Method, system, device and storage medium for detecting abnormal behavior patterns of elderly people at home

Abstract

The present invention provides a method, system, device and medium for detecting abnormal behavior patterns of elderly people at home, which belongs to the field of home care. The method comprises the following steps: obtaining a behavior sequence set #imgabs0# of the elderly at home for the past m days, and obtaining the i-th behavior b in the past m days from #imgabs1# according to the behavior category. i Set B i , define behavior b i The time distance formula between time points x and y; extract set B i Behavior b i The time regularity and frequency regularity of the regular behavior are determined according to the time regularity and frequency regularity, and the set of all regular behaviors B is obtained. reg , the behavior sequence B that occurs on day i i The method converts the regular behavior feature string into the feature string set #imgabs2# and uses the unsupervised learning clustering algorithm to identify abnormal behavior patterns in the feature string set #imgabs3#. The method can accurately extract abnormal behavior patterns.

Description

Technical Field

[0001] The present invention belongs to the field of home care, and in particular relates to a method, system, device and storage medium for detecting abnormal behavior patterns of elderly people at home. Background Art

[0002] The increasingly severe aging population presents increasing challenges to the healthcare industry, such as rising healthcare costs and insufficient caregivers, forcing the search for solutions to address these challenges. The development of wireless sensor networks and the Internet of Things (IoT) is driving smart healthcare for the elderly from hospitals and clinics to the home. At the same time, seniors prefer to live in their own homes. Consequently, home care has become an active research area, encompassing a variety of technologies, including monitoring systems, behavior recognition, and violation detection. Monitoring systems aim to record residents' activities through video, wearable devices, or wireless sensor networks. Wireless sensor networks have attracted significant attention due to their privacy and user-friendliness, which require no equipment on the part of the elderly. Furthermore, numerous behavior recognition algorithms have been proposed, identifying behaviors by analyzing the relationship between behavior and sensor data. Irregular behavior can indicate unsafe conditions, such as changes in the elderly's health, deteriorating mood, or instability. Therefore, irregularity detection can effectively improve the quality of life for elderly people living alone.

[0003] Irregularity detection can be categorized into two main types: explicit and implicit. Explicit irregularity detection aims to identify irregular behaviors in real time and requires appropriate caregivers to immediately address abnormal behaviors, such as accelerated heart rate and fall detection. Typically, some clear irregularity features are known, so specific sensors can be deployed to capture these known features to identify irregularities. However, the features of implicit irregularities are uncertain, making detection difficult. For example, discomfort is a type of implicit irregularity that can cause changes in the timing or sequence of certain behaviors, such as sleeping and eating, but may not cause drastic changes in sensor values. In this case, in-depth analysis of behaviors or behavioral patterns is required to identify irregularities and alert caregivers.

[0004] Because implicit irregularities are difficult to characterize, some existing algorithms only consider the irregularity of individual behaviors, without addressing the irregularity of combinations of behaviors. Some studies have also used daily behavior as a fundamental analysis element, considering the varying impacts of different behaviors on implicit irregularity detection. However, the extraction of regular behavior patterns in these studies is crude and imprecise, resulting in inaccurate results for implicit irregular behavior detection. Summary of the Invention

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method for detecting abnormal behavior patterns of elderly people at home, comprising the following steps:

[0006] Get the behavior sequence set of the elderly at home in the past m days According to the behavior category, from the behavior sequence set Get the i-th behavior b in the past m days i Set B i ;

[0007] Define behavior b i The time distance formula between time point x and time point y is combined with the time distance formula to obtain set B i Behavior b i Time regularity and frequency regularity of judgment set B i Whether the start time and end time of each behavior in satisfies the time regularity, and whether the occurrence frequency of a behavior satisfies the frequency regularity, if so, it is determined to be a regular behavior;

[0008] Obtained by the determination method of regular behavior All regular behaviors in the set constitute the regular behavior set B reg ; B reg The behavior sequence B that occurs on day i i Each regular behavior in is converted into a feature string, and a feature string set is obtained. Use unsupervised learning clustering algorithm to cluster feature strings Perform classification and identify abnormal behavior patterns based on the classification results.

