Method for generating pressure level information

Abstract

A method for generating stress level information indicative of a stress level of a population comprising a plurality of individuals, in which a real-time statistical value of the stress level of the population is generated by statistically processing real-time individual stress information for each of the plurality of individuals of the population; upon receiving more real-time individual pressure information for each of the plurality of individuals via the network, continuously updating the real-time statistical value; receiving, via the network, information indicative of a pressure change event occurring in real time, and associating the real-time statistics of the pressure level for the population with the pressure change event occurring in real time; obtaining a group pressure index according to the association, wherein the group pressure index indicates the pressure of a group consisting of the plurality of individuals caused by a pressure change event occurring in real time; and notifying at least some of the plurality of individuals of the population stress index or notifying a third party of the population stress index.

Description

Methods for generating pressure level information

[0001] This application is a divisional application of the invention patent application with application number 201580072635.5. Technical Field

[0002] The content of this article generally relates to methods for generating stress level information for human groups. Background Technology

[0003] Stress is believed to contribute to a range of illnesses, including heart disease, obesity, diabetes, and cancer. It is also thought to negatively impact worker productivity. One estimate puts $2,500 annually at the expense of employers in developed countries due to absenteeism and inattentiveness among workers. The combined cost of stress-related healthcare and productivity losses amounts to billions of dollars annually.

[0004] Human stress can be categorized as acute (short-term) or chronic (long-term).

[0005] Examples of sources of acute stress include physical activities that are not used to, relationship distress, bereavement, public speaking, or having an unusually high workload for days, weeks, or months. People typically adapt to acute stress and recover once it subsides. Because of this ability to adapt and recover, acute stress itself may not be as damaging to our health as chronic stress.

[0006] However, stress resilience can indicate potential harm to a person's health. Stress resilience is a person's ability to respond to acute stressful events or states of stress. For example, a particularly important aspect of stress level resilience is the time it takes for an individual's acute stress elements and indicators (single or in combination) to return to "stress-free" or baseline levels after any particular stressful event.

[0007] For example, if a person becomes stressed—while exercising or giving a presentation at work—their stress indicators, such as heart rate, blood pressure, and perspiration (dermal conductivity), will rise. These stress measurements can be detected and recorded.

[0008] When stress subsides, these metrics will return to their previous baseline within the next 15 to 30 minutes. However, for individuals with "decreased stress resilience," their stress response can be faster (more "excited"), can be amplified or intensified (more "reactive"), and takes longer to return to "normal." Their stress "half-life" or "recovery to baseline" takes longer (slower recovery). The faster and more severe the response, the longer the recovery time, and the lower the individual's stress resilience, even if their stress measurements eventually return to "normal" or "baseline" levels.

[0009] When observing a large group of people, such as assessing the group's stress levels, identifying the group's underlying stress characteristics or stress profile is very useful to provide better context for analyzing the specific stress levels or stress characteristics of any individual within the group. This can greatly increase the accuracy and effectiveness of assessing an individual's acute and chronic stress.

[0010] Detailed data on stress levels among a large number of people is needed. Governments will be able to use this data in several ways. First, detailed stress data will enable governments and other organizations to objectively assess the benefits of stress management methods and programs.

[0011] Second, it is economically advantageous for governments to be able to quickly determine the impact of their policies on the stress felt by those they govern. Almost any government policy can potentially affect the level of stress felt by those it governs, and stress, in turn, impacts economic productivity. Unfortunately, it is impossible to directly and quickly measure the impact of policy decisions on the stress felt by a group.

[0012] One problem hindering stress research is the inability to quickly measure stress in large populations, such as groups within a city or country. Current methods for measuring stress typically include psychological, physiological, or cognitive tests. However, large-scale testing involves conducting these types of tests on a large scale, which is slow, labor-intensive, and expensive.

[0013] The cost of conducting stress tests results in a relatively small number of people being included in studies. The only option is to infer trends from a small group of test subjects, but this process assumes that the sample group is representative of the entire population, which is unlikely, and it is difficult to find a sample group willing to give up the time to conduct tests regularly. Summary of the Invention

[0014] In one embodiment, a method for generating stress level information indicating the stress level of a group comprising multiple individuals is provided. The method includes: receiving personal information of each of the multiple individuals and individual stress information of each of the multiple individuals via a network; in a processing system, selecting the multiple individuals from the multiple individuals to constitute the group using the personal information of each of the multiple individuals; receiving real-time individual stress information of each of the multiple individuals via the network, wherein the real-time individual stress information of each of the multiple individuals includes one or more of the following: psychometric information of each of the multiple individuals, physiological information of each of the multiple individuals, and behavioral information of each of the multiple individuals; and in the processing system, statistically processing... The method further includes: processing real-time individual stress information of each of the plurality of individuals in the group to generate a real-time statistical value of the stress level of the group; and continuously updating the real-time statistical value as more real-time individual stress information of each of the plurality of individuals is received via the network; the method also includes: receiving information indicating a real-time stress change event via the network, and associating the real-time statistical value of the stress level of the group with the real-time stress change event; obtaining a group stress index from the association, the group stress index indicating the stress level of the group composed of the plurality of individuals caused by the real-time stress change event; and notifying at least some of the plurality of individuals of the group, or notifying a third party of the group stress index.

[0015] In one embodiment, the individual stress information also includes cognitive function information of each of the plurality of individuals.

[0016] In one embodiment, the personal information includes at least one of the following: date of birth information, place of birth information, gender information, ethnicity information, occupation information, postal code information, education information, health insurance coverage information, relationship status information, number of children information, pet information, exercise habits information, dietary habits information, health history information, and information indicating the stress management methods currently being used.

