METHOD FOR CONTROLLING ELECTRONIC EQUIPMENT AND WEARABLE DEVICE

• US 20170228027A1
• Filed: 05/27/2016
• Published: 08/10/2017
• Est. Priority Date: 02/05/2016
• Status: Active Grant

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: LIN, Hsing-Chen, HSUEH, Wen-Chung, HUANG, Ching-Yu, WU, Bo-Sheng, WENG, Chi-Hsiang
• Granted Patent: US 10,120,453 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 1/163 Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 1/1694 the I/O peripheral being a single or a set of motion sensors for pointer control or gesture input obtained by sensing movements of the portable computer
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/0346 with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors

Abstract

A method for controlling an electronic equipment and a wearable device are provided, respectively. The method for controlling the electronic equipment includes the following steps. An inertial signal is detected. A gesture is obtained by dividing the inertial signal or classifying the inertial signal. A controlling command is outputted based on the gesture to control the electronic equipment, such as a desktop device, a portable device or the wearable device.

Claims

1. A method for controlling an electronic equipment, comprising:

detecting an inertial signal;

obtaining a gesture by dividing the inertial signal or classifying the inertial signal; and

outputting a controlling command according to the gesture to control the electronic equipment.

2. The method for controlling the electronic equipment according to claim 1, wherein in the step of detecting the inertial signal, the inertial signal is an acceleration signal or an angular velocity signal.

3. The method for controlling the electronic equipment according to claim 1, further comprising:

correcting the inertia signal to reduce an offset of the inertia signal.

4. The method for controlling the electronic equipment according to claim 1, further comprising:

filtering the inertia signal to remove a noise.

5. The method for controlling the electronic equipment according to claim 1, wherein the step of obtaining the gesture includes:

dividing the inertial signal to be a first sub-signal and a second sub-signal;

extracting a plurality of first features of the first sub-signal and a plurality of second features of the second sub-signal; and

recognizing the gesture related to one of the first sub-signal and the second sub-signal according to the first features and the second features.

6. The method for controlling the electronic equipment according to claim 5, wherein in the step of dividing the inertial signal to be the first sub-signal and the second sub-signal, the inertial signal is divided by a Fast Independent Component Analysis algorithm (Fast ICA algorithm).

7. The method for controlling the electronic equipment according to claim 5, wherein in the step of recognizing the gesture related to one of the first sub-signal and second sub-signal, the gesture is recognized by a decision tree algorithm, a probabilistic neural network algorithm or a fuzzy neural network algorithm.

8. The method for controlling the electronic equipment according to claim 1, wherein the step of obtaining the gesture includes:

extracting a plurality of features of the inertial signal;

classifying the inertial signal to a group according to the features; and

recognizing the gesture related to the inertial signal according to the group.

9. The method for controlling the electronic equipment according to claim 8, wherein in the step of classifying the inertial signal to the group, the inertial signal is classified by a support vector machine algorithm (SVM algorithm).

10. A wearable device, comprising:

a detecting unit, for detecting an inertial signal; and

a processing unit, for obtaining a gesture by dividing the inertial signal or classifying the inertial signal, and for outputting a controlling command according to the gesture to control the electronic equipment.

11. The wearable device according to claim 10, wherein the detecting unit is an accelerometer or a gyroscope.

12. The wearable device according to claim 10, wherein the processing unit includes:

a calibrator, for correcting the inertia signal to reduce an offset of the inertia signal.

13. The wearable device according to claim 10, wherein the processing unit includes:

a filter, for filtering the inertia signal to remove a noise.

14. The wearable device according to claim 10, wherein the processing unit includes:

a separator, for dividing the inertial signal into a first sub-signal and a second sub-signal;

a feature extractor, for extracting a plurality of first features of the first sub-signal and a plurality of second features of the second sub-signal; and

a recognizer, for recognizing the gesture related to one of the first sub-signal and second sub-signal according to the first features and the second features.

15. The wearable device according to claim 14, wherein the separator divides the inertial signal into the first sub-signal and the second sub-signal by a Fast Independent Component Analysis Algorithm (Fast ICAAlgorithm).

16. The wearable device according to claim 14, wherein the recognizer recognizes the gesture related to one of the first sub-signal and second sub-signal by a decision tree algorithm, a probabilistic neural network algorithm or a fuzzy neural network algorithm.

17. The wearable device according to claim 10, wherein the processing unit includes:

a feature extractor, for extracting a plurality of features of the inertial signal; and

a classifier, for classifying the inertial signal to a group according to the features, and for recognizing the gesture related to the inertial signal according to the group.

18. The wearable device according to claim 17, wherein the classifier classifies the inertial signal to the group by a support vector machine algorithm (SVM algorithm).

19. The wearable device according to claim 10, wherein the electronic equipment is the wearable device or a portable device, and the wearable device further comprises:

a database, for storing a reference data used for obtaining the gesture;

a first controlling unit, connecting to the processing unit; and

a first transmitting unit, connecting the processing unit and a second transmitting unit of the portable device, wherein the second transmitting unit is connected to a second controlling unit of the portable device;

wherein the controlling command is transmitted to the first controlling unit to control the wearable device;

or, the controlling command is transmitted to the second controlling via the first transmitting unit and the second transmitting unit to control the portable device.