Inspiration system related symptom sensing system, apparatus, and method

1 . An inspiration system related symptom sensing system, comprising:

a patch, configured to detect a vibration generated by an inspiration system corresponding to an organism, comprising:

at least one vibrator, configured to generate a reference vibration; and

a plurality of receivers; and

a processor, communicatively connected to the patch;

wherein the inspiration system related symptom sensing system is configured to execute the following operations:

detecting, by the receivers, the reference vibration corresponding to the at least one vibrator to generate a plurality of first vibration signals;

detecting, by the receivers, the vibration generated by the inspiration system corresponding to the organism to generate a plurality of second vibration signals;

determining, by the processor, a relative position of each of the receivers related to the inspiration system of the organism based on the first vibration signals, the second vibration signals, and a plurality of positional relationships of the at least one vibrator corresponding to the receivers;

extracting, by the processor, abnormal signals from the second vibration signals;

generating, by the processor, a spatial position corresponding to the inspiration system of the organism and a symptom type corresponding to the spatial position by using a classification model based on the abnormal signals of the second vibration signals and the relative positions corresponding to the receivers, and

making a remote diagnosis to determine a physiological state in the organism based on the spatial position corresponding to the inspiration system of the organism and the symptom type corresponding to the spatial position.

2 . The inspiration system related symptom sensing system of claim 1 , wherein the inspiration system related symptom sensing system is further configured to:

generating, by the processor, a lung sound signal corresponding to the inspiration system based on each of the second vibration signals; and

generating, by the processor, the spatial position in the inspiration system of the organism and the symptom type corresponding to the spatial position based on the lung sound signals and the relative positions corresponding to the receivers.

3 . The inspiration system related symptom sensing system of claim 2 , wherein the operation of the processor generating the lung sound signal further comprises:

filtering, by the processor, a noise and a heart sound from the second vibration signals.

4 . The inspiration system related symptom sensing system of claim 1 , wherein the operation of the processor extracting the abnormal signal further comprises:

transforming, by the processor, each of the second vibration signals into a spectrum signal based on a frequency domain; and

extracting, by the processor, the abnormal signal based on the spectrum signals.

5 . The inspiration system related symptom sensing system of claim 1 , wherein the classification model is configured to calculate the symptom type corresponding to the second vibration signals and a possibility of the organism having the symptom type.

6 . The inspiration system related symptom sensing system of claim 1 , wherein the classification model is generated through the following operations:

training a machine learning model based on a plurality of history abnormal signals and a symptom diagnostic result corresponding to each of the history abnormal signals; and

taking the trained machine learning model as the classification model.

7 . The inspiration system related symptom sensing system of claim 1 , wherein the inspiration system related symptom sensing system is further configured to:

generating, by the processor, a concentration diagnostic result based on the second vibration signals by using a concentration model.

8 . The inspiration system related symptom sensing system of claim 7 , wherein the concentration model is generated through the following operations:

training a machine learning model based on a plurality of history vibration signal sets and a history concentration diagnostic result corresponding to each of the history vibration signal sets; and

taking the trained machine learning model as the concentration model.

9 . The inspiration system related symptom sensing system of claim 1 , wherein the processor calculates the relative position of each of the receivers relative to the inspiration system of the organism and the spatial position in the inspiration system of the organism by using a Time of Flight method and a triangulation method.

10 . The inspiration system related symptom sensing system of claim 1 , wherein the inspiration system related symptom sensing system is further configured to:

establishing, by the processor, a three-dimensional model based on the relative positions, wherein the three-dimensional model comprises a plurality of positional relationships between the patch and a plurality of organs in the inspiration system in a three-dimensional space.