[0017] In one embodiment, the information indicating real-time stress change events is derived from general data related to situations or events that may affect a large number of people. This general data includes at least one of the following: internet keyword search behavior information, content information, sentiment or themes of social media communications, date information, time information, public holiday information, temperature information, humidity information, weather information, traffic information, news information, current events information, consumer shopping information, financial market information, economic information, announcements information, political event information, sports event information, major event information, mortgage interest rate information, housing information, employment information, survey information, voting information, voting schedule information, business confidence information, business investment information, and business productivity information.

[0018] In one embodiment, the method includes the step of generating a stress index using the statistical values ​​in the processing system.

[0019] In one embodiment, the method includes the step of the processing system sending the stress index to a plurality of computing devices.

[0020] In one embodiment, the method includes the step of the processing system sending statistical values ​​of the stress levels of the plurality of individuals to the plurality of computing devices.

[0021] In one embodiment, the step of receiving psychometric information from each of the plurality of individuals includes: each of the plurality of individuals responding to an electronic stress questionnaire.

[0022] In one embodiment, the questionnaire is divided into two parts, each part including a different set of predefined questions, thereby presenting the second set of questions to the individual based on predetermined criteria related to the answers provided to the first set of questions.

[0023] In one embodiment, the step of receiving physiological information from each of the plurality of individuals includes generating at least one of the following: heart rate information, heart rate variability information, respiratory rate information, respiratory rate variability information, blood pressure information, body movement information, cortisol level information, skin conductivity information, skin temperature information, blood oxygen saturation information, surface electromyography information, electroencephalography information, blood information, saliva information, skin conductance information, information about chemicals found on or within the skin, and urine information.

[0024] In one embodiment, the step of receiving behavioral information of each of the plurality of individuals includes at least one of the following steps: generating eye movement information indicating eye movements of each of the plurality of individuals; generating location information indicating multiple locations where each of the plurality of individuals has been; generating proximity device information indicating multiple devices of multiple people nearby for each of the plurality of individuals; generating internet browsing history information for each of the plurality of individuals; generating keystroke rate, rhythm, typing style, pressure, or "force" detection information for the individual; generating voice analysis for the individual, including pitch, rhythm, word, and phrase detection information; generating telephone usage analysis for the individual, including call duration, dialed numbers, and time of day for the call; generating driving style for the individual, including data from steering input, acceleration, deceleration, braking, driving speed, braking and accelerator force, and information from door pressure sensors; generating The system generates individual data including: movement and body temperature; television usage data (including channel viewing, viewing time, and eye movements during viewing); refrigerator analysis; and heating and cooling analysis. It also generates individual bicycle data, including pedaling force, pedaling rhythm, acceleration, speed, route taken, GPS data, altimeter data, time spent on the bicycle, and pedometer data. Furthermore, it generates individual pedometer data and gait analysis information. The system generates application usage information indicating the application usage of each individual; media consumption information indicating the media consumption of each individual; consumption behavior information indicating the consumption behavior of each individual; food selection information indicating multiple food choices made by each individual; social travel information indicating the social travel activities of each individual; and vacation information indicating the vacation time of each individual.

[0025] In one embodiment, the step of receiving cognitive function information of each of the plurality of individuals includes at least one of the following steps: generating memory function information indicating memory function of each of the plurality of individuals; generating reaction time information indicating reaction time of each of the plurality of individuals; generating attention, peripheral vision and comprehension of the individuals; and generating decision ability information indicating decision-making ability of each of the plurality of individuals.

[0026] In one embodiment, the method further includes the step of generating a stress resilience score indicating the response of each of the plurality of individuals to acute stress; wherein the stress resilience score indicates one or more of the following: the time required for the plurality of individuals to respond to the acute stress event, whether the plurality of individuals responded to the acute stress event, and if so, the level of response exhibited by the plurality of individuals to the acute stress event, and the time taken for the stress information of the plurality of individuals to return to a baseline level after the acute stress period. Attached Figure Description

[0027] Embodiments will now be described by way of example only with reference to the accompanying drawings, in which: Figure 1 shows a block diagram of the components of the system architecture, and a method for generating the profile of the pressure level and pressure elasticity level in the population. Detailed Implementation

[0028] Figure 1 is a block diagram of the components of the pressure profiler architecture, which includes: 1. Group pressure profiler 2. Server 3. Database 4. Individual pressure profiler 5. Communication network 6. General data source.

[0029] The group stress analyzer (1) includes a computer server (2) that communicates with a database (3).

[0030] A computer server (2) is configured to perform the steps of an embodiment of the methods described herein. The method may be encoded as a program for instructing the processor of the computer server. In this embodiment, the program is stored in non-volatile memory, but may also be stored in FLASH, EPROM, or any other form of tangible media external to the computer server. The program typically (but not necessarily) comprises multiple software modules that cooperate when installed on the system to perform the steps of an embodiment of the method. Each software module corresponds at least partially to a step of a method or component of the system described herein. These functions or components may be divided into modules or segmented across multiple software and / or hardware modules. Software modules may be formed using any suitable language, examples including C++ and assembly. The program may take the form of an application programming interface or any other suitable software architecture.

[0031] The computer system coupled to the computer server (2) includes a suitable microprocessor, such as or similar to an Intel Xeon or AMD Opteron microprocessor connected to memory via bus 16, the memory including approximately 1 GB of suitable form of random access memory 18, or generally any suitable alternative capacity, and non-volatile memory 20 such as a hard disk drive or solid-state non-volatile memory (e.g., NAND-based FLASH memory) with a capacity of approximately 500 Gb, or any alternative suitable capacity. Alternative logic devices may be used instead of the microprocessor. Examples of suitable alternative logic devices include application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and digital signal processing units. Some embodiments in these examples may be entirely hardware-based.

