Biological feedback system and operation method thereof
A biofeedback and physiological technology, applied in the field of biofeedback systems, can solve problems such as blind spots in interpretation, ineffective functions of health aids, and ignorance of measurement process information
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Embodiment 1
[0064] Embodiment 1 takes heart rhythm variation analysis as an example to illustrate how the present invention utilizes process information to solve errors caused by ignoring time sequence in traditional static data and 2D images. Table 4 below shows the RRI samples of a first user A and a second user B. The two sets of sample values are exactly the same, only the order is different.
[0065] Table 4: Samples of physiological signals between heartbeats of the first user A and the second user B
[0066] A
725
748
743
779
795
788
805
822
821
830
815
842
860
882
856
B
779
795
815
743
860
805
748
882
725
830
822
856
788
842
821
[0067] Please refer to Fig. 2 (a) and Fig. 2 (b) simultaneously, Fig. 2 (a) is the schematic diagram of traditional heartbeat interval scatter diagram; Fig. 2 (b) is the heartbeat interval scatter diagram after using the present invention to ...
Embodiment 2
[0073] This embodiment 2 takes the heart rhythm variation analysis as an example to illustrate how the present invention applies biofeedback, stream-guided execution module 5 with a display device as a training aid.
[0074] Please refer to image 3 , image 3 It is a schematic diagram of the present invention applied to yoga teaching and autonomic nerve detection. Such as image 3 As shown, the coach C, the first learner L1 and the second learner L2 all wear attached or wearable sensing devices, that is, the detection module 1 .
[0075] The detection module 1 can collect in real time the original signal streams generated by the current coach C, the first learner L1 and the second learner L2 due to yoga exercises and changes in their autonomic nerve activity, and transmit the original signals to the database 2 for storage. , and parse them together in parsing module 3. The analyzed raw signal will be converted into a physiological signal and sent to the analysis control m...
Embodiment 3
[0081] Embodiment 3 takes brainwave detection as an example to illustrate how the present invention uses long-term monitoring and original signal recording as a diagnostic tool.
[0082] This embodiment is for users with chronic insomnia. The detection module 1 is a wearable device, which can monitor the brainwave signal of the user for a long time when worn on the user, and the database 2 can record the brainwave signal. The execution module 5 can simultaneously display the dynamic brain wave D in FIG. 4(a) and the dynamic power spectrum density graph AS in FIG.
[0083] When a person is in different sleep states, the power spectrum density map reflected by the brain wave will show different appearances. As shown in Figure 4(b), when the user is in the non-sleep state S1, the light sleep state S2, and the deep sleep state S3, the area of the frequency region corresponding to the α, β, γ, θ and δ waves in the brain wave Significantly different. By observing the dynamic po...
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