Deriving movement behaviour from sensor data

A sensor and motion technology, applied in the field of machine learning, which can solve the problem that labeled data cannot be reused

Inactive Publication Date: 2018-03-16
圣蒂安斯公众有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As a result, each system requires large amounts of manually labeled training data, and labeled data from one system cannot be reused by another system

Method used

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  • Deriving movement behaviour from sensor data
  • Deriving movement behaviour from sensor data
  • Deriving movement behaviour from sensor data

Examples

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Embodiment Construction

[0052] The present invention relates to a method and machine learning framework for estimating, predicting or detecting the motion behavior of a user of a mobile communication device. The invention also relates to methods for training such frameworks without the need for large amounts of manually labeled training data.

[0053] figure 1 A general overview of a machine learning framework 100 according to an embodiment of the invention is shown. The framework takes as input raw sensor data 110 from the user's mobile communication device. Raw sensor data 110 is obtained from sensors in the mobile communication device, such as, for example, accelerometers, compasses and / or gyroscopes. The framework 100 estimates, as output 112 , some type of athletic behavior 112 of a user of the mobile communication device.

[0054] A first type of motor behavior is, for example, driving behavior characterized by assigning points to discrete driving events such as but not limited to braking, a...

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Abstract

Method for estimating movement behaviour of a user of a mobile communication device by a neural network comprising one or more lower and one or more higher hidden layers. The method comprising a stepof obtaining (401) sensor data from sensors in the mobile device; a step of obtaining (402) measurements related to a movement of the user; a step of labelling (403) these measurements as weakly labelled data; pre-training (404) the lower hidden layers to estimate the measurements from the first set of sensor data; a step of obtaining (405) a second set of sensor data wherein movement behaviour ofthe user is labelled as labelled data; a step of training (406) the higher hidden layers with the labelled data to estimate the movement behaviour of the user as said output.

Description

technical field [0001] The present invention relates to machine learning, and more specifically, to deep learning using neural networks to analyze a user's athletic behavior based on raw sensor data. Background technique [0002] The user's motion behavior can be described by a feature set such as the traffic mode in the traffic session (session), the driving aggressiveness in the driving session, the walking pace or the number of steps in the walking session, and so on. [0003] To estimate and summarize this motion behavior, traditional methods of measuring these features require the user to wear dedicated sensors or motion capture devices. Most people today carry a smartphone, and most smartphones contain sensors such as accelerometers, gyroscopes, magnetometers, compasses, barometers, and GPS, which can be used as inexpensive and widely available alternatives to these dedicated sensors or motion capture devices. Alternatives used. [0004] There are already some specif...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06N3/044B60W40/09
Inventor 弗兰克·韦尔比斯特乔伦·范·泽韦伦文森特·斯普鲁伊特文森特·约克凯
Owner 圣蒂安斯公众有限公司
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