[0032] The pressure profiler (1) has at least one communication interface. In this embodiment, at least one communication interface 22 includes a network interface in the form of an Ethernet card; however, any suitable network interface, such as a Wi-Fi module, can generally be used. The network interface 22 is configured in this embodiment (but not necessarily in all embodiments) to send and receive information in the form of data packets. The data packets are in the form of Ethernet frames with Internet Protocol (IP) packet payloads. Although any suitable protocol can be used, IP packets typically have Transmission Control Protocol (TCP) segment payloads. In this embodiment, the TCP segment may carry Hypertext Transfer Protocol (HTTP) data, such as web page information in HTTP, or HTTP requests or responses. HTTP data may be sent to a remote machine. However, in alternative embodiments, proprietary protocols and applications may be used, or generally any suitable protocol (e.g., SONET, Fibre Channel) or appropriate application may be used.

[0033] Specifically, the pressure profiler (1) receives pressure data transmitted to it by many individual pressure profilers (4) via a communication network (5) (e.g., the Internet). The group pressure profiler (1) also receives general data transmitted from various other data sources (6) (e.g., news publications, government statistical bureaus, stock markets, and weather data services).

[0034] The database (3) stores received stress data, personal data, and general data. The server (2) includes software that periodically searches for trends in stress data, personal data, and general data, as well as correlations between stress, personal data, and general data. In particular, the server (2) may include a learning function that identifies patterns in stress information associated with previous stress periods. Over time, the learning function gradually improves the accuracy and speed of stress profiling of users.

[0035] The server (2) may also include predictive capabilities that identify patterns of stress information that indicate early signs of stress and inform the user at an early stage. For example, the stress profiler 1 may correlate patterns of eye movements with physiological or psychometric indicators of stress for a particular user and inform the user when those eye movements are detected before severe symptoms appear.

[0036] In addition, the predictive function can identify patterns of stress information that indicate the likelihood of future stress and inform the user accordingly.

[0037] Each personal stress profiler (4) operates on a computing device such as a smartphone, smartwatch, tablet, desktop computer, or laptop computer, and may be wireless (as shown in Figure 1) or connected via cable. Each personal stress profiler may use external devices (such as a heart rate monitor) or an integrated data logging system to measure and observe stress, and use that information to generate stress scores in various forms of stress: 1. physical / physiological stress, 2. mental stress, 3. emotional stress, and 4. currently felt life stress.

[0038] Each stress score indicates the magnitude of a form of stress. Once the individual profiler has generated a set of stress scores, these scores, along with individual data (age, location, measurement time, etc.), are transmitted to the group stress profiler. However, if the user has previously consented to this, the user's stress data and individual data are only transmitted to the server.

[0039] As described above, the stress profiler (1) receives stress data from the group and individual data, and uses the data to generate a stress profile indicating the stress felt by the group. The stress data from each individual in the group includes at least two of the following: • psychometric data indicating stress, • physiological data indicating stress, • behavioral data indicating stress, and • cognitive function data indicating stress.

[0040] Obtaining at least two types of stress data from each individual in the group is important for the following reasons: 1. Multiple types of stress data increase the sensitivity to lower stress levels during testing. Some forms of stress testing tend to be more sensitive to acute stress, while others tend to be more sensitive to chronic stress. For example, chronic stress cannot be determined simply by measuring only physiological data.

[0041] 2. Multiple types of stress data increase the percentage (or “range”) of people whose stress can be detected during testing. This is because stress is manifested differently in different populations, depending on many factors such as genetic makeup, health status, physical condition, and health history. Multiple types of stress tests detect more stress responses.

[0042] 3. Multiple types of stress data allow for the identification of more specific forms of stress felt by people, such as acute or chronic stress, or other categories such as physical / physiological stress, mental stress, emotional stress, or currently felt life stress. The ability to identify specific forms of stress enables the development of more targeted and effective treatment plans.

[0043] The population stress profiler of this invention can be used to measure stress in large populations (e.g., thousands, millions, or billions of people). With a large number of people submitting stress and personal data, the stress profiler will receive frequent stress measurements, making it possible to monitor stress rapidly.

[0044] Individuals in a group generate stress data by taking a standardized self-administered stress test. Preferably, each person in the group uses a device to guide them through the self-administered stress test and transmits stress data and personal data to a stress profiler. An example of such a device is the personal stress profiler described in another patent application filed by the applicant on November 11, 2014, namely Australian Patent Application No. 2014904524. People are motivated to use the device because it allows them to provide direct personal feedback on their stress, which helps in managing stress.

[0045] The amount of stress data received from a group will vary from person to person, depending on how much data they choose to collect and how much they choose to share. A stress profiler obtains at least two types of stress data from each individual in the group. In one embodiment, the stress profiler receives psychometric and physiological data. However, the accuracy and sensitivity of the stress data from each individual typically increase when more types of stress data are received from each person. Therefore, a stress profiler can obtain three or even all four types of stress data from a group.

[0046] Stress data should be in a standardized format, and the same type of test should be used on all people in the group to ensure fair data comparisons between individuals.

[0047] Stress data can be raw data from each test, or it can be derived data indicating test results (such as test scores). Receiving test scores instead of raw data is advantageous because it reduces the amount of data to be transmitted.

[0048] Examples of personal data that a personal data stress profiler can receive from individuals in a group include: • Date of birth; • Place of birth; • Gender; • Race; • Occupation; • Postal code of home address; • Postal code of employment location; • Education; • Previous postal code or postal district code; • Health insurance; • Relationship status; • Number of children; • Pets; • Exercise and dietary habits; • Health history; • Current stress management methods.

[0049] Stress profilers use personal data to categorize stress data, such as by age, geographic location, occupation, relationship status, or exercise habits. This personal data helps understand whether stress interventions are effective for everyone, or more effective for specific groups.

[0050] Stress profilers can protect user privacy by avoiding the collection of any information from individuals who explicitly identify themselves as providing personal and stress data.

[0051] The amount of data received from a group will vary from person to person, depending on how much data they choose to collect and how much data they choose to share.

