CN114595748B - Data segmentation method for fall protection system - Google Patents
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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 the data of an inertial sensor worn by a user before the user falls to strike the ground, and constructing a training data set; training a training data set based on a multichannel convolutional neural network model MC-CNN to obtain feature maps of all channels and corresponding weights; and combining the obtained feature map with the corresponding weight and 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 further being used for fall detection of a fall protection system. The method can clearly divide the triggering condition of fall detection and the intercepting length of the window, avoids the defect that the length of the data dividing window is determined empirically by the existing dividing method, and solves the problem that the fall protection system is difficult to divide the data.
Description
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
Worldwide, elderly falls are a major public health problem. Falls are the main cause of death, disability and disability of elderly people over 65 years of age, and also of hospitalization (one elderly person is hospitalized for falls every 11 seconds). Therefore, automatic fall protection systems are very interesting.
The main purpose of the fall protection system is to accurately detect when a fall has occurred, so that the fall protection device is activated before the body hits the ground, to reduce the fall injury.
In general, fall protection systems are more suitable for use with wearable systems based on inertial sensors. The core of the fall protection system is a detection algorithm, which is responsible for constantly classifying any movement of the human body as a fall or a daily life movement. Detection methods based on artificial intelligence have been widely validated for higher accuracy than threshold-based methods.
Since for fall protection systems based on artificial intelligence models, 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 sensor into a plurality of data segments. There are two general data segmentation methods, the first being a sliding window of fixed duration. The sliding window duration defines the boundaries of the data segment, and features and classifications are further extracted after interception. The method is simple, but has large calculation amount, and is not suitable for a fall protection system with high real-time requirements. The second method first sets a trigger condition, detects potential fall events in the input data stream by searching for inertial sensor values that exceed a preset threshold. When a potential falling event is detected, the data segments are intercepted through one or more windows, and classification is achieved. However, how the window duration is determined is not an explicit method, most of which are experimental empirical values. If the window duration is too long, the real-time performance of fall detection can be affected, and the protection device is not opened for enough time; if the window duration is too short, the accuracy of fall detection will be affected.
Disclosure of Invention
To solve the above-mentioned drawbacks and disadvantages of the prior art, an object of the present invention is to provide a method for inertial sensor data segmentation. The data segmentation method is realized through a multichannel convolutional neural network MC-CNN and an importance mapping method. Firstly preprocessing the existing data set, intercepting inertial sensor data before a human body impacts the ground, constructing a training data set, secondly training through an MC-CNN network, extracting model parameters, determining the importance of each sequence position for classification by combining an importance mapping method, and thus determining the clear range of data segmentation.
In order to achieve the above 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 behavior actions of a user;
s2, preprocessing the acquired data set, intercepting the data of an inertial sensor worn by a user before the user falls down to strike the ground, and constructing a training data set;
s3, training a training data set based on a multichannel convolutional neural network model MC-CNN to obtain feature maps of all channels and corresponding weights;
s4, combining the obtained feature map with the corresponding weight, and obtaining the importance of each sequence position for data segmentation by an importance mapping method, so as to determine a specific region of the data segmentation;
s5, setting a corresponding data segmentation algorithm in the fall protection system according to the determined data segmentation area, and using the data segmentation algorithm for fall detection of the fall protection system.
The preprocessing of the acquired data set in step S2 is as follows:
s21, calculating signal amplitude vectors of triaxial inertial sensorWherein (1)> And->Respectively representing the values of the inertial sensors of x, y and z;
s22, constructing a data set: for fall motion, the SMV peak represents the moment when the body hits the ground, so the signal 1s before the maximum peak is intercepted as the training dataset; for daily behavior actions such as slow walking, running, ascending stairs, descending stairs and the like, data are intercepted at time intervals of 1s and used as a training data set.
The inertial sensor in step S2 includes a tri-axial accelerometer and a gyroscope.
In the step S3, training is performed on the training data set based on the multi-channel convolutional neural network model MC-CNN, and the training process is as follows:
firstly, processing triaxial accelerometer and gyroscope signals independently, comprehensively considering the influence of the triaxial accelerometer and gyroscope signals on classification after convolution operation, merging feature maps of the two parts to obtain a region with important bisection class, and finally judging classification quality by using a cross entropy loss function to finish training a data set;
in the above process, the Convolution operation is composed of four one-dimensional Convolution 1D Convolitions and a global average pooling layer GAP, a linear full-connection layer FC and a softmax logistic regression layer, the Convolution kernels of the four one-dimensional Convolution 1D Convolitions are respectively 8, 16, 32 and 64, and the global average pooling layer GAP reduces the feature mapping dimension of each channel of the fourth Convolution layer from (1×100) to (1×1).
