Method for identifying a person's posture
First Claim
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1. A method for identifying postures of a person, comprising:
- attaching a movement sensor on the person, the sensor providing a signal depending on the postures of the person during a time period;
extracting a first signal from the sensor;
making a temporal partition of the first signal into temporal segments each corresponding to a particular one of the postures, said temporal partition;
making a first classification by classifying each temporal segment according to first decision rules, a part of the temporal segments being classified in a class of known posture and another part of the temporal segments, not being classified in a class of known postures, being classified in a class of undetermined posture;
extracting a second signal from the sensor;
creating an automatic learning base, which automatically elaborates second decision rules depending on the second signal and at least some of those temporal segments classified in a class of known postures in the first classification; and
making a second classification by classifying the segments in the class of undetermined posture in a class of known posture of the first classification with said second decision rules,wherein the making the temporal partition includes using partitioning parameters with an activity index based on at least one of a kinetic energy of the person or a change in the pitch or yaw of the person.
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Abstract
A classification method including first classifying an event of any kind by first rules, and then second classifying events, not identified by the first classification, by a learning base reinforced with all the events identified by the first classification. The method is adaptive if the second classification rules are amended according to new examples that were able to be determined by the first rules.
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Citations
10 Claims
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1. A method for identifying postures of a person, comprising:
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attaching a movement sensor on the person, the sensor providing a signal depending on the postures of the person during a time period; extracting a first signal from the sensor; making a temporal partition of the first signal into temporal segments each corresponding to a particular one of the postures, said temporal partition; making a first classification by classifying each temporal segment according to first decision rules, a part of the temporal segments being classified in a class of known posture and another part of the temporal segments, not being classified in a class of known postures, being classified in a class of undetermined posture; extracting a second signal from the sensor; creating an automatic learning base, which automatically elaborates second decision rules depending on the second signal and at least some of those temporal segments classified in a class of known postures in the first classification; and making a second classification by classifying the segments in the class of undetermined posture in a class of known posture of the first classification with said second decision rules, wherein the making the temporal partition includes using partitioning parameters with an activity index based on at least one of a kinetic energy of the person or a change in the pitch or yaw of the person. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification