CN111542012B - Human body tumbling detection method based on SE-CNN - Google Patents
Human body tumbling detection method based on SE-CNN Download PDFInfo
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Abstract
The invention discloses a human body tumble detection method based on SE-CNN, which comprises the following steps of (1) establishing a data acquisition environment: a wireless radio frequency tomography network node is used as a communication basis of the network; constructing a complete wireless sensor network communication system by using network nodes; (2) collecting and processing data: the processing method of the wireless sensor network data is 'transmitting-receiving-storing'; extracting effective links, denoising, wavelet transformation, time domain characteristics and wavelet domain characteristics according to the collected radio frequency signal intensity values, and training an SE-CNN model by using the extracted multi-domain characteristics; (3) the real-time human body tumbling detection method comprises the following steps: and screening the multi-domain features obtained by the XGboost model in a permutation and combination mode on the time domain feature components and the wavelet domain feature components to obtain joint feature components with strong robustness, and establishing a multi-domain feature perception fingerprint library of the falling action. And the SE-CNN is trained by using the extracted multi-domain characteristics, so that the detection of human body tumble can be realized. The invention has simple structure, is convenient and feasible in implementation and is suitable for most scenes.
Description
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a human body tumbling detection method based on SE-CNN.
Background
The ever-aging population worldwide presents a significant challenge to the system of care and prevention of household accidents, particularly for elderly people living alone. According to the latest statistics, the aged people above 60 years old reach 2.4 hundred million and account for 17.3 percent of the total population by 2017 according to the data of China aged offices. This ratio is also rising considering people over age 70. The direct consequence of a fall may be muscle or ligament injury, fracture and head trauma, and consequent brain damage, among others. Serious injury constitutes a significant risk to morbidity and mortality following a fall.
With the advancement of technology, wireless signals have substantially covered every corner of our lives. With the rapid development of artificial intelligence technology in recent years, the role of wireless signals is not limited to the traditional communication field, and is endowed with more possibilities, thus receiving wide attention and driving the development of a series of emerging technologies. In the area covered by the wireless network, human activities can affect wireless signal links, such as physical actions of absorption, reflection, refraction and the like of wireless signals. Different human body actions have different degrees of shielding the wireless link, and therefore different influences can be caused certainly. The human body falling detection is carried out by utilizing a radio frequency wireless sensor network formed by radio frequency signals, appropriate sensor nodes are arranged in a monitoring area, the sensor nodes are self-organized into the wireless sensor network, the state characteristic component of a monitoring target is extracted by adopting a corresponding technology, and the falling state of the human body target is judged by combining a deep learning method. In the monitoring process, a human target does not need to carry equipment, and video or photographing monitoring is not needed, so that the privacy of a user can be protected to a great extent in a bedroom or a toilet where the falling probability is large and the user is private, and the acceptance of the user to a wireless signal technology is increased. The wireless radio frequency signal technology is used for detecting the falling of the human body, has the characteristics of easiness in arrangement, low cost, privacy protection and the like, has good research value and commercial value, and has wide application scenes in the arrangement of accelerating smart cities, intelligent medical treatment and smart homes in the future.
Disclosure of Invention
The invention aims to solve the problems that: the human body tumble detection method based on the SE-CNN is simple in structure, convenient and feasible in implementation and suitable for most scenes.
The technical scheme provided by the invention for solving the problems is as follows: a human body tumble detection method based on SE-CNN comprises the following steps,
(1) and building a data acquisition environment: a wireless radio frequency tomography network node is used as a communication basis of the network; constructing a complete wireless sensor network communication system by using network nodes;
(2) collecting and processing data: the processing method of the wireless sensor network data is 'transmitting-receiving-storing'; extracting effective links, denoising, wavelet transformation, time domain characteristics and wavelet domain characteristics according to the collected radio frequency signal intensity values, and training an SE-CNN model by using the extracted multi-domain characteristics;
(3) the real-time human body tumbling detection method comprises the following steps: and screening the multi-domain features obtained by the XGboost model in a permutation and combination mode on the time domain feature components and the wavelet domain feature components to obtain combined feature components with strong robustness, and establishing a multi-domain feature perception fingerprint library of the falling action. And the SE-CNN is trained by using the extracted multi-domain characteristics, so that the detection of human body tumble can be realized.
Preferably, the wireless network system mainly comprises 6 PVC pipes, 36 sensor nodes, 1 aggregation node and a PC.
