Intelligent data analysis method for pension information service system

Abstract

The invention provides an intelligent data analysis method for a pension information service system, relates to the technical field of physiological data processing, and aims to judge behavior stages according to the daily moving speed of the elderly and the continuity of the speed in time, select stable effective behavior stages through deterministic scores, and improve the accuracy of the behavior stages. Judging a corresponding behavior state according to the change characteristic of the heart rate in the effective behavior stage, identifying the body function expression of the old person in the corresponding state according to the change rate characteristic of the central rate of each behavior state, and judging the reaction intensity in the first behavior state and the flat intensity in the second behavior state; and a heart function change index is constructed by combining the long-term comprehensive expression of the reaction intensity, the flat intensity and the heart rate in the third behavior state, so that the overall body function change characteristics are reflected and quantified, and a guardian can conveniently know the overall body condition change of the old.

Description

Technical Field

[0001] This invention relates to the field of physiological data processing technology, specifically to a data intelligent analysis method for elderly care information service systems. Background Technology

[0002] In the field of elderly care, the most important issue is the physical condition of the elderly. The heart, as the most core state and indicator, often directly reflects the overall physical function level of the elderly. As people age, their physical function weakens significantly, and the burden on the heart becomes increasingly heavy. This is mainly manifested in the fact that during sleep, the heart rate increases to compensate for the reduced absorption of nutrients, thus pumping blood. During activity, the heart rate increases in a shorter time, while during rest, the heart rate takes longer to calm down. At the same time, the decline in cardiac function also increases the resting and exercise heart rate and slows down the recovery speed. Therefore, by analyzing the heart rate performance of the elderly over a long period of time, we can understand the overall changes in their physical function.

[0003] In the prior art, CN119073938A discloses a smart elderly care monitoring system that facilitates the monitoring of the vital signs of the elderly. The system includes three parts: a monitoring terminal, a data service, and a monitoring terminal. The monitoring terminal is used to monitor and collect the vital signs and exercise intensity information of the elderly and send it to the data service. The data service is used to process the information data collected by the monitoring terminal, generate an elderly monitoring index, and notify the optimal caregiver when the monitoring index is abnormal. The monitoring terminal is used to monitor the vital signs of visitors.

[0004] While the publicly available documents can enable real-time monitoring of the elderly's vital signs and guide corresponding monitoring services, they cannot detect long-term changes in the elderly's physical functions, nor can they make quantitative assessments of these changes.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a data intelligent analysis method for elderly care information service systems to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The data intelligence analysis method used in elderly care information service systems includes the following specific steps: Step 1: Real-time acquisition of the elderly’s heart rate and movement speed and add time tags. Align the heart rate and speed according to the time tags. Based on the speed’s performance in terms of time continuity, determine the elderly’s various behavioral stages throughout the day. Filter out the effective behavioral stages based on the number of speed points and the average speed within each behavioral stage. Step 2: When the speed is not 0 within the effective behavior phase, the effective behavior phase is determined to be the first behavior state. When all data within the effective behavior phase is 0, the effective behavior phase is determined to be the second or third behavior state based on the rising and falling trend of the heart rate within the effective behavior phase. Step 3: Identify the duration of heart rate elevation based on the heart rate change rate during the first behavioral state and label it as the heart rate response duration. Construct the response intensity using the average speed and heart rate response duration during the first behavioral state. Step 4: Identify the duration of heart rate decrease based on the rate of change of heart rate in the second behavioral state and mark it as the heart rate recovery duration. Construct the recovery intensity based on the amount of heart rate change and the heart rate recovery duration. Step 5: Obtain the average daily response intensity and average recovery intensity since the start of recording. At the same time, obtain the average heart rate of the third behavioral state each day to construct a cardiac function change index and set a change threshold. When the cardiac function change index exceeds the change threshold, remind the elderly to have a physical examination.

[0008] Furthermore, the heart rate of the elderly is acquired in real time, which is the number of heartbeats per minute. The heart rate is acquired once per minute, and a time label is added to each recorded heart rate.

[0009] Furthermore, the elderly person's speed is obtained, which is the average speed of their movement per minute, and is obtained once per minute. Each recorded speed is labeled with a time tag, which is aligned with the time tag of the heart rate. The daily speeds are sorted by time, and a fluctuation range and an abnormal threshold are set. Data points with speeds exceeding the abnormal threshold are marked as discontinuities. Behavioral stages are filtered from the time sorting, with the following logic: Using the speed at a certain point in time as the midpoint, examine the continuous speed points within the range above and below this midpoint. When a speed exceeds the range above and below this midpoint, it is marked as a discontinuity. The distance from the starting point to the discontinuity represents a continuous interval, and this continuous interval is designated as a behavior stage. The behavior stage requires at least three speed points within the continuous interval; otherwise, it is not recorded as a continuous interval or designated as a behavior stage. Record the number of speed points and the average speed within the behavior stage.