[0009] Preferably, the unsupervised learning clustering algorithm is a density clustering algorithm DBSCAN, which is used to cluster the feature string set Classify and identify abnormal behavior patterns based on the classification results, specifically: Clustering is performed, regular behavior patterns are extracted based on the clustering results, and feature strings with noise are determined to be abnormal behavior patterns.

[0010] Preferably, the time regularity includes the regularity of occurrence time and the regularity of end time, and the judgment set B i Whether the start time and end time of a behavior in satisfies the time regularity must satisfy both the occurrence time regularity and the end time regularity.

[0011] Preferably, the time distance formula is:

[0012]

[0013] in, Represents set B i The jth element in Represents set B i The lth element in AD(x, y) represents a function that ignores the date and calculates the absolute difference between time points x and y. express The start time of the occurrence, express The start time of the occurrence.

[0014] Preferably, the time regularity is:

[0015]

[0016] Where, Indicates regular behavior The mean square error of the start time, Indicates regular behavior The mean square error of the end time, and Regularity thresholds representing the start and end times of regular behavior

[0017] Preferably, the frequency regularity is:

[0018]

[0019] In the formula, regular behavior The mean square error of the frequency of occurrence, Represents the frequency regularity threshold.

[0020] Preferably, the density clustering algorithm DBSCAN is used to obtain the set B i Behavior b i The temporal regularity of i frequency regularity.

[0021] The present invention also provides a system for detecting abnormal behavior patterns of elderly people at home, comprising:

[0022] The behavior sequence acquisition module is used to obtain the behavior sequence set of the elderly at home in the past m days According to the behavior category, from the behavior sequence set Get the i-th behavior b in the past m days i Set B i ;

[0023] Regular behavior determination module, used to define behavior b i The time distance formula between time point x and time point u is combined with the time distance formula to obtain set B i Behavior b i Time regularity and frequency regularity of judgment set B iWhether the start time and end time of each behavior in satisfies the time regularity, and whether the occurrence frequency of a behavior satisfies the frequency regularity, if so, it is determined to be a regular behavior;

[0024] Behavior pattern anomaly detection module, used to obtain the abnormal behavior based on the judgment method of regular behavior All regular behaviors in the set constitute the regular behavior set B reg ; B reg The behavior sequence B that occurs on day i i Each regular behavior in is converted into a feature string, and a feature string set is obtained. Use unsupervised learning clustering algorithm to cluster feature strings Perform classification and identify abnormal behavior patterns based on the classification results.

[0025] The present invention also provides a computer device, including a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the method for detecting abnormal behavior patterns of elderly people at home.

[0026] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is suitable for loading by a processor to execute the method for detecting abnormal behavior patterns of elderly people at home.

[0027] The method, system, device, and storage medium for detecting abnormal behavior patterns of elderly people at home provided by the present invention have the following beneficial effects:

[0028] The present invention obtains a set of behaviors over the past few days and determines regular behaviors in the set of behaviors by extracting the temporal regularity and frequency regularity from the set of behaviors. This process takes the behaviors of multiple days as analysis factors and considers the different effects of different behaviors on the detection of implicit irregularities. It transforms the idea of ​​implicit irregularity detection into the extraction of features of regular behavior patterns, making the detection results more refined. By adopting an unsupervised clustering algorithm to perform multi-category clustering on daily behavior patterns, it can facilitate a more detailed analysis of the behavior patterns, thereby accurately extracting abnormal behavior patterns. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0030] Figure 1 This is a flow chart of a method for detecting abnormal behavior patterns of elderly people at home according to an embodiment of the present invention;

[0031] Figure 2 Extracting flow charts for regularity of daily behavior sequences;

[0032] Figure 3 A method for constructing feature representations of daily behavior sequences;

[0033] Figure 4 The relationship between regular behavior patterns, feature representations and regular behaviors. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0035] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the technical solutions of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0036] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meaning of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0037] Example

[0038] The present invention provides a method for detecting abnormal behavior patterns of elderly people at home, specifically Figure 1 As shown, the following steps are included:

[0039] Step 1: Obtain the behavior sequence set of the elderly at home in the past m days According to the behavior category, from the behavior sequence set Get the behavior b of the past m days i Set B i ; define behavior b i The time distance formula between time point x and time point y is used to obtain set B using the density clustering algorithm DBSCAN in combination with the time distance formula. i Behavior b i The temporal regularity of i Frequency regularity of; judgment set B i Whether the start time and end time of a certain behavior in the algorithm satisfies the time regularity, and whether the occurrence frequency of a certain behavior satisfies the frequency regularity, if so, it is determined to be a regular behavior.