Preferably, the "transmission-reception-storage" mode is as follows:
(1) the power supply is switched on, and the sensor nodes periodically transmit the radio frequency signal intensity of the link formed by the nodes in a polling mode;
(2) and the periodic radio frequency signal sent by the sensor node is received by a sink node connected to the PC and is transmitted to the PC through the serial assistant to be stored as a txt text.
1. Preferably, the SE-CNN network classification model algorithm is:
(1) and data preprocessing: deleting incomplete periods before and after each data text to obtain a plurality of complete periods of data, converting hexadecimal into decimal, and subtracting an offset to obtain a real RSS value;
(2) and extracting a characteristic value: deleting an invalid link, extracting time domain characteristics from the RSS measured value of the valid link, then using wavelet transformation to process the RSS measured value of the valid link to extract more robust wavelet characteristics, and then utilizing an XGboost model to respectively compare the characteristics of different combinations to select the time domain wavelet combination characteristics with the most distinguishing capability;
(3) modeling and parameter solving: the time domain wavelet joint characteristics and the source RSS measurement value are obtained and used as input data of the SE-CNN method, the characteristics of the wireless radio frequency signal input matrix are extracted by the SE-CNN, and the mutual dependency relationship of characteristic channels of the wireless radio frequency signal input matrix is explicitly modeled.
Preferably, real-time data is collected and time domain characteristics of the RSS measurement values of the effective link are extracted by continuously monitoring in real time, and then more robust wavelet characteristics are extracted by processing the RSS measurement values of the effective link by using wavelet transformation. And taking the acquired time domain wavelet joint characteristics and the source RSS measured value as input data of the SE-CNN model. And establishing a fingerprint database so as to detect the condition of the human body in real time.
Compared with the prior art, the invention has the advantages that: the work applies radio frequency signals to people flow rate monitoring, uses a novel wireless radio frequency tomography network node as a communication node, utilizes the characteristic that the radio frequency signals can be influenced by a human body and combines a machine learning method to monitor people flow rate, is simple in structure, convenient and feasible in realization and suitable for most scenes.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of the monitoring system configuration of the present invention;
FIG. 2 is a schematic diagram of the wavelet transform of the present invention;
FIG. 3 is a schematic diagram of the SE-CNN-based classification performance comparison of the present invention;
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the accompanying drawings and examples, so that how to implement the embodiments of the present invention by using technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
A human body tumble detection method based on SE-CNN comprises the following steps,
(1) and building a data acquisition environment: a wireless radio frequency tomography network node is used as a communication basis of the network; constructing a complete wireless sensor network communication system by using network nodes; the node is a low-power-consumption wireless radio frequency network node platform suitable for deployment in smaller scenes such as intrusion detection, people flow statistics and falling monitoring. The low-power-consumption wireless radio frequency network node platform takes a system-on-chip (SoC) chip CC2530 as a core, and 2 programmable LED lamps and 1 key are expanded; the onboard voltage stabilizing module is compatible with the most common 3.3-5V power supply; and the JTAG interface of the programming program is designed as a non-standard interface with only 5 pins (which can supply power to the nodes) so as to reduce the size of the nodes as much as possible, namely, only 24 x 31 mm. Therefore, the low-power-consumption wireless radio frequency network node platform has the characteristics of low cost, small size, light weight, low power consumption, high reliability and the like, and is very suitable for the installation and deployment of indoor intelligent home and intelligent sensing-oriented wireless radio frequency tomography networks.
(2) Collecting and processing data: the processing method of the wireless sensor network data is 'transmitting-receiving-storing'; extracting effective links, denoising, wavelet transformation and time domain characteristics and wavelet domain characteristics according to the collected radio frequency signal intensity values;
(3) the real-time human body tumbling detection method comprises the following steps: and screening the multi-domain features obtained by the XGboost model in a permutation and combination mode on the time domain feature components and the wavelet domain feature components to obtain joint feature components with strong robustness, and establishing a multi-domain feature perception fingerprint library of the falling action. And the extracted multi-domain characteristics are used for training the SE-CNN model, so that the detection of human body tumble can be realized.
The wireless network system mainly comprises 6 PVC pipes, 36 sensor nodes, 1 aggregation node and a PC.
The transmission-receiving-storing mode is as follows:
(1) the power supply is switched on, and the sensor nodes periodically transmit the radio frequency signal intensity of the link formed by the nodes in a polling mode;
(2) and the periodic radio frequency signal sent by the sensor node is received by a sink node connected to the PC and is transmitted to the PC through the serial assistant to be stored as a txt text.