[0052] By combining stress data and personal data with more general data, stress profilers can also be configured to receive and process many other types of general data related to situations or events that may affect a large number of people. Stress profilers can be configured to search for correlations between general data, stress data, and personal data. By collecting and processing general data, stress data, and personal data, stress profilers have the opportunity to identify causes of stress and correlations between stress data and various aspects of general data.

[0053] If sufficient pressure data is received in near real-time to monitor pressure, the correlation between general data and the timing of pressure data can be used to identify the relationship between the two. For example, a pressure profiler can monitor the impact of published news and announcements on pressure levels.

[0054] Examples of general data that can be received by a pressure profiler include information indicating the following: • Internet keyword search behavior; • Content, sentiment, or topics of social media communications; • Dates, times, and public holidays; • Temperature, humidity, and weather; • Traffic; • News and current events; • Consumer shopping data (sales units, purchase order levels or indices, consumer confidence levels, etc.); • Financial market data (currency exchange rates, commodities, stocks, financial indices, etc.); • Economic data; • Public and political announcements; • Political events, sporting events, and other thematic activities; • Housing loan interest rates, housing, and employment data; • Group surveys or voting; • Ballots; • Business confidence data; • Business investment data; • Business productivity data.

[0055] Pressure profilers can receive and process many other types of general data.

[0056] For example, a group stress profiler can search for and identify the correlation between group stress levels and the use of specific keyword search terms in internet search engines.

[0057] Group stress profilers measure stress fluctuations within a population. Data received by these profilers can be used to measure stress fluctuations within the population as a whole or within specific segments (e.g., stress changes within a group based on geographical location, age, employment type, etc.). With a sufficient number of participants (thousands or millions), group stress profilers can be sensitive to instantaneous fluctuations in stress and monitor stress in near real-time, similar to data submitted by individual stress profilers.

[0058] By inputting general data, the Group Stress Profiler can determine the impact of variables such as weather, news, or traffic on stress levels. Stress fluctuations can be categorized by age, gender, occupation, income, and any other category.

[0059] The Group Stress Index Profiler can generate a group stress index that indicates the magnitude of group stress. It can also publish the group stress index to show the impact of news and announcements on stress levels.

[0060] A population stress profiler does not necessarily determine the cause of stress changes within a population. Instead, it provides data showing whether average stress is rising or falling, offering an opportunity to investigate the underlying causes.

[0061] Data can be transmitted back to the user group stress analyzer or back to the individual stress analyzer.

[0062] a) The algorithmic group stress profiler can transmit updates to the algorithm used by the individual stress profiler to calculate stress scores.

[0063] (b) Current Group Stress Levels: Group stress profilers can transmit information about currently measured stress levels within a group or a portion of the group relevant to the user of the profiler. For example, a group stress profiler can inform a user about stress levels in their local area or within the same country and industry of employment. This feedback will be useful to the user and may encourage them to submit their stress data and personal information to the group stress profiler.

[0064] For example, if a user's stress score in San Francisco increases by 2%, this can be communicated so they can understand their stress score in that situation. This improves the relevance of stress scores measured by a personal stress profiler.

[0065] The instantaneous data collected by group stress profilers enhances the ability of personal stress profilers to detect and quantify acute stress compared to each individual's chronic stress. Acute stress is generally considered less harmful and less noticed than chronic stress, thus enabling the identification of differences and helping to pinpoint the types of stress that users should pay more attention to.

[0066] The ability of individuals to compare their scores with other comparable individuals in near real-time can help motivate people to make positive changes to stress-related behaviors. Comparing oneself to others can be motivating, and the near real-time nature of the information generated by the group stress profiler provides greater perceived relevance.

[0067] For example, an accountant will be able to see how his colleagues increase their stress scores by x% during tax time, but he is only affected by y% due to his stress management habits. According to published research, he can see that by increasing his existing stress score by a%, his productivity increases by b%.

[0068] c) Risk Index: Over time, the group stress profiler can identify situations typically associated with stress and generate a risk index for general situations. If the stress profiler has information about the user's individual circumstances, it can inform the user of their risk of experiencing significant stress, even before reporting changes in stress.

[0069] Users can also use the stress index to assist in decision-making and potentially avoid stressful situations in the future. For example, for a 40-year-old divorced male accountant with two children who is moving to London and earning £70,000 a year, the group stress profiler can provide a stress index indicating the level of stress he might experience in such a situation. The accountant can consider this information when deciding whether to proceed with the move to London.

[0070] Once users submit their stress and personal data to the group stress profiler, it can suggest how their stress scores might change in the future—a "stress trajectory." Users can use this information to implement stress management interventions and identify the potential impact of these stresses. Their stress trajectories are updated as users submit further stress and personal data.

[0071] On a larger scale, the group stress profiler can generate risk indices and stress profiles for entire groups within a city or country.

[0072] Psychometric data indicates the response to an individual's subjective stress perception questionnaire.

[0073] Preferably, the questionnaire asks questions about a wide range of signs or symptoms associated with human stress response, especially those related to chronic stress accumulation.

[0074] The questionnaire has a wide range of questions, which is beneficial because it allows stress to be detected in more people.

[0075] To best measure psychological stress, both "long-form" and "short-form" questionnaires have been developed as part of this invention. In use, psychological stress measurement will be deployed as a two-stage approach, including the "long-form" and "short-form" questionnaires. During the first stage, an initial set of questions is presented to the individual. In a preferred embodiment, the questions constituting part of this first stage will take approximately three minutes to complete. If the individual's score exceeds a certain cutoff level or a pre-defined pattern, the individual will be prompted to complete another set of questions, constituting the second stage of the questionnaire. In a preferred embodiment, this second set of questions will take approximately four to five minutes to complete. It is also envisioned that individuals will be able to choose (if needed) to complete the second stage of questions, regardless of their score when completing the first stage.