[0040] Assume that a daily behavior monitoring system is installed in an apartment of an elderly person living alone, and the sensors of the monitoring system can collect the elderly person’s behavior. In this paper, the behavior set of interest is recorded as B = {b1, b2, ..., b |B|}, the behavior sequence occurring on day i is recorded as Each behavior b i，j ∈B i The specific information can be expressed as (name, start_time, end_time), where name∈B represents the name of the behavior, start_time and end_time represent b respectively. i，j The start time and end time of the occurrence, the behavior sequence set collected in the past m days is recorded as

[0041] Assuming that the elderly have regular behavior patterns, they can be further divided into unrelated categories of behavior patterns. For example, the elderly may have a regular behavior pattern on weekdays, such as getting up around 5 o'clock, eating breakfast, lunch and dinner at 6 o'clock, 12 o'clock and 18 o'clock respectively, and going to bed around 20 o'clock. However, the elderly may also have other types of regular behavior patterns, such as taking the children out for dinner on Saturdays and going to the market every Wednesday. Represents a regular behavior pattern, which contains some regular behavior sequences. represents a set of q behavior patterns, which can be obtained from the behavior sequence set If the behavior sequence on a certain day does not belong to any behavior pattern, it can be considered that the behavior on that day is abnormal.

[0042] The regularity of daily behavior sequences is composed of regular behaviors. Therefore, the following will define regular behaviors and then explain the extraction of regularities in detail.

[0043] (1) Definition of regular behavior

[0044] A behavior is regular if it occurs regularly at a certain time of day and over a certain number of days.

[0045] make Represents the i-th regular behavior, which has 8 attributes, namely:

[0046] 1) id indicates regular behavior A unique identifier for a . For example and

[0047] 2) Name indicates regular behavior The name of It should be noted that different regular behaviors may have the same behavior name but different ids.

[0048] 3) Indicates regular behavior The statistical mean of the start time.

[0049] 4) Indicates regular behavior The mean squared deviation of the start times.

[0050] 5) Indicates regular behavior The statistical average of the end time.

[0051] 6) Indicates regular behavior The mean square error of the end times.

[0052] 7) Indicates regular behavior The statistical mean of the frequency of occurrence.

[0053] 8) Indicates regular behavior The mean square error of the frequencies of occurrence.

[0054] Regular behavior Will regularly Starting near End near. Let μ represent the time unit, for example, μ is set to 1 day. Then, if the regular behavior Occurs regularly every day times, then its frequency of occurrence is For example, in every unit time μ, the behavior of "eating lunch" will occur once, so the frequency of this behavior is 1. If the elderly go to the vegetable market once every Wednesday, then the frequency of "going to the vegetable market" is 1 / 7. Based on the definition of regular behavior, the following will detail how to identify regular behavior.

[0055] (2) Identification of regular behaviors

[0056] The present invention will identify regular behaviors through the constraints of time regularity and frequency regularity. These constraints are mainly composed of The following describes the regularity recognition process, including time regularity constraint checking and frequency regularity constraint checking. Figure 2 shown.

[0057] The first step is to find a behavior that satisfies the time regularity constraint. The time regularity constraint ensures a regular behavior Regularly starts around a certain time and regularly ends around a certain time. Indicates that the behavior in the past m days is b i For example, b j，k .name = 'eating lunch' and m = 365, then all "eating lunch" in the past year will be put into set B i .make For set B i The jth element in For set B i The lth element in Expressing behavior and behavior The distance from the start time, then the distance It can be calculated by the following formula:

[0058]

[0059] AD(x, y) is a function that ignores dates and calculates the absolute difference between time points x and y. For example, if x = '2022-01-02 08:00' and y = '2022-01-12 08:30', AD(x, y) will return 30 minutes.

