IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0330673
(2002-12-30)
|
등록번호 |
US-7409373
(2008-08-05)
|
발명자
/ 주소 |
|
출원인 / 주소 |
|
대리인 / 주소 |
Ostrolenk, Faber, Gerb & Soffen, LLP
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인용정보 |
피인용 횟수 :
7 인용 특허 :
11 |
초록
▼
Method and arrangement for providing a computerized system having an interface arrangement for interfacing a data source. The data source delivering data related to motion of a person, a memory arrangement for storing said data, a processor for processing the data, an artificial neural network (ANN)
Method and arrangement for providing a computerized system having an interface arrangement for interfacing a data source. The data source delivering data related to motion of a person, a memory arrangement for storing said data, a processor for processing the data, an artificial neural network (ANN) using the processor, means for collecting a second set of data from the person, means for calculating one or several parameters distinctive of various features of said person, and means for feeding the parameter values to the ANN trained to recognize the various features.
대표청구항
▼
The invention claimed is: 1. A method for the detection of a characteristic of a person by means of a computer which generates a report placing said person in a special category in need of treatment, said computer employing a trained artificial neural network (ANN) in which motion data are analyzed
The invention claimed is: 1. A method for the detection of a characteristic of a person by means of a computer which generates a report placing said person in a special category in need of treatment, said computer employing a trained artificial neural network (ANN) in which motion data are analyzed, said method comprising: measuring motion data from said person; collecting other measured data from said person, under influence of a drug; calculating the values of one or several parameters distinctive of various characteristics; feeding said parameter values to said ANN trained to recognize said various characteristics; analyzing said parameter values in said trained ANN; wherein said parameters comprise at least the variance of continuous performance task (CPT) variables, said variance of CPT variables comprising variance of latency t defined as: where the latency t is the delay or reaction time, N being the number of samples of the complete measurement, and 2. The method of claim 1, wherein said characteristics include at least one psychological syndrome. 3. In a computerized system, a method for the detection of patients with ADHD employing a trained artificial neural network (ANN) in which motion data are analyzed, said method comprising: measuring motion data on a patient; collecting other measured data from the patient; calculating, from said motion data and other measured data, the values of one or several parameters distinctive of ADHD; feeding said parameter values to said ANN trained to recognize ADHD; analyzing said parameter values in said trained ANN; and wherein said parameters comprise: the variance of distance, the variance of continuous performance task (CPT) variables, the residual signal defined as difference between the input signal and a smoothed version of the same, an estimate of immobility duration, and one or more parameters suited to detect periodicity in the one or more of the input signals; said variance of CPT variables comprising variance of latency t defined as: where the latency t is the delay or reaction time, N being the number of samples of the complete measurement, and 4. The method of claim 3, wherein said ANN is trained with data collected from patients being under influence of drugs. 5. The method of claim 3, wherein said ANN comprises a number of nodes representing sets of training data. 6. The method of claim 3, wherein the ANN is a Kohonen-map type ANN. 7. The method of claim 3, further comprising the use of linear predictive coding (LPC) to analyze the parameter values fed to the ANN. 8. The method of claim 3, wherein said parameters are used for optimal correlation between parameter distance and conceptual distance. 9. A method for the detection of a characteristic of a person by means of a computer which generates a report placing said person in a special category in need of treatment, said computer employing a single trained artificial neural network (ANN) in which motion data are analyzed, said method comprising: measuring motion data from said person; collecting other measured data from said person, under influence of a drug; calculating the values of one or several parameters distinctive of various characteristics; feeding said parameter values to said ANN trained to recognize said various characteristics; analyzing said parameter values in said trained ANN; wherein said parameters comprise: the variance of distance, the variance of continuous performance task (CPT) variables, the residual signal defined as difference between the input signal and a smoothed version of the same, an estimate of immobility duration, and at least one additional parameter suited to detect periodicity in the one or more of the input signals; said variance of CPT variables comprising variance of latency t defined as: where the latency t is the delay or reaction time, N being the number of samples of the complete measurement, and
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