CN114595748A - Data segmentation method for fall protection system - Google Patents

Data segmentation method for fall protection system Download PDF

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CN114595748A
CN114595748A CN202210157764.6A CN202210157764A CN114595748A CN 114595748 A CN114595748 A CN 114595748A CN 202210157764 A CN202210157764 A CN 202210157764A CN 114595748 A CN114595748 A CN 114595748A
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CN114595748B (en
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刘继忠
冯明旭
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Nanchang University
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Abstract

The invention relates to a data segmentation method for a fall protection system, which comprises the following steps: acquiring a data set of falling actions and daily behavior actions of a user; preprocessing the acquired data set, intercepting data of an inertial sensor worn by the user before the user falls down and impacts the ground, and establishing a training data set; training a training data set based on a multi-channel convolutional neural network model MC-CNN to obtain feature maps and corresponding weights of all channels; and (3) combining the obtained feature map and corresponding weight with an importance mapping method to obtain the importance of each sequence position for data segmentation, thereby determining a specific area for data segmentation and further being used for fall detection of a fall protection system. The method can clearly divide the triggering condition of falling detection and the intercepting length of the window, avoids the defect that the length of the data dividing window is determined by experience in the existing dividing method, and solves the problem that a falling protection system is difficult to divide data.

Description

Data segmentation method for fall protection system
Technical Field
The invention relates to the technical field of sensor data segmentation, in particular to a data segmentation method for a fall protection system.
Background
Falling of elderly people is a major public health problem worldwide. Tumble is the leading cause of death, disability and loss of independence in the elderly over age 65, and is also the leading cause of hospitalization (one elderly is hospitalized by tumbling every 11 seconds). Therefore, an automatic fall protection system is very interesting.
The main purpose of a fall protection system is to accurately detect when a fall has occurred in order to activate the fall protection device before the body hits the ground in order to reduce fall injuries.
In general, fall protection systems are more suitable for wearable systems based on inertial sensors. The core of the fall protection system is a detection algorithm which is responsible for continuously classifying any movement of the human body as a fall or a daily life action. It has been widely verified that artificial intelligence based detection methods can achieve higher accuracy than threshold based methods.
Since for fall protection systems based on artificial intelligence models the classification features can only be extracted from data segments of limited duration, the detection algorithm first segments the data, i.e. divides the continuous data stream obtained from the inertial sensors into a plurality of data segments. There are two general approaches to data segmentation, the first being a fixed duration sliding window. The duration of the sliding window defines the boundary of the data segment, and features and classification are further extracted after interception. Although the method is simple, the calculation amount is large, and the method is not suitable for a fall protection system with a high real-time requirement. The second method first sets a trigger condition to detect a potential fall event in the input data stream by searching for inertial sensor values that exceed a preset threshold. When a potential fall event is detected, the data segments are intercepted through one or more windows, and classification is achieved. However, how the window duration is determined, there is no clear method at present, and most are experimental empirical values. If the duration of the window is too long, the real-time property of falling detection is influenced, and the protection device does not have enough time to open; if the window duration is too short, the accuracy of fall detection will be affected.
Disclosure of Invention
To address the above-discussed deficiencies and inadequacies of the prior art, it is an object of the present invention to provide a method of inertial sensor data segmentation. The data segmentation method is realized by a multi-channel convolutional neural network MC-CNN and an importance mapping method. The method comprises the steps of preprocessing an existing data set, intercepting data of an inertial sensor before a human body impacts the ground, constructing a training data set, training through an MC-CNN network, extracting model parameters, and determining the importance of each sequence position for classification by combining an importance mapping method, so that the clear range of data segmentation is determined.
In order to achieve the purpose, the invention adopts the following technical scheme:
a data segmentation method for a fall protection system, comprising the steps of:
