AFFECTIVE WELL-BEING SUPERVISION SYSTEM AND METHOD

• US 20120323087A1
• Filed: 12/21/2009
• Published: 12/20/2012
• Est. Priority Date: 12/21/2009
• Status: Abandoned Application

• Current Assignee: Fundacion Tecnalia Research & Innovation (Fundación Tecnalia Corporación Tecnológica)
• Original Assignee: Fundacion Tecnalia Research & Innovation (Fundación Tecnalia Corporación Tecnológica)
• Inventors: León Villeda, Enrique Edgar, Montalbán Pontesa, Iraitz, Garzo Manzanares, Ainara
• Application Number:
  US13/521,782
• Publication Number:
  US 20120323087A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16

Abstract

System and method capable of reducing the effects of negative emotional states by performing physiological measurements of a user with wearable sensors (1), detecting an emotional state of said user according to the performed measurements, preferably using an autoassociative memory, and generating commands or instructions for external devices (3) whenever an emotional change is detected, in order to compensate or alleviate said emotional change.

Claims

1. An affective well-being supervision system comprising:

at least one wearable sensor (1) configured to measure at least one physiological signal of a user;

a logical means (2) configured to receive the at least one measured physiological signal, to detect an emotional change according to said signal, and to generate instructions to an external device (3) according to the detected emotional change;

a first communication means configured to connect the at least one wearable sensor (1) and the logical means (2); and

a second communication means configured to connect the logical means (2) to the external device (3) and to send the generated instructions to said external device (3),

wherein the logical means (2) comprise a memory (7) and the logical means (2) is adapted to;

if a data block of the measured signal is classified as being a neutral physiological state, store in the memory (7) said data block classified as being a neutral physiological state; and

detect the emotional change by comparing each incoming data block of the measured physiological signal with the last data block of said signal stored in the memory (7) classified as being a neutral physiological state.

2. The system according to claim 1 wherein the logical means further comprise a non parametric sequential change point detector (9).

3. The system according to claim 1 wherein the logical means further comprise a plurality of classifying methods (20) sharing a same input, and a vote counter (21) that determines if the emotional change occurs according to outputs of said plurality of classifying methods (20).

4. The system according to claim 3 wherein the plurality of classifying methods (20) comprise a Support Vector Machine, a Linear discriminant analysis, and a decision tree.

5. The system according to claim 1 wherein the first communication means are wireless communication means.

6. The system according to claim 1 wherein the second communication means are wireless communication means.

7. The system according to claim 1 wherein the logical means (2) comprise a previously trained autoassociative memory (6) and an accumulator to compute and accumulate a difference between the measured signal and an estimation of said measured signal performed by the autoassociative memory (6).

8. The system according to claim 7 wherein the logical means (2) further comprise a misrecognition counter which computes a number of false emotional changes detections, and wherein the logical means (2) are configured to train the autoassociative memory (6) if the misrecognition counter exceeds a threshold.

16. The system according to claim 4 wherein the logical means (2) comprise a previously trained autoassociative memory (6) and an accumulator to compute and accumulate a difference between the measured signal and an estimation of said measured signal performed by the autoassociative memory (6).

17. The system according to claim 16 wherein the logical means (2) further comprise a misrecognition counter which computes a number of false emotional changes detections, and wherein the logical means (2) are configured to train the autoassociative memory (6) if the misrecognition counter exceeds a threshold.

9. A method of affective well-being supervision comprising:

measuring at least one physiological signal of a user;

detecting an emotional change according to the measured signal; and

generating instructions to an external device (3) according to the detected emotional change,

wherein the step of detecting the emotional change further comprises;

if a data block of the measured signal is classified as being a neutral physiological state, storing in the memory (7) said data block classified as being a neutral physiological state; and

comparing each incoming data block of the measured physiological signal with the last data block of said physiological signal stored in the memory (7) classified as being a neutral physiological state.

10. The method according to claim 9 wherein the step of detecting the emotional change further comprises applying a non parametric sequential change point detector (9).

11. The method according to claim 9 wherein the step of detecting the emotional change further comprises applying a plurality of classifying methods (20) sharing a same input, and using outputs of said plurality of classifying methods (20) in a vote counter (21) that determines if the emotional change occurs.

12. The method according to claim 11 wherein the plurality of classifying methods (20) comprise a Support Vector Machine, a Linear discriminant analysis, and a decision tree.

13. The method according to claim 9 wherein the step of detecting an emotional change according to the measured signal comprises estimating the measured signal by means of a previously trained autoassociative memory (6) and computing a difference between the measured signal and the estimation of said measured signal.

14. The method according to claim 13 further comprising, if the computed difference exceeds a threshold, comparing a segment of the at least one measured signal and a previously stored segment of a signal corresponding to a reference emotional state.

15. The method according to claim 13 further comprising computing a number of false emotional changes detections, and training the autoassociative memory (6) if the misrecognition counter exceeds a threshold.