1. Field of the Invention
The present invention relates to a feature-quantity extracting apparatus which extract a feature quantity from entered data.
2. Description of the Related Art
Technology is known, in which a device having a built-in acceleration sensor is put on a human body and behavior of the human is estimated based on signal data sent from the acceleration sensor.
When the technology is applied to activity meters, into which pedometers are developed, an activity quantity (METS hours) will be calculated more precisely based on the behavior of the human wearing the active meter. Further, calories consumed in the activity can be calculated from the activity quantity and personal information (height, weight, etc).
In general, the following system is employed as the specific technology for estimating behavior of a human. For example, in the system, various sorts of feature quantities are acquired for every given interval from signal data which is acquired while the human is in some behavior. Meanwhile, feature quantities are acquired from plural humans whose behavior is previously known and the acquired feature quantities are used as supervised learning data. When data is acquired from a sensor while a human is in unknown behavior, a similar feature quantity is calculated from the acquired data. The similar feature quantity is collated with the supervised learning data, whereby the behavior of the human is estimated. More specifically, using the well known classifying method such as AdaBoost and Support Vector Machine (SVM), the system generates a classifier from the feature quantity used as the supervised learning data and stores the classifier in an activity meter. When using the activity meter, the system calculates a feature quantity from data that is output from the sensor while the human is in unknown behavior and enters the calculated feature quantity to the classifier, thereby acquiring a resultant classification.
As conventional technology for estimating the human behavior, the following system is known (for example, refer to Japanese Unexamined Patent Publication No. Hei10-113343). In the system, a measuring device is fit on a human to measure his/her motion and/or behavior, and a feature-quantity extracting unit extracts a feature quantity from a signal representing the motion and/or behavior of the human, and a signal processing unit for confirming human motion and/or behavior compares the extracted feature quantity with reference data, wherein the reference data is previously stored in a database of data representing feature quantities of various sorts of human motions and/or behaviors, whereby the motion and/or behavior of the feature quantity having a highest correlational relationship is output as a classification result.
Generally, in the conventional system, a frequency feature quantity acquired in a time-frequency transform arithmetic processing such as the Fourier transform and Wavelet transform is used as the feature quantity to be calculated from data of the sensor.