[0060] With the distance calculation formula, we can use the DBSCAN clustering algorithm to extract set B i Behavior b i After DBSCAN clustering, each behavior instance Will be clustered into a group G with temporal regularity TR or is considered as noise. Indicates all B i G obtained after DBSCAN clustering TR Collection, that is,

[0061]

[0062] gather The number of elements in is affected by the DBSCAN parameters. DBSCAN has two parameters, min_pts and Eps, where min_pts represents the minimum number of points in a cluster and Eps represents the minimum distance between two data points in the same cluster. These two parameters will be discussed in the performance analysis below.

[0063] make Representing a temporal regularity group The corresponding behavior name, then the group All behavior instances in a have the same behavior name And they all start at similar time points. In other words, the behavior With time regularity. Then the behavior They can be calculated according to the following formulas.

[0064]

[0065]

[0066]

[0067]

[0068] Assume that, in the past m days, the behavior The start time and end time of the behavior obey the normal distribution, then the confidence interval of the start time and end time of the behavior is defined as and The size of the variance indicates the degree of discreteness of the data, that is, the degree of regularity of the behavior. and Represents the start time and end time regularity thresholds respectively. If the following formula is satisfied, the behavior is considered to have ended with temporal regularity.

[0069]

[0070] make Represents a set of behaviors that satisfy temporal regularity constraints.

[0071] Next, the second step is to check the behavior Whether the frequency regularity is satisfied. The present invention uses Fourier transform to find the periodicity of the behavior. Let F s Indicates the sampling frequency, which is the behavior Construct a time series That is, time series F is collected in a unit time μ s For example, assuming the time unit μ is set to one day, F s Set to 24*2. This means that if the behavior Every half hour, if it happens, check it. The result of the check will be 1 or 0, indicating that it happened or did not happen respectively. There are 24*2 elements in a day, and There are 24*2*m elements in the past m days.

[0072] So, The nonparametric estimation of the power spectral density P i (f) can be calculated by the following formula.

[0073]

[0074] P i (f) is a function of frequency f, where Let P trh represents the periodic threshold, let Behavior The frequency set of Attributes They can be calculated by the following formulas:

[0075]

[0076]

[0077] Similarly, let Indicates the regularity threshold of frequency, behavior If the following equation is satisfied, then the behavior satisfies the frequency regularity constraint.

[0078]

[0079] If a behavior satisfies both the time regularity and frequency regularity constraints, it is called a regular behavior. Represents the obtained regular behavior-set. Among them, B reg The regularity extracted from daily behavior sequences. Figure 1 As shown, the behavioral dataset collected in the past m days Described as input. In the regularity recognition layer, in the set Partition according to the behavior name and perform DBSCAN clustering according to the start time to obtain temporal regularity clusters Then perform time regularity constraint check and frequency regularity constraint check, and output regular behavior set B reg The next stage will represent daily behavior sequences based on the extracted regularities.

[0080] Step 2: Obtain based on the determination method of regular behavior The set B of all regular behaviors in reg ; Set B reg The behavior sequence B that occurs on day i i Each regular behavior in is converted into a feature string, and a feature string set is obtained.

[0081] Use B reg The regular behaviors extracted from the That is, keep the regular behaviors in the daily behavior sequence and remove the irregular behaviors. represents the sequence of behaviors identified on day i. Each b i，j ∈B i Will be checked to see if it is a regular behavior. If so, b i，j will be retained and represented by the corresponding regular behavior. Otherwise, b i，j will be deleted. Therefore, each daily behavior sequence can be represented by regular behaviors extracted from the previous stage. is an example of regular behavior b i，j. The following is the characteristic representation of the regular behavior b i，j Instance feature representation The generation method of .

[0082] Let the Boolean variable η i，j，k Indicates behavior b i，j ∈B i Is it regular behavior? An example of

[0083]

[0084] It is possible that multiple regular behaviors have the same name. For example, the "TakeMedicine" behavior may appear regularly at around 8 o'clock, 13 o'clock, and 19 o'clock. That is, three regular behaviors with the same name but different time patterns are extracted from the regular extraction behavior sequence. Therefore, behavior b i，j The mapping to regular behavior is not clear. The next step is to resolve this ambiguity. First, define the behavior instance b i，j and regular behavior distance.

[0085]

[0086] Then find b according to the following formula i，j The best feature representation breg .id.

[0087]

[0088] Each behavior instance b i，j ∈B i After the above processing, we can get the behavior sequence B i feature representation.