s1, acquiring a data set of falling actions and daily behaviors of a user;
s2, preprocessing the acquired data set, intercepting data of an inertial sensor worn by the user before the user falls down and impacts the ground, and establishing a training data set;
s3, training a training data set based on a multi-channel convolutional neural network model MC-CNN to obtain feature maps and corresponding weights of all channels;
s4, combining the obtained feature map and corresponding weight with an importance mapping method to obtain the importance of each sequence position for data segmentation, thereby determining a specific area of the data segmentation;
and S5, setting a corresponding data segmentation algorithm in the falling protection system according to the determined data segmentation area, wherein the corresponding data segmentation algorithm is used for falling detection of the falling protection system.
In step S2, the acquired data set is preprocessed, and the preprocessing process is as follows:
s21, calculating signal amplitude vectors of three-axis inertial sensor
Figure BDA0003513456880000021
Wherein the content of the first and second substances,
Figure BDA0003513456880000022
Figure BDA0003513456880000023
and
Figure BDA0003513456880000024
representing x, y and z inertial sensor values, respectively;
s22, establishing a data set: for the falling action, the SMV peak point represents the moment when the body impacts the ground, so that a signal 1s before the maximum peak point is intercepted and used as a training data set; for the daily behavior actions such as walking slowly, running, going upstairs and going downstairs, data are intercepted at the time interval of 1s and serve as a training data set.
The inertial sensor in step S2 includes a three-axis accelerometer and a gyroscope.
In step S3, the training data set is trained based on the multi-channel convolutional neural network model MC-CNN, and the training process is:
firstly, processing signals of a three-axis accelerometer and a gyroscope independently, comprehensively considering the influence of the signals of the three-axis accelerometer and the gyroscope on classification after convolution operation, merging feature maps of two parts to obtain an area which is more important for classification, and finally judging the classification quality by using a cross entropy loss function to finish the training of a data set;
in the above process, the Convolution operation is composed of four one-dimensional Convolution 1D Convolution and global average pooling layers GAP, a linear full-link layer FC, and a softmax logistic regression layer, Convolution kernels of the four one-dimensional Convolution 1D Convolution are respectively 8, 16, 32, and 64, and the global average pooling layer GAP reduces a feature mapping dimension of each channel of the fourth Convolution layer from (1 × 100) to (1 × 1).
In step S4, the importance of each sequence position for data segmentation is obtained by combining the importance mapping method, and the specific process is as follows:
after the MC-CNN model is trained, feature maps and corresponding weights of all channels can be obtained, and input inertia is subjected toSensor time series in Sk(x) Representing the output sequence on channel k, x represents the time position on the sequence,
Figure BDA0003513456880000031
representing the weight of each channel feature k to a different class c, the input to the softmax logistic regression layer is denoted gcThen, there are:
Figure BDA0003513456880000032
from this, an importance mapping is built from the sequences to each class C, defined as Mc；
Figure BDA0003513456880000033
Mc(x) Indicating the importance of position x in the time series for classifying the series as c.
In step S4, M is implementedc(x) After the visualization, specific areas of data segmentation are determined by summarizing the rules of the importance mapping maps of the areas, and data segments corresponding to the areas are used for behavior classification based on a machine learning model so as to verify whether the data segments are falling actions.
Compared with the prior art, the invention has the beneficial effects that:
the method is realized by a multichannel convolutional neural network MC-CNN and an importance mapping method. The method comprises the steps of intercepting data of an inertial sensor before a human body impacts the ground through preprocessing of an existing data set, establishing a training data set, then training the data set through an MC-CNN network, extracting model parameters, and determining the importance of each sequence position for classification by combining an importance mapping method, so that the clear range of data segmentation is determined.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a general flow chart of a data segmentation method for a fall protection system according to the present invention;
figure 2 is a diagram of fall signature analysis in an embodiment of the invention;
FIG. 3 is a schematic diagram of the components of a multi-channel convolutional neural network in an embodiment of the present invention;
FIG. 4 is a sample graph of an importance map in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without any inventive step, are within the scope of the present invention.
Example (b): see fig. 1-4.
As shown in fig. 1, the data segmentation method for a fall protection system of the present invention includes the following steps:
s1, acquiring a data set of falling actions and daily behaviors of a user;
s2, preprocessing the acquired data set, intercepting data of an inertial sensor worn by the user before the user falls down and impacts the ground, and establishing a training data set;
s3, training a training data set based on a multi-channel convolutional neural network model MC-CNN to obtain feature maps and corresponding weights of all channels;
s4, combining the obtained feature map and corresponding weight with an importance mapping method to obtain the importance of each sequence position for data segmentation, thereby determining a specific area of the data segmentation;
and S5, setting a corresponding data segmentation algorithm in the falling protection system according to the determined data segmentation area, wherein the corresponding data segmentation algorithm is used for falling detection of the falling protection system.
In step S2, the acquired data set is preprocessed, and the preprocessing process is as follows:
s21, calculating signal amplitude vectors of three-axis inertial sensor
Figure BDA0003513456880000041
Wherein the content of the first and second substances,
Figure BDA0003513456880000042
Figure BDA0003513456880000043
and
Figure BDA0003513456880000044
representing x, y and z inertial sensor values, respectively;
s22, establishing a data set: as shown in fig. 2, for a falling action, the SMV peak point represents the moment when the body hits the ground, so the signal 1s before the maximum peak point is intercepted as a training data set; for the daily behavior actions such as walking slowly, running, going upstairs and going downstairs, data are intercepted at the time interval of 1s and serve as a training data set.
The inertial sensor in step S2 includes a three-axis accelerometer and a gyroscope.
In step S3, the training data set is trained based on the multi-channel convolutional neural network model MC-CNN, and the training process is:
as shown in fig. 3, firstly, signals of the triaxial accelerometer and the gyroscope are processed independently, after convolution operation, the influence of the signals of the triaxial accelerometer and the gyroscope on classification is considered comprehensively, then feature maps of the two parts are merged to obtain an area which is important for classification, and finally, the classification quality is judged by using a cross entropy loss function to complete the training of a data set;
in the above process, the Convolution operation is composed of four one-dimensional Convolution 1D Convolution and global average pooling layers GAP, a linear full-link layer FC, and a softmax logistic regression layer, Convolution kernels of the four one-dimensional Convolution 1D Convolution are respectively 8, 16, 32, and 64, and the global average pooling layer GAP reduces a feature mapping dimension of each channel of the fourth Convolution layer from (1 × 100) to (1 × 1); different from the existing CNN network, the method cancels any pooling operation after the convolutional layer, thereby ensuring that the data length of the feature map is unchanged so as to be convenient for determining the subsequent segmentation area.
In step S4, the importance of each sequence position for data segmentation is obtained by combining the importance mapping method, and the specific process is as follows:
after the MC-CNN model is trained, feature maps and corresponding weights of all channels can be obtained, and S is used for an input inertial sensor time sequencek(x) Representing the output sequence on channel k, x represents the time position on the sequence,
Figure BDA0003513456880000051
representing the weight of each channel feature k to a different class c, the input to the softmax logistic regression layer is denoted gcThen, there are:
Figure BDA0003513456880000052
from this, an importance mapping is built from the sequences to each class C, defined as Mc；
Figure BDA0003513456880000053
Mc(x) Indicating the importance of position x in the time series for classifying the series as c.
In step S4, M is implementedc(x) After the visualization, as shown in fig. 4, a specific area of data segmentation is determined by summarizing the rules of the importance mapping diagram, the darker the color bars in the diagram indicate that the corresponding position sequence is more important for classification, in this embodiment, when the acceleration count value is less than 0.93 times of the gravitational acceleration, a potential fall is determined, and from this moment, accelerometer and gyroscope data segments are intercepted according to the length of a 310ms window for behavior classification based on a machine learning model, and finally, whether the fall is detected is verified.
It should be noted that the application range of the data segmentation method is not limited to fall detection, and for other types of actions in life, rules can be trained and extracted by the method according to specific conditions, so as to obtain corresponding data segmentation positions.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the structure of the present invention in any way. Any simple modification, equivalent change and modification of the above embodiments according to the technical spirit of the present invention are within the technical scope of the present invention.