[0010] Furthermore, starting from the first time point, all behavioral stages in the velocity time series are acquired, and a deterministic score is generated for each behavioral stage, based on the following formula: in, Indicates the behavioral stage score. This indicates the number of data points within this stage. This indicates the first action within this phase. Data points, This indicates the data point retrieval variable within this stage. , , This represents the average speed within this behavior phase; The behavioral phase with the highest certainty score is selected as the effective behavioral phase, and each data point in this behavioral phase is marked as a discontinuity.

[0011] Furthermore, the acquisition of the behavioral phase, the formation of a deterministic score, and the selection of an effective behavioral phase are grouped into one round of operation, and then several rounds of operation are performed on the velocity sequence. Set a certainty score threshold. If the highest certainty score selected in the latest round is lower than the certainty score threshold, the operation will stop.

[0012] Furthermore, the effective behavioral phases selected from all rounds are summarized, and the behavioral state of each effective behavioral phase is determined based on the heart rate corresponding to the speed in the selected effective behavioral phase. The determination logic is as follows: The heart rate during the effective behavior phase is sorted in chronological order and divided into three equal parts, labeled as the first segment, middle segment and last segment respectively. The average heart rate of each segment is obtained, and a heart rate change threshold is set. If the speed is not 0 within the effective behavior phase, then the behavior state of the effective behavior phase is determined to be the first behavior state. If the speed is 0 throughout the effective behavior phase and the average heart rate in the first phase exceeds the sum of the average heart rate in the second phase and the heart rate change threshold, then the behavior state of the effective behavior phase is determined to be the second behavior state. If the speed is 0 throughout the effective behavior phase and the average heart rate in the first phase does not exceed the sum of the average heart rate in the second phase and the heart rate change threshold, then the behavior state of the effective behavior phase is determined to be the third behavior state.

[0013] Furthermore, the heart rate time series of the first behavioral state is obtained, the heart rate change rate at each time point is determined, and the time point when the heart rate change rate is 0 or when the heart rate change rate first turns from positive to negative is identified and marked as the reaction point. The duration from the start of the first behavioral state to the reaction point is the heart rate reaction time. The average velocity during the first behavioral state is obtained, and the reaction intensity is constructed based on the following formula: in, For the reaction strength, Indicates heart rate reaction time. This represents the average velocity during the first state. If the first behavioral state does not have a time point where the heart rate change rate is 0 or the heart rate change rate first turns from positive to negative, then the time point at the end of the first behavioral state is marked as the reaction point.

[0014] Furthermore, the heart rate time series of the second behavioral state is obtained, the heart rate variability rate at each time point is determined, and the time point when the heart rate variability rate is 0 or the first time the heart rate variability rate turns from negative to positive is identified and marked as the calming point. The heart rate calming duration is recorded as the time point between this time point and the start time of the second behavioral state. The heart rate at the start of the second behavioral state and the heart rate at the calming point are obtained respectively, and the calming intensity is constructed based on the following formula: in, Indicates the intensity of the smoothing effect. This indicates the heart rate at the start of the second behavioral state. This indicates the heart rate at the calming point. Indicates the duration of heart rate pacing; If the second behavioral state does not have a heart rate variability of 0 or a time point where the heart rate variability first turns from negative to positive, then the time point at the end of the second behavioral state is marked as the calming point.