[0089] make Represents behavior sequence B i Then It is composed only of regular behaviors, which is a regular behavior sequence. There is a frequency of occurrence, which is determined by the regular behavior The minimum number of occurrences of an instance is determined. express The frequency of occurrence is due to Moderate regular behavior The frequency is So Frequency of occurrence It can be calculated by the following formula.

[0090]

[0091] Figure 3 A method for constructing feature representation of daily behavior sequences is given. Figure 3 As shown, the daily behavior sequence Contains multiple behavior instances, such as Figure 3 At the same time, Figure 3 The set B in the right half of reg Contains 6 regular behaviors. Regular behaviors The frequency is equal to 1, which means that the behavior 'GetUp' occurs once a day, and The frequency is equal to 1 / 3, which means that the behavior 'G0toMarket' occurs once every 3 days. According to the following formula, the characteristic representation of each behavior in B1 can be obtained. Figure 3 As shown, behavior instance b 1，1 The feature representation of ∈B1 is the character 'a' because it maps the regular behavior For 'a'. Example b 1，2 ∈B1 has no feature representation because b 1，2 .name does not appear in regular behavior set B reg Similarly, instance b 1，3 The feature of . is represented by the character 'b'. In addition, three regular behaviors, namely and With instance b 1，4have the same behavior name, then based on the distance calculation, instance b 1，4 The characteristic is represented by 'e', ​​such as Figure 3 As shown, we can finally get Further, we can calculate the characteristic string according to the above formula Frequency is 1 / 3.

[0092] In each behavioral sequence After conversion to feature representation, the feature string set can be obtained In the next stage, the feature string set Unsupervised learning clustering algorithms are applied to discover daily behavior patterns and identify abnormal behavior sequences.

[0093] Step 3: Use unsupervised learning clustering algorithm to identify the characteristic string set Abnormal behavior patterns in .

[0094] For the feature string set The goal of this step is to discover some regular behavior patterns and identify abnormal behavior sequences. Specifically, each feature string It is a behavior pattern. Each characteristic string Each action is checked to determine whether it belongs to a regular behavior pattern or an abnormal behavior sequence. To achieve this goal, this phase includes three main tasks: weight assignment, distance calculation, and feature representation clustering. In the weight assignment task, each action is assigned a weight to emphasize the importance of its regularity. Then, the distance calculation task aims to define and calculate the distance between any two action strings. Finally, in the feature representation clustering task, the DBSCAN processing learning algorithm is applied to discover regular patterns and identify abnormal behavior patterns. The detailed approach for each task is given below.

[0095] (1) Weight distribution

[0096] In calculating string and Before calculating the distance between them, each regular behavior should be associated with a weight to emphasize its importance to the regularity. The weight of regular behavior will be based on the time regularity and frequency regularity. Indicates regular behavior The weight of the regular behavior has been defined before The length of the confidence interval for the start time is Its value indicates the regularity of the behavior. If the value of is small, the temporal regularity is strong. On the contrary, if The larger the value of , the weaker the temporal regularity. Similarly, The value of indicates regular behavior The degree of frequency regularity, A larger value indicates a behavior The frequency regularity is strong, on the contrary, The smaller the value of is, the weaker the frequency regularity of the behavior is. The regularity of behavior can be determined by both time regularity and frequency regularity. of It can be calculated according to the following formula.

[0097]

[0098] (2) Distance calculation

[0099] make Indicates a characteristic string and The distance between them. Figure 3 As shown, The feature is represented as a feature string, so It can also be defined as a string from the characteristics Transformed into Here, basic character operations include insertion, deletion, or modification.

[0100] Assume the signature string When performing the kth basic operation, the operation rule behavior is Then the cost of the kth basic operation is recorded as Its value is equal to As shown in the following formula.

[0101]

[0102] Assumed signature string After K basic character operations, the characteristic string can be obtained from Convert to The cumulative operation cost on Its value can be calculated according to the following formula.

[0103]

[0104] In fact, the characteristic string After K basic character operations, the characteristic string can be obtained There are many combinations of operations. Therefore, the distance It is defined as the minimum cost of a combination of multiple operations and can be calculated according to the following formula.