The importance of each sequence position for data segmentation is obtained by combining the importance mapping method in the step S4, and the specific process is as follows:
after training through MC-CNN model, all can be obtainedFeature map of channel and corresponding weight, for input inertial sensor time series, in S k (x) Representing the output sequence on channel k, x represents the time position on the sequence,the input to the softmax logistic regression layer, representing the weight of each channel feature k to a different class c, is denoted g c The following steps are:
from this, an importance map from the sequence to each class C is established, defined as M c ；
M c (x) The importance of position x in the time series for classifying the series as c is shown.
In step S4, the implementation pair M c (x) After the visualization of (2), determining a specific region of the data segmentation by summarizing the law of the importance map thereof, and using the data segment corresponding to the region for behavior classification based on a machine learning model to verify whether the region is a falling action.
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 can clearly divide the triggering condition of falling detection and the intercepting length of a window, avoids the defect that the length of the window for data division is determined empirically by the existing dividing method, and solves the problem that the falling protection system is difficult to divide the data.
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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 that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a general flow diagram of a data segmentation method for a fall protection system according to the invention;
FIG. 2 is a diagram of fall profile analysis in an embodiment of the invention;
FIG. 3 is a schematic diagram of the composition of a multi-channel convolutional neural network in an embodiment of the present invention;
FIG. 4 is a sample map of importance mapping in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by one of ordinary skill in the art without inventive faculty, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
Examples: see fig. 1-4.
As shown in fig. 1, a 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 behavior actions of a user;
s2, preprocessing the acquired data set, intercepting the data of an inertial sensor worn by a user before the user falls down to strike the ground, and constructing a training data set;
s3, training a training data set based on a multichannel convolutional neural network model MC-CNN to obtain feature maps of all channels and corresponding weights;
s4, combining the obtained feature map with the corresponding weight, and obtaining the importance of each sequence position for data segmentation by an importance mapping method, so as to determine a specific region of the data segmentation;
s5, setting a corresponding data segmentation algorithm in the fall protection system according to the determined data segmentation area, and using the data segmentation algorithm for fall detection of the fall protection system.
The preprocessing of the acquired data set in step S2 is as follows:
s21, calculating signal amplitude vectors of triaxial inertial sensorWherein (1)> And->Respectively representing the values of the inertial sensors of x, y and z;
s22, constructing a data set: as shown in fig. 2, for a fall motion, the SMV peak represents the moment when the body hits the ground, so the signal 1s before the maximum peak is truncated as a training dataset; for daily behavior actions such as slow walking, running, ascending stairs, descending stairs and the like, data are intercepted at time intervals of 1s and used as a training data set.
The inertial sensor in step S2 includes a tri-axial accelerometer and a gyroscope.
In the step S3, training is performed on the training data set based on the multi-channel convolutional neural network model MC-CNN, and the training process is as follows:
as shown in fig. 3, firstly, the triaxial accelerometer and gyroscope signals are processed independently, the influence of the triaxial accelerometer and gyroscope signals on classification is comprehensively considered after convolution operation, then feature maps of the two parts are combined to obtain a region with important classification, and finally the classification quality is judged by using a cross entropy loss function so as to complete training of a data set;
in the above process, the Convolution operation is composed of four one-dimensional Convolution 1D Convolitions and a global average pooling layer GAP, a linear full-connection layer FC and a softmax logistic regression layer, the Convolution kernels of the four one-dimensional Convolution 1D Convolitions are respectively 8, 16, 32 and 64, and the global average pooling layer GAP reduces the feature mapping dimension of each channel of the fourth Convolution layer from (1×100) to (1×1); different from the existing CNN network, any pooling operation after the convolution layer is canceled in the invention, so that the feature map data length is ensured to be unchanged, and the subsequent determination of the segmentation area is facilitated.