[0015] Furthermore, the mean response intensity for all first behavioral states and the mean calming intensity for all second behavioral states were obtained daily. Simultaneously, the mean heart rate during all third behavioral states was obtained. The mean response intensity, mean calming intensity, and mean heart rate for all individual days since recording began were obtained and normalized to obtain cardiac function change indicators. The formula used is as follows: in, Indicators representing changes in cardiac function Indicates the first Average daily reaction intensity Indicates the first The average daily recovery intensity Indicates the first Average daily heart rate Indicates a single-day retrieval variable. , , This represents the total number of days since records began; Set a threshold for changes; when the changes in cardiac function indicators exceed the threshold, remind the elderly to undergo a physical examination; otherwise, do not remind them.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention determines behavioral stages based on the elderly person's daily movement speed and the continuity of that speed over time. It selects stable and effective behavioral stages through deterministic scoring, identifies the corresponding behavioral state based on the heart rate variation characteristics within each effective behavioral stage, and recognizes the elderly person's physical function performance in each behavioral state based on the heart rate variation characteristics. It judges the reaction intensity in the first behavioral state and the calming intensity in the second behavioral state, and constructs a cardiac function change index by combining the long-term reaction intensity, calming intensity, and heart rate in the third behavioral state. This reflects and quantifies the overall physical function changes, making it easier for caregivers to understand the changes in the elderly person's overall physical condition. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0019] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example: Please see Figure 1 The present invention provides a technical solution: The data intelligence analysis method used in elderly care information service systems includes the following specific steps: Step 1: Real-time acquisition of the elderly’s heart rate and movement speed and add time tags. Align the heart rate and speed according to the time tags. Based on the speed’s performance in terms of time continuity, determine the elderly’s various behavioral stages throughout the day. Filter out the effective behavioral stages based on the number of speed points and the average speed within each behavioral stage. Step 1 includes the following: Step 101: Acquire the elderly person's heart rate in real time. The heart rate is the number of heartbeats per minute. Acquire the heart rate once per minute and label each recorded heart rate with a time tag. The speed of the elderly is obtained, which is the average speed of movement per minute, and is obtained once per minute. Each recorded speed is labeled with a time tag, which is aligned with the time tag of the heart rate. The daily speeds are sorted by time, and a fluctuation range and an abnormal threshold are set. Data points with speeds exceeding the abnormal threshold are marked as discontinuities. Behavioral stages are filtered from the time-sorted data. The logic is as follows: Using the speed at a certain point in time as the midpoint, examine the continuous speed points within the range above and below this midpoint. When a speed exceeds the range above and below this midpoint, it is marked as a discontinuity. The distance from the starting point to the discontinuity represents a continuous interval, and this continuous interval is designated as a behavior stage. The behavior stage requires at least three speed points within the continuous interval; otherwise, it is not recorded as a continuous interval or designated as a behavior stage. Record the number of speed points and the average speed within the behavior stage.

[0021] As a preferred embodiment, the daily life of the elderly involves many specific states, such as eating, entertainment, and climbing stairs. It is difficult to categorize each state according to a fixed pattern. However, the most prominent states can be identified by their duration, such as the third behavioral state (e.g., sleep), the second behavioral state (e.g., nap), and the first behavioral state (e.g., walking). Although there are still relatively chaotic and irregular parts between different states, by judging the continuity and certainty scores, relatively stable and valuable life states can be selected. For example, a 1-kilometer walk from home to the park or a 30-minute walk in the park can be considered the first behavioral state. By analyzing these long-term stable life states, changes in bodily functions can be understood.

[0022] As a preferred embodiment, in actual practice, the elderly do not consistently maintain a certain behavioral state. Therefore, by setting a fluctuation range, this unstable factor can be taken into account to ensure the practicality of the data. For example, when the elderly are taking transportation, their speed will still be relatively stable. However, this situation is chaotic and not conducive to the analysis of physical functions. Therefore, by setting an abnormal threshold, when the elderly are detected taking transportation, this part of the data is excluded in the form of discontinuities. In addition, by combining the speed range, the speed data of the elderly can be accurately separated from waiting, walking and taking transportation.

[0023] By defining "floating range" and "abnormal threshold" to simulate the natural continuity and sudden interruptions in human activity, this step does not simply involve fixed time window segmentation, but rather employs a dynamic segmentation algorithm based on the inherent fluctuation characteristics of the data. This method effectively distinguishes between stages where the elderly "continuously engage in a certain type of activity" (such as slow walking or sitting still) and moments of "activity state transition" (such as brief, rapid movements). Points where the speed exceeds the floating range are marked as "discontinuities," essentially identifying the critical moments when the elderly's behavior patterns undergo qualitative changes, and abnormal moments, such as using transportation, are excluded using the abnormal threshold. Requiring at least three data points in a continuous interval ensures that the divided "behavioral stages" have statistically minimal analytical significance, preventing the inclusion of accidental and meaningless continuous actions in subsequent analyses. This step lays the foundation for data cleaning and primary feature extraction for the entire system, transforming the raw, homogeneous time-stream data into heterogeneous "behavioral primitives" with clear start and end boundaries, which is a prerequisite for all subsequent advanced analyses.

[0024] Step 102: Starting from the first time point, obtain all behavioral stages in the velocity time series and generate a deterministic score for each behavioral stage, based on the following formula: in, Indicates the behavioral stage score. This indicates the number of data points within this stage. This indicates the first action within this phase. Data points, This indicates the data point retrieval variable within this stage. , , This represents the average speed within this behavior phase; The dependent variable of the formula The (deterministic score) is a comprehensive indicator that directly reflects the "quality" or "reliability" of a behavioral phase. A higher score indicates that the described activity of the elderly person in that phase is more stable and consistent, such as prolonged, steady walking or quiet sitting. Conversely, a low score suggests that the phase may be brief, chaotic, or a transitional period. This score aims to address the problem of reliably extracting behavioral patterns from real-world environments—in the massive amounts of data generated by continuous monitoring, not all activity segments have equal analytical value. The system needs a method to automatically identify the periods that best represent the elderly person's regular, steady-state activities, serving as a reliable basis for subsequent in-depth physiological analysis (such as determining behavioral status and assessing cardiac function). This represents the duration of the behavior phase (the more points, the longer the duration). The larger, The higher the divisor, the more likely it is to be a meaningful "phase of behavior" (rather than a random action), reflecting a logic in reality: the longer an activity lasts, the greater its likelihood of being a meaningful "phase of behavior" (rather than a random action), and therefore its higher certainty or reliability. The divisor measures the degree of fluctuation in the elderly person's movement speed during that phase. The larger the divisor, the more volatile the speed and the less stable the activity; the smaller the divisor, the more stable the speed. Since the divisor is in the denominator, the larger the divisor, the more stable the movement. The value will decrease significantly. This corresponds to another practical logic: in a truly stable behavioral phase (such as walking at a constant speed or being completely still), the elderly person's speed should remain relatively consistent, and excessive fluctuations may indicate that the behavior is not pure or is being disturbed.