[0076] The higher the number and severity of chronic stress indicators in the questionnaire, the more likely they are associated with a single underlying cause (chronic stress) rather than just occurring in the same person. For example, a person might occasionally experience tense shoulders, digestive problems, and occasional rashes. These symptoms (alone or even all three together) can have many different causes and are unrelated to the person developing chronic stress. However, if they also have persistent headaches, difficulty falling asleep at night, and frequent viral infections, the situation begins to change: they now have six chronic stress indicators.

[0077] The answers to some questions may be strongly correlated with those to other questions, forming statistical coherence factors (determined through a psychometric statistical method called exploratory factor analysis). Each statistical coherence factor can indicate the specific type of stress an individual experiences.

[0078] In one embodiment, psychometric data includes responses to a questionnaire that asks individuals about their subjective experiences with stress-related signs, symptoms, or indicators in four forms: • physical / physiological stress, • mental stress, • emotional stress, and • currently felt life stress.

[0079] Questionnaires can use multi-line questions to cover a range of known subjective states associated with stress—particularly those identified as indicators of chronic human stress. The questionnaire indicates which form of stress an individual scores higher for. The individual can then be given feedback on which type of intervention is most likely to provide the greatest benefit and the results can be tracked over time.

[0080] By combining psychometric information with other types of stress information, such as physiological, behavioral, or cognitive data, the sensitivity and scope of stress profilers are increased. Furthermore, these additional types of stress data help identify individuals who do not respond well to questionnaires.

[0081] There are many known physiological indicators of stress in humans. Many lie detectors are based on measuring a variety of physiological indicators of stress.

[0082] When the pressure analyzer 1 uses physiological information, and when the physiological information includes measurements of more than one physiological parameter, the accuracy and sensitivity of the pressure analyzer 1 generally increase.

[0083] Examples of various measurements that can be used to provide physiological information include heart rate measurement, heart rate variability measurement, respiratory rate measurement, respiratory rate variability measurement, blood pressure measurement, physical activity observation, cortisol level measurement (measured in blood or saliva), skin conductivity measurement, skin temperature measurement, skin or hair analysis, DNA analysis, blood oxygen saturation measurement, surface electromyography (surface EMG) measurement, electroencephalography (EEG) measurement, and other physiological indicators of pressure that can be determined by analyzing a person's blood, saliva, or urine. Saliva, blood, urine, skin, hair, and DNA measurements can be performed through routine laboratory tests or through nanotechnology. For example, nanotechnology sensors can be used for single-drop blood measurements, can be incorporated into transdermal patches, can be injected subcutaneously or circulated within an individual, or can be combined with subcutaneously embedded microchips or wire sensors.

[0084] In addition, "smart clothing" can be used, including pants / trousers, underwear, socks, shoes, shirts / T-shirts, gloves, hats / caps / helmets, glasses, watches, smartwatches, wrist and ankle straps, and adhesive patches. "Smart clothing" incorporates various sensors, including electrical signals, conductivity (current conductance and resistance), accelerometers, force, temperature, chemical sensors, and nanotechnology sensors, which can be used to provide physiological information.

[0085] Physiological measurements can be selected based on their sensitivity and relevance, as well as their ease of use as screening devices.

[0086] The physiological data collection tool, Pressure Profilometer 1, includes the ability to accept input from multiple physiological information collection tools. Each physiological information collection tool measures one aspect of the user's physiology that indicates the user's stress. Examples of suitable physiological information collection tools that can be used with Pressure Profilometer 1 include, but are not limited to: • Heart rate monitors, such as chest-mounted or arm-mounted devices used in sports, such as Catapult Sports. TM Performance monitoring equipment, Polar TM Heart rate monitor, Fitbit TMOr a smartwatch capable of detecting heart rate; • A respiratory rate monitor, such as a chest-mounted or arm-mounted device used in sports, like Catapult Sports. TM Performance monitoring devices; • Blood pressure monitors, such as cuffs that periodically inflate and deflate around the upper arm; • Body motion sensors, such as motion sensors with gyroscope support used by athletes, for example, by Catapult Sports. TM Offered: • Location tracking devices, such as GPS-enabled smartphones or smartwatches; • Salivary cortisol analysis devices; • Skin conductivity measurement devices; • Skin temperature measurement devices; • Blood oxygen saturation measurement devices, such as finger pulse oximeters; • Surface electromyography (Surface EMG) devices; • Electroencephalography (EEG) devices; • "Smart clothing," including trousers / pants, underwear, socks, shoes, shirts / T-shirts, gloves, hats / caps / helmets, glasses, watches, smartwatches, wrist and ankle straps, and adhesive patches, embedding various sensors, including electrical signals, conductivity (current conductance and resistance), accelerometers, force, temperature, chemical sensors, and nanotechnology sensors that can be used to provide physiological information.

[0087] • Nanotechnology sensors, which may include single-drop blood devices, transdermal patches, subcutaneous injection devices, or circulatory injection devices; • Blood testing devices (e.g., suitable for detecting chemicals, molecules, proteins, and hormones such as catecholamines, adrenaline, noradrenaline, serotonin, or dopamine) that indicate pressure or stimulation of the hypothalamus-pituitary-adrenal axis (HPA axis); and • Chips or wires implanted in the human body (e.g., suitable for detecting chemicals, molecules, proteins, and hormones such as catecholamines, adrenaline, noradrenaline, serotonin, or dopamine) that indicate pressure or stimulation of the hypothalamus-pituitary-adrenal axis (HPA axis).

[0088] The tools described above can be integrated into computing devices, online, or as standalone external devices. If the tool is external, it can be connected to the computing device using any suitable method, such as via cable or wireless Bluetooth connection.

[0089] When the stress profiler 1 uses behavioral information, its accuracy and sensitivity typically increase when the behavioral information includes measurements of more than one behavioral parameter. These behaviors may often be known indicators of human stress, or they may be individual characteristics of the user. For example, a user may exhibit specific eye movement patterns, pace, or visit specific locations when stressed.

