# US 10,120,453 B2 · Method for controlling electronic equipment and wearable device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,120,453 B2 - Document ID: US10120453B2 - Status: Granted - Kind: B2 - Application number: 15/167,192 - Filed: 2016-05-27 - Published: 2017-08-10 - Granted: 2018-11-06 - Earliest claimed: 2016-02-05 - Assignees: Industrial Technology Research Institute - Applicants: Industrial Technology Research Institute - Inventors: Wen-Chung Hsueh; Hsing-Chen Lin; Ching-Yu Huang; Bo-Sheng Wu; Chi-Hsiang Weng - Web page: https://patentgrep.com/patent/US10120453B2 ## CPC - 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)} - G06F 1/163: {Wearable computers, e.g. on a belt} - 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} - G06F 3/0346: with detection of the device orientation or free movement in a three-dimensional [3D] space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## 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 ### Claim 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, wherein the inertial signal is classified into a first group and a second group, and the second group is classified into a first sub-group and a second sub-group, the first group is a non-movement state, the second group is a movement state, the first sub-group is an exercise movement state, and the second sub-group is an exercise-gesture state; and outputting a controlling command according to the gesture to control the electronic equipment, 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. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 1) The method for controlling the electronic equipment according to claim 1, further comprising: filtering the inertia signal to remove noise. ### Claim 5 (depends on 1) The method for controlling the electronic equipment according to claim 1, 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). ### Claim 6 (depends on 1) The method for controlling the electronic equipment according to claim 1, 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. ### Claim 7 (depends on 1) 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 according to the features; and recognizing the gesture related to the inertial signal. ### Claim 8 (depends on 7) The method for controlling the electronic equipment according to claim 7, wherein in the step of classifying the inertial signal, the inertial signal is classified by a support vector machine algorithm (SVM algorithm). ### Claim 9 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, wherein the inertial signal is classified into a first group and a second group, and the second group is classified into a first sub-group and a second sub-group, the first group is a non-movement state, the second group is a movement state, the first sub-group is an exercise movement state, and the second sub-group is an exercise-gesture state, and for outputting a controlling command according to the gesture to control electronic equipment, 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. ### Claim 10 (depends on 9) The wearable device according to claim 9, wherein the detecting unit is an accelerometer or a gyroscope. ### Claim 11 (depends on 9) The wearable device according to claim 9, wherein the processing unit includes: a calibrator, for correcting the inertia signal to reduce an offset of the inertia signal. ### Claim 12 (depends on 9) The wearable device according to claim 9, wherein the processing unit includes: a filter, for filtering the inertia signal to remove noise. ### Claim 13 (depends on 9) The wearable device according to claim 9, 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 ICA Algorithm). ### Claim 14 (depends on 9) The wearable device according to claim 9, 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. ### Claim 15 (depends on 9) The wearable device according to claim 9, 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 according to the features, and for recognizing the gesture related to the inertial signal. ### Claim 16 (depends on 15) The wearable device according to claim 15, wherein the classifier classifies the inertial signal by a support vector machine algorithm (SVM algorithm). ### Claim 17 (depends on 9) The wearable device according to claim 9, 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.