[0025] Select the behavioral stage with the highest certainty score as the effective behavioral stage, and label each data point in this behavioral stage as a discontinuity. The process of acquiring the behavioral phase, forming a deterministic score, and selecting the effective behavioral phase is grouped into one round of operation, and then several rounds of operation are performed on the velocity sequence. Set a certainty score threshold. If the highest certainty score selected in the latest round is lower than the certainty score threshold, the operation will stop.

[0026] As a preferred embodiment, each time point in each round is used as the midpoint for screening effective behavior stages, ensuring that a certain behavior stage exhibits maximum stability over time. Furthermore, each round of operation removes the previously screened effective behavior stages to avoid mutual interference between data and maximize the independence between different effective behavior stages.

[0027] As a preferred embodiment, by selecting the effective behavioral stage with the highest certainty score, the aim is to select the behavioral stage with the greatest reference value, and by selecting one and removing one in each round, the independence and reference value of the behavioral stage selected each time are further guaranteed.

[0028] Introducing a "deterministic score" and iteratively filtering the data constitutes a stability-based pattern-priority extraction strategy. The deterministic score identifies effective behavioral phases characterized by "long duration and minimal fluctuations in speed." These phases typically correspond to the elderly person's most stable and repetitive daily activities (such as long, steady walks or quiet afternoon naps). Through multiple iterations, each time the most stable pattern is "removed," the system peels back layers like an onion, revealing the various activity patterns throughout the day, from the most stable to the least stable. Setting a "deterministic score threshold" as a stopping condition is crucial for adaptively controlling the granularity of the analysis: segmentation stops when no sufficiently stable or significant patterns remain in the remaining data sequence, avoiding over-interpretation and forced classification of noisy or transitional data. This step elevates the analysis from "behavioral primitives" to "significant behavioral patterns," ensuring that the analysis objects delivered to subsequent steps have undergone importance ranking and reliability verification.

[0029] Step 2: When the speed is not 0 within the effective behavior phase, the effective behavior phase is determined to be the first behavior state. When all data within the effective behavior phase is 0, the effective behavior phase is determined to be the second or third behavior state based on the rising and falling trend of the heart rate within the effective behavior phase. Step 2 includes the following: The effective behavior stages selected from all rounds are summarized, and the behavior status of each effective behavior stage is determined based on the heart rate corresponding to the speed in the selected effective behavior stages. The determination logic is as follows: The heart rate during the effective behavior phase is sorted in chronological order and divided into three equal parts, labeled as the first segment, middle segment and last segment respectively. The average heart rate of each segment is obtained, and a heart rate change threshold is set. If the speed is not 0 within the effective behavior phase, then the behavior state of the effective behavior phase is determined to be the first behavior state. If the speed is 0 throughout the effective behavior phase and the average heart rate in the first phase exceeds the sum of the average heart rate in the second phase and the heart rate change threshold, then the behavior state of the effective behavior phase is determined to be the second behavior state. If the speed is 0 throughout the effective behavior phase and the average heart rate in the first phase does not exceed the sum of the average heart rate in the second phase and the heart rate change threshold, then the behavior state of the effective behavior phase is determined to be the third behavior state.