[0090] The stress profiler 1 can gradually acquire behavioral information by progressively correlating behavior with other forms of stress information, such as cognitive function information, psychometric information, or physiological information.

[0091] Examples of different measurements or behavioral observations that can be used to provide behavioral information include eye movement patterns, social interactions, types of websites visited, types of applications used, news reading, consumer behavior, food choices, social outings, vacations, and so on.

[0092] Data can be obtained from smartphones, smartwatches or other wearable devices, tablets, and computers, and can be measured via accelerometers, gyroscopes, altimeters, GPS, NFC (proximity to other devices, enhanced location specificity), Bluetooth (proximity to other devices, enhanced location specificity), and Wi-Fi (proximity to other devices, enhanced location specificity). Other inputs can be measured, such as keystroke rate, rhythm, typing style, pressure or "force" detection (keyboard, touchpad, screen pressure sensors), voice analysis (pitch, rhythm, word and phrase detection), phone usage, including call duration, dialed numbers, application ("app") usage, including the specific app used, usage duration, time spent using the app throughout the day, in-app analytics (usage characteristics within any app), keyword searches, word and phrase usage (typically applied to word processing, email, messaging, and social media apps, but not limited to), eye movement patterns, gait and posture analysis, and shopping history.

[0093] Other behavioral observations can be obtained from car / driving / riding styles, including steering input, acceleration, deceleration, braking, driving speed, braking and accelerator force, door pressure sensors, and other vehicle sensors.

[0094] Further behavioral observations can be obtained from home or office sensors that can measure motion, body temperature, TV usage (channels watched, viewing time, eye movements), refrigerator analysis, heating and cooling analysis, and other "smart home" analytics.

[0095] In addition, behavioral observations can be obtained from other measurement devices such as bicycle gauges (pedal force, pedaling rhythm, acceleration, speed, route taken, GPS, altimeter, time spent on the bicycle, etc.), pedometers, gait analysis measurements, and other measurements from "smart clothing," including trousers / pants, underwear, socks, shoes, shirts / T-shirts, gloves, hats / caps / helmets, glasses, watches, smartwatches, wrist and ankle straps, and adhesive patches.

[0096] The behavioral data collection tool, Stress Profiler 1, includes the ability to accept input from multiple behavioral information collection tools. Each behavioral information collection tool measures one aspect of user behavior that indicates user stress. Examples of suitable behavioral information collection tools that can be used in Stress Profiler 1 include, but are not limited to: • Eye-tracking software; • Location tracking devices, such as GPS-enabled smartphones or smartwatches; • Bluetooth tracking software to track devices owned by other individuals in the vicinity; • Internet browsing history analysis software; • Accelerometers, gyroscopes, or altimeters on smartphones, smartwatches, or other wearable devices, tablets, or computers; • Proximity sensing devices, such as NFC, Wi-Fi, or Bluetooth, especially with enhanced location specificity (proximity to other devices, enhanced location specificity); • Keystroke rate, rhythm, typing style, pressure, or “force” detection (keyboard, touchpad, screen pressure sensor); • Voice analysis (pitch, rhythm, word, and phrase detection), telephone usage, including call duration, dialed numbers, and call duration throughout the day; • Application (“app”) usage, including the specific app used, duration of use, and app usage throughout the day. • Time, in-app analytics (using any in-app features), keyword searches, word and phrase usage (typically applied to word processing, email, messaging, and social media apps, but not limited to these), gait and posture analysis, and shopping history; • Car / driving / cycling style, including steering input, acceleration, deceleration, braking, driving speed, braking and accelerator force, door pressure sensors, and other vehicle sensors; • Home or office sensors that can measure motion, body temperature, TV usage (channels watched, viewing time, eye movements), refrigerator analytics, heating and cooling analytics, and other "smart home" analytics; • Bicycle gauges (pedal force, pedaling rhythm, acceleration, speed, route taken, GPS, altimeter, time spent on a bicycle, etc.), pedometers, gait analysis measurements; and • "Smart clothing," including pants / trousers, underwear, socks, shoes, shirts / T-shirts, gloves, hats / caps / helmets, glasses, watches, smartwatches, wrist and ankle straps, and adhesive patches.

[0097] The pressure analyzer 1 first requests the user's permission to collect behavioral information, and then routinely collects information in the background without interrupting the user.

[0098] The tool can be integrated into a computing device, or used in a standalone external device. If the tool is external, it can be connected to the computing device using any suitable method, such as via cable or wireless Bluetooth connection.

[0099] Cognitive function data indicates stress-related cognitive function measurements performed on individuals within a group.

[0100] Examples of cognitive function measurements include memory test results, reaction time measurement results, and decision-making test results. The accuracy and sensitivity of cognitive function measurements typically increase when more than one cognitive function parameter is measured.

[0101] Cognitive function or performance tests can be online tasks or in the form of interaction with smartwatches, smartphones or other computing devices.

[0102] Literature on the correlation between human cognitive function and stress includes, for example, “Stress Effects on Working Memory, Explicit Memory, and Implicit Memory for Neutral and Emotional Stimuli in Healthy Men,” Mathias Luethi, Beat Meier, and Carmen Sandi, Frontiers of Behavioural Neuroscience, 2008; 2:5.

[0103] The cognitive function data collection tool, Stress Profiling 1, includes the ability to accept input from multiple cognitive function information collection tools. Each cognitive function information collection tool measures one aspect of the user's cognitive function that indicates user stress. Examples of suitable cognitive function information collection tools that can be used in Stress Profiling 1 include, but are not limited to: • software for testing user memory; • software for testing user reaction time; • software for testing user attention, peripheral vision, and comprehension; • software for testing user decision-making ability.