11 . An inspiration system related symptom sensing apparatus, comprising:

a transceiver interface, communicatively connected to a patch, wherein the patch comprises at least one vibrator and a plurality of receivers; and

a processor, electrically connected to the transceiver interface;

wherein the inspiration system related symptom sensing apparatus is configured to execute the following operations:

receiving, by the transceiver interface, a plurality of first vibration signals corresponding to the at least one vibrator and a plurality of second vibration signals corresponding to an inspiration system corresponding to an organism from the patch;

determining, by the processor, a relative position of each of the receivers related to the inspiration system of the organism based on the first vibration signals, the second vibration signals, and a plurality of positional relationships of the at least one vibrator of the patch corresponding to each of the receivers of the patch;

extracting, by the processor, abnormal signals from the second vibration signals;

generating, by the processor, a spatial position corresponding to the inspiration system of the organism and a symptom type corresponding to the spatial position by using a classification model based on the abnormal signals of the second vibration signals and the relative positions corresponding to the receivers; and

making a remote diagnosis to determine a physiological state in the organism based on the spatial position corresponding to the inspiration system of the organism and the symptom type corresponding to the spatial position.

12 . An inspiration system related symptom sensing method, being adapted for use in an inspiration system related symptom sensing system, wherein the inspiration system related symptom sensing system comprises a processor and a patch, the patch comprises at least one vibrator and a plurality of receivers, and the inspiration system related symptom sensing method comprises:

detecting, by the receivers, a reference vibration corresponding to the at least one vibrator to generate a plurality first vibration signals;

detecting, by the receivers, the vibration generated by the inspiration system corresponding to an organism to generate a plurality of second vibration signals;

determining, by the processor, a relative position of each of the receivers related to the inspiration system of the organism based on the first vibration signals, the second vibration signals, and a plurality of positional relationships of the at least one vibrator corresponding to each of the receivers;

extracting, by the processor, abnormal signals from the second vibration signals;

generating, by the processor, a spatial position corresponding to the inspiration system of the organism and a symptom type corresponding to the spatial position by using a classification model based on the abnormal signals of the second vibration signals and the relative positions corresponding to the receivers; and

making a remote diagnosis to determine a physiological state in the organism based on the spatial position corresponding to the inspiration system of the organism and the symptom type corresponding to the spatial position.

13 . The inspiration system related symptom sensing method of claim 12 , further comprising:

generating, by the processor, a lung sound signal corresponding to the inspiration system based on the second vibration signals; and

generating, by the processor, the spatial position in the inspiration system of the organism and the symptom type corresponding to the spatial position based on the lung sound signals and the relative positions corresponding to the receivers.

14 . The inspiration system related symptom sensing method of claim 13 , wherein the step of the processor generating the lung sound signal further comprises:

filtering, by the processor, a noise and a heart sound from the second vibration signals.

15 . The inspiration system related symptom sensing method of claim 12 , wherein the step of the processor extracting the abnormal signal further comprises:

transforming, by the processor, each of the second vibration signals into a spectrum signal based on a frequency domain; and

extracting, by the processor, the abnormal signal based on the spectrum signals.

16 . The inspiration system related symptom sensing method of claim 12 , wherein the classification model is configured to calculate the symptom type corresponding to the second vibration signals and a possibility of the organism having the symptom type.

17 . The inspiration system related symptom sensing method of claim 12 , wherein the classification model is generated through the following steps:

training a machine learning model based on a plurality of history abnormal signals and a symptom diagnostic result corresponding to each of the history abnormal signals; and

taking the trained machine learning model as the classification model.

18 . The inspiration system related symptom sensing method of claim 12 , further comprising:

generating, by the processor, a concentration diagnostic result based on the second vibration signals by using a concentration model.

19 . The inspiration system related symptom sensing method of claim 18 , wherein the concentration model is generated through the following steps:

training a machine learning model based on a plurality of history vibration signal sets and a history concentration diagnostic result corresponding to each of the history vibration signal sets; and

taking the trained machine learning model as the concentration model.

20 . The inspiration system related symptom sensing method of claim 12 , wherein the relative position of each of the receivers relative to the inspiration system of the organism and the spatial position in the inspiration system of the organism are calculated by using a Time of Flight method and a triangulation method.