[0030] In a preferred embodiment, among the three behavioral states—the first, second, and third—the first behavioral state is an active state, which necessarily involves a heart rate increase phase. The second and third behavioral states are resting states, which necessarily involve a heart rate decrease phase. Comparing the heart rate at the beginning and end of each phase can identify whether one is in an active state. However, simply judging by the duration is insufficient to determine whether it is a resting state (e.g., taking a break after walking, or walking to a square to sunbathe) or a prolonged resting state (e.g., a midday nap). This is because the third behavioral state can be short-lived, while the second behavioral state can be long-lived. In the third behavioral state, the heart rate difference between the beginning and end is not significant, whereas in the second behavioral state, the difference is substantial. By setting a heart rate variation range, the second and third behavioral states can be accurately identified. This step achieves the fusion and judgment of "behavioral-physiological" dual-modal data. It doesn't just rely on zero velocity to determine a resting state; more importantly, it introduces the dynamic physiological signal of "heart rate changes in the early, middle, and late stages" as the gold standard for classification. This step can accurately distinguish between the second behavioral state (e.g., "sitting rest," where the heart rate slowly declines from a slightly higher level) and the third behavioral state (e.g., "deep sleep," where the heart rate is at its lowest and stable throughout the day). This solves the fundamental problem that velocity sensors alone cannot distinguish static activity. Dividing the effective behavioral stage into three equal parts and comparing the heart rate in the early and late stages essentially quantifies the "relaxation trajectory" of the cardiovascular system within that stage. Setting a heart rate change threshold establishes a quantifiable physiological standard for this relaxation, allowing for some variability. This step is a crucial turning point in the entire scheme, moving from "behavioral recognition" to "physiological state assessment," giving subsequent specialized analyses for different states (steps 3 and 4) clear targeting and biological interpretability.

[0031] Step 3: Identify the duration of heart rate elevation based on the heart rate change rate during the first behavioral state and label it as the heart rate response duration. Construct the response intensity using the average speed and heart rate response duration during the first behavioral state. Step 3 includes the following: Obtain the heart rate time series for the first behavioral state, determine the heart rate variability at each time point, identify the time point where the heart rate variability is 0 or the first time the heart rate variability turns from positive to negative, and mark this time point as the reaction point. The duration from the start of the first behavioral state to the reaction point is the heart rate reaction time. Obtain the average velocity during the first behavioral state and construct the reaction intensity based on the following formula: in, For the reaction strength, Indicates heart rate reaction time. This represents the average velocity during the first state. Dependent variable (Response intensity) is a rate-dependent composite index that directly reflects the "efficiency" or "economy" of how an elderly person's heart adjusts to support their primary activity state. A higher value indicates that the heart can mobilize its functions more quickly and efficiently to meet the demands of physical activity; a lower value indicates that the heart's mobilization process is relatively slow or labored. This index aims to address a key practical health assessment problem: how to quantify the heart's dynamic adaptability to daily physical activity (i.e., chronotropic cardiac function). The absolute values ​​of primary activity state velocity or heart rate alone cannot fully describe this dynamic relationship, while... By combining the two in the time dimension, the “agility” of the heart’s response to motion was captured.

[0032] This represents the objective intensity of the activity in the first behavioral state. Under the same heart rate reaction time, the higher the average speed that an older adult can maintain, the better. The larger the value, the better. This reflects the physiological logic of reality: a well-functioning heart should be able to effectively support higher-intensity physical activity. It measures the time required for the heart rate to climb and stabilize at the corresponding level from the onset of the initial behavioral state. It directly reflects the speed at which the autonomic nervous system (primarily the sympathetic nervous system) activates the heart. A shorter reaction time means the heart can enter a "working state" more quickly, resulting in a smaller denominator in the formula, thus leading to… The value increases. This corresponds to another important health concept: the heart's ability to rapidly adjust to exercise load is a marker of cardiovascular health and good physical fitness. When older adults are in their first behavioral state, their average speed is higher ( (Large) and the heart rate can be quickly stabilized ( When the numerator is small, the denominator is large. The value will rise significantly, which indicates the function of the heart and the body as a whole.

[0033] If the first behavioral state does not have a time point where the heart rate change rate is 0 or the heart rate change rate first turns from positive to negative, then the time point at the end of the first behavioral state is marked as the reaction point.

[0034] In a preferred embodiment, during the first behavioral state, the heart rate increases, but it does not increase throughout the entire first behavioral state. Once the heart rate reaches a certain level that meets the exercise requirements, it will not increase further. Therefore, the process of increasing heart rate can be identified by the time point when the heart rate changes to 0 or when it changes from positive to negative. The reason why there may be a negative rate of change is that after the heart rate stabilizes at a certain level, it is a fluctuating range, and there may be a time point when the heart rate has a negative rate of change close to 0.

[0035] This step focuses on the "first behavioral state," quantifying the heart's dynamic adaptability to physical exertion (e.g., chronotropic cardiac function). Identifying the essence of "heart rate reaction time" involves capturing the time required from the start of exercise to the heart rate reaching a stable level matching the exercise intensity. This duration is a direct indicator of the speed of autonomic regulation (especially sympathetic activation). Creatively, "average velocity" and "reaction time" are combined into "reaction intensity," constructing a composite indicator that measures the regulatory effort the heart exerts to support a unit of exercise intensity. High reaction intensity means the heart can quickly and accurately mobilize to support the current activity with excellent efficiency; conversely, low intensity indicates slow mobilization or overreaction. This analytical approach avoids the limitations of viewing heart rate or velocity in isolation, focusing instead on their dynamic relationship. It provides a novel, dynamic observation window for assessing exercise endurance and cardiac reserve function in older adults, serving as an early and sensitive indicator for predicting potential cardiac function decline.