[0104] The processor prompts the user to complete one or more cognitive function tests. If the user agrees to the test, the processor presents a brief cognitive function test. The test should typically be completed quickly, but may take anywhere from 5 seconds to 2 minutes. Memory tests may prompt the user to recall a piece of information at a later time.

[0105] The tool can be integrated into a computing device, or used in a standalone external device. If the tool is external, it can be connected to the computing device using any suitable method, such as via cable or wireless Bluetooth connection.

[0106] Example Example 1 This embodiment is a mobile version of the stress profiler 1, in which, in this example, each individual in a group within a relatively small geographical area operates a smartphone, smartwatch, or tablet computing device to provide relevant individual stress information.

[0107] Specifically, each of the multiple individuals in the group uses a device including a mobile application. Some relevant stress information is collected by the application in the background without requiring any manual input from the user, while the rest requires active user participation.

[0108] As described above, preferably, each individual in the group uses the device to guide them through a self-administered stress test and transmits both stress data and personal data to a stress profiler. An example of such a device is the personal stress profiler described in another patent application filed by the applicant on November 11, 2014, namely Australian Patent Application No. 2014904524. In this way, an individual's stress level is calculated using a smartphone, desktop computer, tablet, or any other suitable connected device such as a smartwatch, smart clothing, nanotechnology sensors, etc.

[0109] Once the score is calculated, it is transmitted to a central server group via a regular communication channel (if available), such as Wi-Fi, mobile or satellite connections and / or via the Internet, and is compared with previously recorded data shared by users (groups, genders, occupations, lifestyles, etc.). Other users' stress levels are also compared in the same way by the central server, and the average overall stress score of the group or population is calculated using overall stress data from multiple users: the stress score of group x over a specific time period (minutes, hours, days, weeks, months, or years) (which can be categorized or defined by geographic location, gender, occupation, age, etc., or finely subcategorized) = a) User a) Stress score over the specified time period + b) User b) Stress score over the specified time period + c) User c) Stress score over the specified time period + ...

[0110] ...continue to add up multiple individuals within the relevant group.

[0111] Divide by the total number of users (i.e. individuals) in the group within the specified time period (a + b + c ... / number of users included in the total) = physical stress score of group X within the specified time period.

[0112] As an example of the above, one group of entities that can use the systems and methods of the present invention for generating profiles of pressure levels and pressure elasticity levels is a discrete geographical location in Cambridge, Massachusetts, United States. In particular, a group relevant to the specific example is the suburbs including the Harvard University campus.

[0113] The group body stress measurement or score in Cambridge, Massachusetts will include the body stress scores of all active users in the suburb (i.e., each of the multiple individuals within the group). The group body stress measurement or score will be continuously measured daily using the connected devices listed above—smartphones, tablets, desktop computers, smartwatches, etc. The data will be transmitted via the internet to a central server through regular communication channels such as Wi-Fi, mobile networks, or other means. These body stress scores are likely to be a very accurate measure, especially for acute or short-term stress.

[0114] Typically, the average physical stress score for the entire population in Cambridge, Massachusetts, is expected to rise at the start of the school year, and then rise again before exams and / or immediately before the end of the term. A significant drop in average physical stress scores is typically expected as summer break begins.

[0115] Within the scope of this invention, the Cambridge, Massachusetts population can be further subdivided into individuals aged 17 to 28. With this "subgroup" of young people (potentially students), it is expected that these data will provide above-average physical stress scores during these time periods.

[0116] Similarly, if a “subgroup” of academic professionals, such as professors and support staff, is selected, then different “group patterns” of stress are expected, most likely peaking at the start of the academic year, but falling below normal exam levels when most academic professionals’ workloads will decrease, and then immediately rising again when academic professionals are under significant pressure to assess their grades.

[0117] These different stress levels can inform university policies, enabling universities to develop stress management initiatives for specific subgroups when most needed, thereby better supporting students and staff and making more judicious use of resources.

[0118] The published "body stress score of group x in a specified time period" score can also be weighted by multiplying the individual or "body stress score of group x in a specified time period" by a weighting factor to take into account the characteristics or variations of the group, or to account for the influence of specific variables, such as seasonal variations, to make the comparison more accurate and / or more useful.

[0119] Continuing with the Cambridge, Massachusetts example above, this is a unique geographical location experiencing extremely cold winters. This can cause physical stress scores to rise unrelated to any workplace-related stress factors during the cold winter months, especially during exceptionally cold, long, or "once-in-a-lifetime" blizzards / storms. To accurately determine the level of workplace stress and the benefit and necessity of interventions, the impact of weather needs to be considered using weighted factors; an increase in physical stress scores for a group during particularly severe weather periods does not necessarily warrant employer attention or intervention.

[0120] As another example of this “weighting,” consider the highly variable geographic locations due to “fruit picking”: seasonal workers bring their own individual physical stress characteristics, which may affect the average physical stress score at that location. A “seasonally adjusted physical stress score” could provide more useful data for individuals considering permanent migration to that location, or for calculating the impact of providing health services or political announcements on overall stress levels.

[0121] This "physical stress score of group x in a specified time period" can then be correlated with other relevant data such as traffic, weather, political announcements, and news to determine the impact of external and environmental events on the stress levels of the entire group or subgroup.

[0122] Let's continue with the example of Cambridge, Massachusetts. If a political announcement were made that a heavily polluting industry had been approved to discharge millions of tons of toxic substances annually into the Charles River, directly upstream of Boston, residents of the Boston area could likely expect unease or stress.

[0123] Seeing this increase in stress levels, potentially affecting over a million people, and its connection to political announcements in several ways could be extremely beneficial. Administrators at Harvard, MIT, and Boston College could understand the stress levels of their staff and potential students, considering it as a factor not solely attributable to university workloads. Furthermore, the government would have data on stress levels in the Boston area that could lead to decreased productivity and increased healthcare costs. This information could provide concrete data for their decision-making processes, something previously impossible; the overall loss of productivity and increased healthcare costs across the region could outweigh the economic benefits of new industries.

