comprising:
extrapolating a second set of significant frequencies from a second set of bio-acoustic information comprising sonic information embedded within the person's voice after implementing the portion of the first protocol at the person,
deriving a second set of correlations in the second set of frequency information;
using at least a portion of the derived second set of correlations to develop a second protocol that implements a second frequency at a corresponding second duration; and
implementing at least a portion of the second protocol at the person's body.
3. The method of claim 2 , wherein the step of using the portion of the derived second set of correlations to develop the second protocol comprises selecting the second frequency as a function of a difference between the second set of significant frequencies and the first set of significant frequencies.
4. The method of claim 2 , wherein the second frequency comprises an alternative frequency to the first frequency, when the first frequency is not detected in subsequent frequencies collected from the person within a threshold period of time.
5. The method of claim 1 , further comprising receiving the first set of significant frequencies from a cellular phone.
6. The method of claim 1 , further comprising receiving the first set of significant frequencies from a wearable device.
7. The method of claim 1 , wherein the step of deriving the first set of correlations comprises deriving correlations within a single wavelet.
8. The method of claim 1 , wherein the step of deriving the first set of correlations comprises deriving correlations between wavelets.
9. The method of claim 1 , further comprising receiving a first set of health data about the person, wherein deriving the first set of correlations comprises deriving correlations between the first set of significant frequencies and the first set of health data.
10. The method of claim 9 , wherein the first frequency comprises at least one of the first set of the significant frequencies.
11. The method of claim 9 , wherein the first frequency comprises a harmonic of at least one of the first set of the significant frequencies.
12. The method of claim 1 , wherein implementing at least a portion of the first protocol at the person's body comprises reinforcing an existing significant positive frequency.
13. The method of claim 1 , wherein implementing at least a portion of the first protocol at the person's body comprises introducing a missing significant positive frequency.
14. The method of claim 1 , wherein implementing at least a portion of the first protocol at the person's body comprises canceling an existing negative frequency.
15. The method of claim 1 , wherein extrapolating the first set of significant frequencies comprises emitting frequencies at the person and detecting frequency feedback from the person's body.
16. A method for improving a health status of a person comprising:
extrapolating a first set of significant frequencies from a first set of bio-acoustic information comprising sonic information embedded within the person's voice;
deriving a first set of correlations based on the first set of significant frequencies;
using at least a portion of the derived first set of correlations to develop a first protocol that implements a first frequency at a corresponding first duration;
implementing at least a portion of the first protocol at the person's body;
wherein the step of extrapolating the first set of significant frequencies from the first set of bio-acoustic information comprises identifying frequencies that appear more than five times in at least 80% of a contiguous portion of the bio-acoustic information.
17. The method of claim 16 , further comprising:
extrapolating a second set of significant frequencies from a second set of bio-acoustic information comprising sonic information embedded within the person's voice after implementing the portion of the first protocol at the person,
deriving a second set of correlations in the second set of frequency information;
using at least a portion of the derived second set of correlations to develop a second protocol that implements a second frequency at a corresponding second duration; and
implementing at least a portion of the second protocol at the person's body.
18. The method of claim 16 , wherein the step of deriving the first set of correlations comprises deriving correlations within a single wavelet.
19. The method of claim 16 , wherein the step of deriving the first set of correlations comprises deriving correlations between wavelets.
20. The method of claim 16 , further comprising receiving a first set of health data about the person, wherein deriving the first set of correlations comprises deriving correlations between the first set of significant frequencies and the first set of health data.