[0036] Step 4: Identify the duration of heart rate decrease based on the rate of change of heart rate in the second behavioral state and mark it as the heart rate recovery duration. Construct the recovery intensity based on the amount of heart rate change and the heart rate recovery duration. Step 4 includes the following: Obtain the heart rate time series for the second behavioral state, determine the heart rate variability at each time point, identify the time point where the heart rate variability is 0 or the first time the heart rate variability turns from negative to positive, and mark this time point as the flattening point. Record the heart rate flattening duration from this time point to the start of the second behavioral state. Obtain the heart rate at the start of the second behavioral state and the heart rate at the flattening point respectively, and construct the flattening intensity based on the following formula: in, Indicates the intensity of the smoothing effect. This indicates the heart rate at the start of the second behavioral state. This indicates the heart rate at the calming point. Indicates the duration of heart rate pacing; Dependent variable (Recovery strength) is a rate-based indicator that directly reflects the "efficiency" or "rate" of the heart rate decrease process after an elderly person stops activity. A higher value indicates a faster and more significant decrease in heart rate during the initial resting phase; a lower value indicates a slower or less significant decrease. This indicator aims to precisely characterize a key dimension of cardiovascular health: the reactivation and recovery capacity of the cardiac vagus nerve (such as the parasympathetic nervous system). Recovery strength quantifies the efficiency of this recovery process and is an important window for assessing cardiac resilience, stress recovery capacity, and autonomic nervous system balance. The greater the decrease, the larger the numerator. The value tends to be higher. This reflects an important physiological reality: a single rest period should demonstrate a substantial recovery of heart rate from a higher post-activity level to a resting level. A smaller decrease may suggest that cardiac load has not been effectively relieved at rest or that regulatory function is limited; the shorter the recovery time, the smaller the denominator. The higher the value, the faster the recovery process. A rapid drop in heart rate indicates that the parasympathetic nervous system is being activated quickly and forcefully, a sign of a healthy cardiovascular system and good recovery. Conversely, a prolonged recovery time suggests a sluggish recovery mechanism.

[0037] If the second behavioral state does not have a heart rate variability of 0 or a time point where the heart rate variability first turns from negative to positive, then the time point at the end of the second behavioral state is marked as the calming point.

[0038] As a preferred embodiment, the second behavioral state will only begin when the elderly person feels tired. Before the next exercise, the elderly person must feel relaxed. This relaxation process will involve a decrease in heart rate. When the rest is adequate, the heart rate will decrease to a certain level and fluctuate stably. Therefore, the recovery of the heart and bodily functions can be judged by the process of heart rate decrease.

[0039] This step targets the "second behavioral state," and its underlying logic is to quantify the heart's recovery capacity after load removal (such as vagal nerve reactivation). Identifying "heart rate calming duration" and calculating "calming intensity" can represent the speed and efficiency with which the parasympathetic nervous system regains dominance, "braking" the heart from its working state and resting to its resting level. High calming intensity indicates a sensitive and efficient recovery mechanism, an important marker of cardiovascular health; low calming intensity or excessively long calming duration suggests impaired recovery function and is associated with various cardiovascular risks. By combining "heart rate decrease" with "decrease time," this indicator simultaneously considers the magnitude and rate of recovery. Steps 3 and 4 together construct a complete assessment system for the "tension and relaxation" aspects of cardiac autonomic nervous function, which has significant practical implications for cardiovascular risk screening in the elderly.

[0040] Step 5: Obtain the average daily response intensity and average recovery intensity since the start of recording. At the same time, obtain the average heart rate of the third behavioral state each day to construct a cardiac function change index and set a change threshold. When the cardiac function change index exceeds the change threshold, remind the elderly to have a physical examination.