2. The SE-CNN network classification model algorithm is as follows:
(1) and data preprocessing: deleting each data text to obtain incomplete periods, enabling the obtained data to be a plurality of complete periods, collecting enough RSS sample data and filling missing values;
(2) and extracting a characteristic value: deleting an invalid link, extracting time domain characteristics from the RSS measured value of the valid link, then using wavelet transformation to process the RSS measured value of the valid link to extract more robust wavelet characteristics, and then utilizing an XGboost model to respectively compare the characteristics of different combinations to select the time domain wavelet combined characteristics with the most distinguishing capability. (ii) a
(3) And establishing a model: the acquired time domain wavelet domain joint characteristics and the source RSS measurement value are used as input data of an SE-CNN model, the characteristics of a wireless radio frequency signal input matrix are extracted by the SE-CNN model, and the mutual dependency relationship of characteristic channels of the wireless radio frequency signal input matrix is explicitly modeled.
The algorithm is realized according to the following principle: the resonance frequency of the radio frequency signal is consistent with that of water at 2.4GHz, and the human body can generate great influence on the radio frequency signal due to the fact that most of the human body consists of water, and the algorithm is provided.
Real-time data is collected and the time domain characteristics of the RSS measured values of the effective links are extracted through continuous real-time monitoring, and then the wavelet transform is used for processing the RSS measured values of the effective links to extract more robust wavelet characteristics. And taking the acquired time domain wavelet domain joint characteristics and the source RSS measured value as input data of the SE-CNN model. And establishing a fingerprint database so as to detect the condition of the human body in real time. The invention has the beneficial effects that:
the foregoing is merely illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the claims. The present invention is not limited to the above embodiments, and the specific structure thereof is allowed to vary. All changes which come within the scope of the invention as defined by the independent claims are intended to be embraced therein.
Claims (3)
1. A human body tumbling detection method based on SE-CNN is characterized in that:
(1) and building a data acquisition environment: a wireless radio frequency tomography network node is used as a communication basis of the network; constructing a complete wireless sensor network communication system by using the network node;
(2) collecting and processing data: the processing method of the wireless sensor network data is 'transmitting-receiving-storing'; extracting effective links, denoising, wavelet transformation, time domain characteristics and wavelet domain characteristics according to the collected radio frequency signal intensity values, and training an SE-CNN model by using the extracted multi-domain characteristics;
(3) the transmission-receiving-storage mode is as follows:
1) the power supply is switched on, and the sensor nodes periodically transmit the radio frequency signal intensity of a link formed by each node in a polling mode;
2) the periodic radio frequency signal sent by the sensor node is received by a sink node connected to the PC and is transmitted to the PC through a serial assistant to be stored as a txt text;
(4) the real-time human body tumbling detection method comprises the following steps: screening multi-domain features obtained by arranging and combining the time domain feature components and the wavelet domain feature components by using an XGboost model to obtain joint feature components with strong robustness, and establishing a multi-domain feature perception fingerprint library of the falling action; the SE-CNN is trained by using the extracted multi-domain characteristics, so that the detection of human body tumble can be realized;
(5) the method is characterized in that: the SE-CNN model algorithm is as follows:
1) and data preprocessing: deleting incomplete periods before and after each data text to obtain a plurality of complete periods of data, converting hexadecimal into decimal, and subtracting an offset to obtain a real RSS value;
2) and extracting a characteristic value: deleting an invalid link, extracting time domain characteristics from the RSS measured value of the valid link, then using wavelet transformation to process the RSS measured value of the valid link to extract more robust wavelet characteristics, and then utilizing an XGboost model to respectively compare the characteristics of different combinations to select the time domain wavelet combination characteristics with the most distinguishing capability;
3) and establishing a model: the time domain wavelet joint characteristics and the source RSS measurement value are obtained and used as input data of the SE-CNN method, the characteristics of the wireless radio frequency signal input matrix are extracted by the SE-CNN, and the mutual dependency relationship of characteristic channels of the wireless radio frequency signal input matrix is explicitly modeled.
2. The SE-CNN-based human fall detection method of claim 1, wherein: the wireless sensor network communication system mainly comprises 6 PVC pipes, 36 sensor nodes, 1 aggregation node and a PC.
3. The SE-CNN-based human body tumbling detection method as claimed in claim 1, wherein the human body tumbling detection is realized by continuously performing real-time monitoring, collecting real-time data, extracting more robust wavelet features by processing effective link RSS measurement values through wavelet transformation, obtaining time domain wavelet domain joint features with strong robustness through screening by using an XGboost model, automatically obtaining the importance degree of feature channels through a deep learning network, and improving useful features according to the importance degree of the feature channels.
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