[0105]

[0106] (3) Feature Representation Clustering

[0107] Finally, apply DBSCAN to the set Clustering is performed to discover regular behavior patterns and detect abnormal behavior sequences. The two parameters of DBSCAN, namely and Need to be determined. Parameters Indicates the minimum number of feature strings in a category, and It is expressed as the minimum distance between two feature strings in the same category. Represents the result of clustering by DBSCAN, the elements in the set Represents a category, each category Represents a regular behavior pattern, consisting of some similar feature strings, that is,

[0108] make Represents a set of regular behaviors B reg The minimum frequency of occurrence of regular behavior in the algorithm is given below.

[0109] Criteria for the minimum number of elements in a regular pattern category:

[0110] DBSCAN parameters The following should be satisfied:

[0111]

[0112] Here, m represents the number of days of observed data.

[0113] The following will further explain the above formula and derive Calculation of regular behavior patterns. The frequency of occurrence is Its value will be represented by the feature corresponding to the pattern for each element in the set Decision. Feature string The frequency of the regular behavior that occurs on day i Decision. Regular behavior The frequency of Similarly, the behavior pattern Frequency Defined as a pattern The minimum occurrence frequency of the Chinese character string is calculated according to the following formula.

[0114]

[0115] From the above formula, we can see that the frequency Composed of All possible regular behaviors The minimum frequency is determined by Figure 4 The relationship between regular behavior patterns, feature representations, and regular behavior is shown.

[0116] make Indicates B reg The regular behavior with the minimum frequency in , then:

[0117]

[0118]

[0119] So, Will not be greater than The frequency of any behavior pattern For any regular behavior pattern will satisfy The minimum number of strings in a pattern set in the past m days can be expressed as Taking into account Standard deviation The minimum number of elements in a regular behavior pattern set is:

[0120]

[0121] After DBSCAN clustering, each feature string Will be classified into a regular behavior pattern Finally, we can get a set of regular behavior patterns. Abnormal behavior sequences can also be detected.

[0122] The present invention also provides a system for detecting abnormal behavior patterns of elderly people at home, including a behavior sequence acquisition module, a regular behavior determination module, and a behavior pattern abnormality detection module. The behavior sequence acquisition module is used to obtain the behavior sequence set of elderly people at home in the past m days. According to the behavior category, from the behavior sequence set Get the i-th behavior b in the past m days i Set B i ; Regular behavior determination module is used to define behavior b i The time distance formula between time point x and time point u is combined with the time distance formula to obtain set B i Behavior b i Time regularity and frequency regularity of judgment set B iWhether the start time and end time of each behavior in the model satisfies the time regularity, and whether the occurrence frequency of a behavior satisfies the frequency regularity, if so, it is determined to be a regular behavior; the behavior pattern anomaly detection module is used to obtain the regular behavior according to the determination method of the regular behavior. All regular behaviors in the set constitute the regular behavior set B reg ; B reg The behavior sequence B that occurs on day i i Each regular behavior in is converted into a feature string, and a feature string set is obtained. Use unsupervised learning clustering algorithm to cluster feature strings Perform classification and identify abnormal behavior patterns based on the classification results.

[0123] The present invention also provides a computer device, including a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute a method for detecting abnormal behavior patterns of elderly people at home.

[0124] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is suitable for loading by a processor to execute a method for detecting abnormal behavior patterns of elderly people at home.

[0125] The above-described embodiments are only preferred specific implementation methods of the present invention, and the protection scope of the present invention is not limited thereto. Any simple changes or equivalent replacements of the technical solutions that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention fall within the protection scope of the present invention.