[0041] Step 5 includes the following: The average response intensity for all first-stage behaviors and the average calming intensity for all second-stage behaviors were obtained daily. The average heart rate during all third-stage behaviors was also obtained. The average response intensity, average calming intensity, and average heart rate for all individual days since recording began were obtained and normalized to obtain cardiac function change indicators. The formula used is as follows: in, Indicators representing changes in cardiac function Indicates the first Average daily reaction intensity Indicates the first The average daily recovery intensity Indicates the first Average daily heart rate Indicates a single-day retrieval variable. , , This represents the total number of days since records began; Dependent variable The Cardiac Function Change Indicator (CDI) is a unitless scalar measure characterizing the degree of systematic deviation. It does not directly reflect the absolute level of cardiac function, but rather quantifies the "multidimensional coordinated deviation" of an elderly person's current overall cardiac function status relative to their long-term historical average. This indicator addresses the challenge of identifying early and reliable subtle but potentially clinically significant functional declines involving multiple physiological systems amidst daily fluctuations. While a single daily indicator (such as a slow heart rate response on a particular day) may be influenced by chance, a significant increase in systemic health risk occurs when all three core functional dimensions of the heart—"mobilization" (response intensity), "recovery" (calm-down intensity), and "resting load" (third-state heart rate) simultaneously show signs of deviating from the individual's baseline. Designed specifically to capture signals of such coordinated changes across multiple systems, each summation term in the formula calculates the daily average of the deviations of the daily value of the indicator from its long-term personal mean. If an indicator (such as reaction intensity) remains consistently below its personal historical mean over a period of time, the result of that term is negative; if it remains consistently above, it is positive. The increase in absolute values ​​stems from the fact that the daily biases (whether positive or negative) of these three dimensions failed to cancel each other out during the observation period, instead forming a synergistic or significant accumulation. The underlying physiological logic is that a decline in cardiac function may manifest as a persistently lower response and recovery intensity than the individual's baseline (negative bias), while the heart rate in the third behavioral state may persistently increase (positive bias). Although the directions of the biases differ, their absolute contributions all lead to... An increase in the absolute value of [the value] triggers an early warning.

[0042] Set a threshold for changes; when the changes in cardiac function indicators exceed the threshold, remind the elderly to undergo a physical examination; otherwise, do not remind them.

[0043] This step achieves early and systematic health risk warnings through the fusion of multi-dimensional time series data and trend deviation detection. It doesn't simply compare single-day data, but establishes a "personal health baseline" based on long-term historical performance. The formula calculates and sums the deviations of daily values ​​from long-term averages for three indicators (mean response intensity, mean recovery intensity, and heart rate in the third behavioral state), demonstrating sensitivity: when dimensions reflecting cardiac function (exercise response, recovery ability, and resting load) show abnormal trends deviating from the baseline (whether positive or negative), the "cardiac function change indicator" will significantly increase. It also demonstrates comprehensiveness: it integrates daytime dynamic performance and nighttime resting state, providing a 24 / 7 portrait of cardiac function. Furthermore, it demonstrates foresight: this trend-based deviation detection often detects slowly progressing functional decline earlier than a single absolute threshold (such as setting a fixed heart rate upper limit). Finally, when the comprehensive indicator exceeds the "change threshold," a health check reminder is issued, transforming complex multi-source data analysis results into a clear and actionable health recommendation, realizing a closed-loop value of data-driven intelligent analysis in elderly health management, from monitoring to intervention.

[0044] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0045] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0047] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A data intelligent analysis method for elderly care information service systems, characterized in that, The specific steps include: Step 1: Real-time acquisition of the elderly’s heart rate and movement speed and add time tags. Align the heart rate and speed according to the time tags. Based on the speed’s performance in the continuity of time, determine the elderly’s various behavioral stages in a day. Filter out the effective behavioral stages based on the number of speed points and the average speed within each behavioral stage. Step 2: When the speed is not 0 within the effective behavior phase, the effective behavior phase is determined to be the first behavior state. When all data within the effective behavior phase is 0, the effective behavior phase is determined to be the second or third behavior state based on the rising and falling trend of the heart rate within the effective behavior phase. Step 3: Identify the duration of heart rate increase based on the heart rate change rate during the first behavioral state and label it as the heart rate response duration. Construct the response intensity using the average speed and heart rate response duration during the first behavioral state. Step 4: Identify the duration of heart rate decrease based on the rate of change of heart rate in the second behavioral state and mark it as the heart rate recovery duration. Construct the recovery intensity based on the amount of heart rate change and the heart rate recovery duration. Step 5: Obtain the average daily response intensity and average recovery intensity since the start of recording. At the same time, obtain the average heart rate of the third behavioral state each day to construct a cardiac function change index and set a change threshold. When the cardiac function change index exceeds the change threshold, remind the elderly to have a physical examination.

2. The data intelligent analysis method for an elderly care information service system according to claim 1, characterized in that: The heart rate of the elderly is acquired in real time, which is the number of heartbeats per minute. The heart rate is acquired once per minute, and a time label is added to each recorded heart rate.

3. The data intelligent analysis method for an elderly care information service system according to claim 2, characterized in that: The speed of the elderly is obtained, which is the average speed of movement per minute, and is obtained once per minute. Each recorded speed is labeled with a time tag, which is aligned with the time tag of the heart rate. The daily speeds are sorted by time, and a fluctuation range and an abnormal threshold are set. Data points with speeds exceeding the abnormal threshold are marked as discontinuities. Behavioral stages are filtered from the time-sorted data. The logic is as follows: Using the speed at a certain point in time as the midpoint, examine the continuous speed points within the range above and below this midpoint. When a speed exceeds the range above and below this midpoint, it is marked as a discontinuity. The distance from the starting point to the discontinuity represents a continuous interval, and this continuous interval is designated as a behavior stage. The behavior stage requires at least three speed points within the continuous interval; otherwise, it is not recorded as a continuous interval or designated as a behavior stage. Record the number of speed points and the average speed within the behavior stage.

