The present invention discloses a method and a system for building a human fall detection model. The method can include: collecting the speed information of one and more human fall samples in the human fall process; the speed information includes resultant acceleration, speed and time; analyzing the characteristics of the speed information, calculating and extracting low acceleration threshold value a0, resultant acceleration series within fixed time I0, high acceleration threshold value a1, curve area threshold value ΔS and low acceleration time threshold value ΔT, and building the fall detection mechanism. The system of the present invention fully takes into account the characteristics of human body's movement behaviors, so as to increase the detection rate and reduce the misjudgment rate.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for building a human fall detection model, comprising: collecting the speed information of one and more human fall samples in the human fall process; the speed information includes resultant acceleration, speed and time; analyzing the characteristics of the speed information, calculating and extracting low acceleration threshold value a0, resultant acceleration series within fixed time I0, high acceleration threshold value a1, curve area threshold value ΔS and low acceleration time threshold value ΔT, and building the fall detection mechanism. 2. The method for building a human fall detection model according to claim 1, wherein analyzing the characteristics of the speed information, and calculating and extracting low acceleration threshold value a0, resultant acceleration series within fixed time I0, high acceleration threshold value a1, curve area threshold value ΔS and low acceleration time threshold value ΔT further comprises: associating the calculated and extracted low acceleration threshold value a0, resultant acceleration series within fixed time I0, high acceleration threshold value a1, curve area threshold value ΔS and low acceleration time threshold value ΔT with the corresponding human body state information to the sample; the human body state information includes height, weight and/or state of movement. 3. The method for building a human fall detection model according to claim 1, wherein the following is also included after building the fall detection mechanism: receiving the setup of human body state information, and adjusting a0, ΔS and/or ΔT according to the human body state information. 4. The method for building a human fall detection model according to claim 1, wherein the method further comprises: incorporating the self-detected human fall samples in the fall detection mechanism, and associating the speed information of the samples with the human body state information. 5. A human body fall detection model system, comprising: a sample collection module, which is used to collect the speed information of one and more human fall samples in the human fall process; the speed information includes resultant acceleration, speed and time; an analysis and extraction module, which is used to analyze the characteristics of the speed information, calculate and extract low acceleration threshold value a0, resultant acceleration series within fixed time I0, high acceleration threshold value a1, curve area threshold value ΔS and low acceleration time threshold value ΔT, and build the fall detection mechanism. 6. The human body fall detection model system according to claim 5, wherein the analysis and extraction module is also used to: associate the calculated and extracted low acceleration threshold value a0, resultant acceleration series within fixed time I0, high acceleration threshold value a1, curve area threshold value ΔS and low acceleration time threshold value ΔT with the corresponding human body state information to the sample; the human body state information includes height, weight and/or state of movement. 7. The human fall detection model system according to claim 5, wherein the system further comprises: a setup receiving module, which is used to receive the setup of human state information, and to adjust a0, ΔS and/or ΔT according to the preset human body state information. 8. The human fall detection model system according to claim 5, wherein the system further comprises: a self-learning module, which is used to incorporate the self-detected human fall samples into the fall detection mechanism, and associate the speed information of the sample with the human body state information.