[0124] Variations and / or modifications may be made to the described embodiments without departing from the spirit or scope of the invention. Therefore, these embodiments are to be considered illustrative rather than restrictive in all respects.

[0125] Prior art (if any) should not be regarded as an admission that prior art forms part of common knowledge in any jurisdiction.

[0126] In the following claims and the foregoing description of the invention, unless the context otherwise implies an explicit linguistic or necessary meaning, the word “comprise” or variations such as “comprises” or “comprising” are used in an inclusive sense, that is, to specify the presence of the said feature, but not to exclude the presence or addition of other features in various embodiments of the invention.

Claims

1. A method for generating stress level information indicating the stress levels of a group comprising multiple individuals, the method comprising: Receive personal information of each of multiple individuals and individual stress information of each of the multiple individuals via the network; In the processing system, the personal information of each of the plurality of people is used to select the plurality of individuals from the plurality of people to form the group; real-time individual stress information of each of the plurality of individuals is received via the network, wherein the real-time individual stress information of each of the plurality of individuals includes one or more of the following: psychological measurement information of each of the plurality of individuals, physiological information of each of the plurality of individuals, and behavioral information of each of the plurality of individuals; In the processing system, real-time statistical values ​​of the stress level of the group are generated by statistically processing the real-time individual stress information of each of the plurality of individuals in the group. And as more real-time individual stress information is received from each of the plurality of individuals via the network, the real-time statistics are continuously updated; The method further includes: receiving information indicating a real-time stress change event via the network, and associating the real-time statistical value of the stress level of the group with the real-time stress change event; obtaining a group stress index from the association, the group stress index indicating the stress level of the group consisting of the plurality of individuals caused by the real-time stress change event; and notifying at least some of the plurality of individuals of the group stress index, or notifying a third party of the group stress index.

2. The method as defined in claim 1, wherein the real-time individual stress information further includes cognitive function information of each of the plurality of individuals.

3. The method as defined in claim 1, wherein the personal information includes at least one of the following: date of birth information, place of birth information, gender information, ethnicity information, occupation information, postal code information, education information, health insurance coverage information, relationship status information, number of children information, pet information, exercise habits information, dietary habits information, health history information, and information indicating the stress management method currently being used.

4. The method as defined in claim 1, further comprising the step of generating a stress index using the statistical value in the processing system.

5. The method as defined in claim 4, further comprising the step of the processing system sending the pressure index to a plurality of computing devices.

6. The method as defined in claim 5, further comprising the step of the processing system sending the statistical values ​​of the pressure levels of the plurality of individuals to the plurality of computing devices.

7. The method as defined in claim 1, wherein the step of receiving psychometric information of each of the plurality of individuals comprises: Each of the multiple individuals responded to the electronic stress questionnaire.

8. The method as defined in claim 7, wherein the questionnaire is divided into two parts, each part comprising a different set of predefined questions, thereby presenting a second set of questions to the individual based on predetermined criteria related to the answers provided to the first set of questions.

9. The method as defined in claim 1, wherein the step of receiving physiological information of each of the plurality of individuals comprises generating at least one of the following: heart rate information, heart rate variability information, respiratory rate information, respiratory rate variability information, blood pressure information, body movement information, cortisol level information, skin conductivity information, skin temperature information, skin or hair analysis, DNA analysis, blood oxygen saturation information, surface electromyography information, electroencephalography information, blood information, saliva information, skin conductance information, information about chemical substances found on or within the skin, and urine information.

10. The method as defined in claim 1, wherein the step of receiving behavioral information of each of the plurality of individuals comprises at least one of the following steps: generating eye movement information indicating eye movements of the individual; generating location information indicating multiple locations where the individual has previously been; generating proximity device information indicating the presence of multiple devices of multiple people near the individual; generating internet browsing history information of the individual; generating keystroke rate, rhythm, typing style, pressure, or "force" detection information of the individual; generating voice analysis of the individual, including pitch, rhythm, word, and phrase detection information; generating telephone usage analysis of the individual, including call duration, dialed numbers, and... The system generates the following data: time information for the individual's phone call; driving style data for the individual, including steering input, acceleration, deceleration, braking, driving speed, braking and accelerator force, and data from the door pressure sensor; movement and body temperature data for the individual, including TV usage data such as channel viewing, viewing time, and eye movements during viewing; refrigerator analysis data; heating and cooling analysis data; bicycle data for the individual, including pedal force, pedaling rhythm, acceleration, speed, route taken, GPS data, altimeter data, time spent on the bicycle, and pedometer data; pedometer data and gait analysis information for the individual; and application usage information indicating the individual's application usage. Generate media consumption information indicating the individual's media consumption; generate consumption behavior information indicating the individual's consumption behavior; Generate food selection information that indicates multiple food choices made by the individual; Generate social travel information that indicates the individual's social travel activities; Generate vacation information indicating the individual's vacation time.

11. The method as defined in claim 2, wherein the step of receiving cognitive function information of each of the plurality of individuals comprises at least one of the following steps: generating memory function information indicating memory function of each of the plurality of individuals; generating reaction time information indicating reaction time of each of the plurality of individuals; generating attention, peripheral vision, and comprehension of the individuals; and generating decision ability information indicating decision-making ability of each of the plurality of individuals.

12. The method as defined in claim 1, further comprising: The step of generating a stress resilience score indicating the response of each of the plurality of individuals to acute stress; wherein the stress resilience score indicates one or more of the following: the time required for the plurality of individuals to respond to the acute stress event, whether the plurality of individuals responded to the acute stress event, and if so, the level of response exhibited by the plurality of individuals to the acute stress event, and the time taken for the stress information of the plurality of individuals to return to baseline levels after the acute stress period.

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