4. The data intelligent analysis method for an elderly care information service system according to claim 3, characterized in that: Starting from the first time point, all behavioral stages in the velocity time series are acquired, and a deterministic score is generated for each behavioral stage, based on the following formula: in, Indicates the behavioral stage score. This indicates the number of data points within this stage. This indicates the first action within this phase. Data points, This indicates the variable for retrieving data points within this stage. , , This represents the average speed within this behavior phase; The behavioral phase with the highest certainty score is selected as the effective behavioral phase, and each data point in this behavioral phase is marked as a discontinuity.

5. The data intelligent analysis method for an elderly care information service system according to claim 4, characterized in that: The process of acquiring the behavioral phase, forming a deterministic score, and selecting the effective behavioral phase is grouped into one round of operation, and then several rounds of operation are performed on the velocity sequence. Set a certainty score threshold. If the highest certainty score selected in the latest round is lower than the certainty score threshold, the operation will stop.

6. The data intelligent analysis method for an elderly care information service system according to claim 5, characterized in that: The effective behavior stages selected from all rounds are summarized, and the behavior status of each effective behavior stage is determined based on the heart rate corresponding to the speed in the selected effective behavior stages. The determination logic is as follows: The heart rate during the effective behavior phase is sorted in chronological order and divided into three equal parts, labeled as the first segment, middle segment and last segment respectively. The average heart rate of each segment is obtained, and a heart rate change threshold is set. If the speed is not 0 within the effective behavior phase, then the behavior state of the effective behavior phase is determined to be the first behavior state. If the speed is 0 throughout the effective behavior phase and the average heart rate in the first phase exceeds the sum of the average heart rate in the second phase and the heart rate change threshold, then the behavior state of the effective behavior phase is determined to be the second behavior state. If the speed is 0 throughout the effective behavior phase and the average heart rate in the first phase does not exceed the sum of the average heart rate in the second phase and the heart rate change threshold, then the behavior state of the effective behavior phase is determined to be the third behavior state.

7. The data intelligent analysis method for an elderly care information service system according to claim 6, characterized in that: Obtain the heart rate time series for the first behavioral state, determine the heart rate variability at each time point, identify the time point where the heart rate variability is 0 or the first time the heart rate variability turns from positive to negative, and mark this time point as the reaction point. The duration from the start of the first behavioral state to the reaction point is the heart rate reaction time. Obtain the average velocity during the first behavioral state and construct the reaction intensity based on the following formula: in, For the reaction strength, Indicates heart rate reaction time. This represents the average velocity during the first state. If the first behavioral state does not have a time point where the heart rate change rate is 0 or the heart rate change rate first turns from positive to negative, then the time point at the end of the first behavioral state is marked as the reaction point.

8. The data intelligent analysis method for an elderly care information service system according to claim 7, characterized in that: Obtain the heart rate time series for the second behavioral state, determine the heart rate variability at each time point, identify the time point where the heart rate variability is 0 or the first time the heart rate variability turns from negative to positive, and mark this time point as the flattening point. Record the heart rate flattening duration from this time point to the start of the second behavioral state. Obtain the heart rate at the start of the second behavioral state and the heart rate at the flattening point respectively, and construct the flattening intensity based on the following formula: in, Indicates the intensity of the smoothing effect. This indicates the heart rate at the start of the second behavioral state. This indicates the heart rate at the calming point. Indicates the duration of heart rate pacing; If the second behavioral state does not have a heart rate variability of 0 or a time point where the heart rate variability first turns from negative to positive, then the time point at the end of the second behavioral state is marked as the calming point.

9. The data intelligent analysis method for an elderly care information service system according to claim 8, characterized in that: The average response intensity for all first-stage behaviors and the average calming intensity for all second-stage behaviors were obtained daily. The average heart rate during all third-stage behaviors was also obtained. The average response intensity, average calming intensity, and average heart rate for all individual days since recording began were obtained and normalized to obtain cardiac function change indicators. The formula used is as follows: in, Indicators representing changes in cardiac function Indicates the first Average daily reaction intensity Indicates the first The average daily recovery intensity Indicates the first Average daily heart rate Indicates a single-day retrieval variable. , , This represents the total number of days since records began; Set a threshold for changes; when the changes in cardiac function indicators exceed the threshold, remind the elderly to undergo a physical examination; otherwise, do not remind them.

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