US 2019/0320103 A1 · FUSION OF INERTIAL AND DEPTH SENSORS FOR MOVEMENT MEASUREMENTS AND RECOGNITION

Abstract

A method of recognizing movement includes measuring, by an inertial sensor, a first unit of inertia of an object. In addition, the method includes measuring a three dimensional shape of the object. Further, the method includes receiving, by a processor, a signal representative of the measured first unit of inertia from the inertial sensor and a signal representative of the measured shape from the depth sensor. Still further, the method includes determining a type of movement of the object based on the measured first unit of inertia and the measured shape utilizing a classification model.

Claims11

Dependent claims

A method of recognizing movement comprising:

measuring, by an inertial sensor, a first unit of inertia of an object;
measuring a three dimensional shape of the object;
receiving, by a processor, a signal representative of the measured first unit of inertia from the inertial sensor and a signal representative of the measured shape from the depth sensor;
determining a type of movement of the object based on the measured first unit of inertia and the measured shape utilizing a classification model.

1 more element

A non-transitory computer-readable medium storing instructions that when executed on a computing system cause the computing system to:

receive a signal representative of a measured first unit of inertia from an inertial sensor coupled to an object and a signal representative of a measured shape of the object from a depth sensor;
and
determine a type of movement of the object based on the measured first unit of inertia and the measured shape utilizing a classification model.

Dependencies are inferred from the claim text.

Facts

Application

16/453,999Filed 26 Jun 2019

Priority

6 Apr 2015

Published

17 Oct 2019

Term ends

Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06V 40/23
Movements or behaviour, e.g. gesture recognition: recognition of whole body movements, e.g. for sport training
G06F 18/24155
Based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate: bayesian…
G06F 18/254
Fusion techniques: of classification results, e.g. of results related to same input data
G06F 18/295
Graphical models, e.g. Bayesian networks: markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding…

Family

Application62/143,331Us provisional application · filed 6 Apr 2015

Application15/092,347Continuation · filed 6 Apr 2016

Application16/453,999Continuation

US 10,432,842 B2Related patent

US 2016/0292497 A1Published application

US 2019/0320103 A1This patent

From the US filing history. Foreign family members aren’t listed.

Fusion of inertial and depth sensors for movement measurements and recognition

Life of this patent

Fusion of inertial and depth sensors for movement measurements and recognition

Life of this patent

Abstract

Claims11