The importance of each sequence position for data segmentation is obtained by combining the importance mapping method in the step S4, and the specific process is as follows:
after training through MC-CNN model, feature map and corresponding weight of all channels can be obtained, for the input inertial sensor time series, S is used for k (x) Representing the output sequence on channel k, x represents the time position on the sequence,the input to the softmax logistic regression layer, representing the weight of each channel feature k to a different class c, is denoted g c The following steps are:
from this, an importance map from the sequence to each class C is established, defined as M c ；
M c (x) Representing time sequenceThe importance of position x in the column to classify the sequence as c.
In step S4, the implementation pair M c (x) After the visualization of (2) as shown in fig. 4, a specific area of data segmentation is determined by summarizing the rule of the importance map, and the deeper the color bar in the map is, the more important the corresponding position sequence is for classification, in this embodiment, when the acceleration count value is smaller than 0.93 times of gravity acceleration, the potential fall is determined, and from this moment, the accelerometer and gyroscope data segments are intercepted according to the window length of 310ms for behavior classification based on the machine learning model, and finally whether the fall is verified.
It should be noted that, the application scope of the data segmentation method is not limited to fall detection, and can train and extract rules according to specific situations aiming at other types of actions in life, so as to obtain corresponding data segmentation positions.
The above description is only of 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 variation and modification of the above embodiments according to the technical substance of the present invention fall within the technical scope of the present invention.
Claims (4)
1. A data segmentation method for a fall protection system, comprising the steps of:
s1, acquiring a data set of falling actions and daily behavior actions of a user;
s2, preprocessing the acquired data set, intercepting the data of an inertial sensor worn by a user before the user falls down to strike the ground, and constructing a training data set;
s3, training a training data set based on a multichannel convolutional neural network model MC-CNN to obtain feature maps of all channels and corresponding weights;
s4, combining the obtained feature map with the corresponding weight, and obtaining the importance of each sequence position for data segmentation by an importance mapping method, so as to determine a specific region of the data segmentation;
after training through MC-CNN model, feature map and corresponding weight of all channels can be obtained, for the input inertial sensor time series, S is used for k (x) Representing the output sequence on channel k, x represents the time position on the sequence,the input to the softmax logistic regression layer, representing the weight of each channel feature k to a different class c, is denoted g c The following steps are:
from this, an importance map from the sequence to each class C is established, defined as M c ；
M c (x) Representing the importance of position x in the time series to classify the series as c;
in realizing the M c (x) After the visualization of the data segmentation, determining a specific region of the data segmentation by summarizing the rule of the importance map of the region, and using the data segment corresponding to the region for behavior classification based on a machine learning model so as to verify whether the region is a falling action or not;
s5, setting a corresponding data segmentation algorithm in the fall protection system according to the determined data segmentation area, and using the data segmentation algorithm for fall detection of the fall protection system.
2. A data segmentation method for fall protection systems according to claim 1, wherein the preprocessing of the acquired data set in step S2 is as follows:
s21, calculating signal amplitude vectors of triaxial inertial sensorWherein (1)>Andrespectively representing the values of the inertial sensors of x, y and z;
s22, constructing a data set: for fall motion, the SMV peak represents the moment when the body hits the ground, so the signal 1s before the maximum peak is intercepted as the training dataset; for daily behavior actions such as slow walking, running, ascending stairs, descending stairs and the like, data are intercepted at time intervals of 1s and used as a training data set.
3. A data segmentation method for a fall protection system as claimed in claim 1, wherein the steps of
The inertial sensor in S2 includes a tri-axial accelerometer and a gyroscope.
4. A data segmentation method for fall protection systems according to claim 1, wherein in step S3, the training data set is trained based on a multi-channel convolutional neural network model MC-CNN, and the training process is:
firstly, processing triaxial accelerometer and gyroscope signals independently, comprehensively considering the influence of the triaxial accelerometer and gyroscope signals on classification after convolution operation, merging feature maps of the two parts to obtain a region with important bisection class, and finally judging classification quality by using a cross entropy loss function to finish training a data set;
in the above process, the Convolution operation is composed of four one-dimensional Convolution 1D Convolitions and a global average pooling layer GAP, a linear full-connection layer FC and a softmax logistic regression layer, the Convolution kernels of the four one-dimensional Convolution 1D Convolitions are respectively 8, 16, 32 and 64, and the global average pooling layer GAP reduces the feature mapping dimension of each channel of the fourth Convolution layer from (1×100) to (1×1).
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