Claims

1. A method for detecting abnormal behavior patterns of elderly people at home, characterized in that: The steps include: Get the behavior sequence set of the elderly at home in the past m days According to the behavior category, from the behavior sequence set Get the i-th behavior b in the past m days i Set B i ; Define behavior b i The time distance formula between time point x and time point y is combined with the time distance formula to obtain set B i Behavior b i Time regularity and frequency regularity of judgment set B i Whether the start time and end time of each behavior in satisfies the time regularity, and whether the occurrence frequency of a behavior satisfies the frequency regularity, if so, it is determined to be a regular behavior; According to the determination method of regular behavior, all regular behaviors in B are obtained to form the regular behavior set B reg ; B reg The behavior sequence B that occurs on day i i Each regular behavior in is converted into a feature string, and the feature string set B is obtained. reg ; Use unsupervised learning clustering algorithm to cluster the feature string set B reg Perform classification and identify abnormal behavior patterns based on the classification results; The unsupervised learning clustering algorithm is the density clustering algorithm DBSCAN, which is used to cluster the feature string set B. reg Classify and identify abnormal behavior patterns based on the classification results, specifically: reg Perform clustering, extract regular behavior patterns based on the clustering results, and identify feature strings with noise as abnormal behavior patterns; Among them, the unsupervised learning clustering algorithm is used to cluster the feature string set B reg The specific process of classifying and identifying abnormal behavior patterns based on the classification results is: weight assignment, distance calculation, and feature representation clustering. The distance calculation formula is as follows: Where, is a feature string, is the characteristic string obtained after K basic character operations, is the k-th basic character operation cost, for The characteristic string obtained after K basic character operations The cumulative operating costs, for and distance.

2. The method for detecting abnormal behavior patterns of elderly people at home according to claim 1, characterized in that: The time regularity includes the regularity of occurrence time and the regularity of end time. i Whether the start time and end time of a behavior in satisfies the time regularity must satisfy both the occurrence time regularity and the end time regularity.

3. The method for detecting abnormal behavior patterns of elderly people at home according to claim 1, characterized in that: The time distance formula is: in, Represents set B i The jth element in Represents set B i The lth element in AD(x,y) represents a function that ignores the date and calculates the absolute difference between time points x and y. express The start time of the occurrence, express The start time of the occurrence.

4. The method for detecting abnormal behavior patterns of elderly people at home according to claim 1, characterized in that: The temporal regularity is: Where, Indicates regular behavior The mean square error of the start time, Indicates regular behavior The mean square error of the end time, and The regularity thresholds represent the start and end times of regular behavior, respectively.

5. The method for detecting abnormal behavior patterns of elderly people at home according to claim 1, characterized in that: The frequency regularity is: Where, Indicates regular behavior The mean square error of the frequency of occurrence, Represents the frequency regularity threshold.

6. The method for detecting abnormal behavior patterns of elderly people at home according to claim 1, characterized in that: Use density clustering algorithm DBSCAN to obtain set B i Behavior b i The temporal regularity of i frequency regularity.

7. A system for detecting abnormal behavior patterns of elderly people at home, characterized in that: include: The behavior sequence acquisition module is used to obtain the behavior sequence set of the elderly at home in the past m days According to the behavior category, from the behavior sequence set Get the i-th behavior b in the past m days i Set B i ; Regular behavior determination module, used to define behavior b i The time distance formula between time point x and time point y is combined with the time distance formula to obtain set B i Behavior b i Time regularity and frequency regularity of judgment set B i Whether the start time and end time of each behavior in satisfies the time regularity, and whether the occurrence frequency of a behavior satisfies the frequency regularity, if so, it is determined to be a regular behavior; Behavior pattern anomaly detection module, used to obtain the abnormal behavior based on the judgment method of regular behavior All regular behaviors in the set constitute the regular behavior set B reg ; B reg The behavior sequence B that occurs on day i i Each regular behavior in is converted into a feature string, and a feature string set is obtained. Use unsupervised learning clustering algorithm to cluster feature strings Perform classification and identify abnormal behavior patterns based on the classification results; The unsupervised learning clustering algorithm is the density clustering algorithm DBSCAN, which is used to cluster the feature string set B. reg Classify and identify abnormal behavior patterns based on the classification results, specifically: reg Perform clustering, extract regular behavior patterns based on the clustering results, and identify feature strings with noise as abnormal behavior patterns; Among them, the unsupervised learning clustering algorithm is used to cluster the feature string set B reg The specific process of classifying and identifying abnormal behavior patterns based on the classification results is: weight assignment, distance calculation, and feature representation clustering. The distance calculation formula is as follows: Where, is a feature string, is the characteristic string obtained after K basic character operations, is the cost of the k-th basic character operation, for The characteristic string obtained after K basic character operations The cumulative operating costs, for and distance.

8. A computer device, characterized in that: It comprises a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the method for detecting abnormal behavior patterns of elderly people at home as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor to execute the method for detecting abnormal behavior patterns of elderly people at home as described in any one of claims 1 to 6.

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