Claims (6)

1. A data segmentation method for a fall protection system, comprising the steps of:
s1, acquiring a data set of falling actions and daily behaviors of a user;
s2, preprocessing the acquired data set, intercepting data of an inertial sensor worn by the user before the user falls down and impacts the ground, and establishing a training data set;
s3, training a training data set based on a multi-channel convolutional neural network model MC-CNN to obtain feature maps and corresponding weights of all channels;
s4, obtaining the importance of each sequence position for data segmentation by combining the obtained feature map and corresponding weight with an importance mapping method, thereby determining a specific area of the data segmentation;
and S5, setting a corresponding data segmentation algorithm in the falling protection system according to the determined data segmentation area, wherein the corresponding data segmentation algorithm is used for falling detection of the falling protection system.
2. A data segmentation method for a fall protection system as claimed in claim 1, characterized by the steps of
Preprocessing the acquired data set as described in S2, where the preprocessing process is as follows:
s21, calculating signal amplitude vectors of three-axis inertial sensor
Figure FDA0003513456870000011
Wherein the content of the first and second substances,
Figure FDA0003513456870000012
Figure FDA0003513456870000013
and
Figure FDA0003513456870000014
representing x, y and z inertial sensor values, respectively;
s22, establishing a data set: for the falling action, the SMV peak point represents the moment when the body impacts the ground, so that a signal 1s before the maximum peak point is intercepted and used as a training data set; for the daily behavior actions such as walking slowly, running, going upstairs and going downstairs, data are intercepted at the time interval of 1s and serve as a training data set.
3. A data segmentation method for a fall protection system as claimed in claim 1, characterized by the steps of
The inertial sensor in S2 includes a three-axis accelerometer and a gyroscope.
4. The data segmentation method for a fall protection system as claimed in claim 1, wherein the training data set is trained based on the multi-channel convolutional neural network model MC-CNN in step S3, and the training process is as follows:
firstly, signals of a three-axis accelerometer and a gyroscope are independently processed, the influence of the signals of the three-axis accelerometer and the gyroscope on classification is comprehensively considered after convolution operation, then feature maps of two parts are combined to obtain an area which is important for classification, and finally, the classification quality is judged by using a cross entropy loss function to finish the training of a data set;
in the above process, the Convolution operation is composed of four one-dimensional Convolution 1D Convolution and global average pooling layers GAP, a linear full-link layer FC, and a softmax logistic regression layer, Convolution kernels of the four one-dimensional Convolution 1D Convolution are respectively 8, 16, 32, and 64, and the global average pooling layer GAP reduces a feature mapping dimension of each channel of the fourth Convolution layer from (1 × 100) to (1 × 1).
5. A data segmentation method for a fall protection system as claimed in claim 1, wherein the importance of each sequence position for data segmentation is obtained by combining the importance mapping method in step S4, and the specific process is as follows:
after the MC-CNN model is trained, feature maps and corresponding weights of all channels can be obtained, and S is used for an input inertial sensor time sequencek(x) Representing the output sequence on channel k, x represents the time position on the sequence,
Figure FDA0003513456870000021
representing the weight of each channel feature k to a different class c, the input to the softmax logistic regression layer is denoted gcThen, there are:
Figure FDA0003513456870000022
from this, an importance mapping is built from the sequences to each class C, defined as Mc；
Figure FDA0003513456870000023
Mc(x) Indicating the importance of position x in the time series for classifying the sequence as c.
6. A data segmentation method for a fall protection system as claimed in claim 1, wherein step S4 is implemented for Mc(x) After the visualization, specific areas of data segmentation are determined by summarizing the rules of the importance mapping maps of the areas, and data segments corresponding to the areas are used for behavior classification based on a machine learning model so as to verify whether the data segments are falling actions.
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