Patent ID: 12236167

DETAILED DESCRIPTION

Systems and methods of the invention presented herein are described below in the drawings and detailed description. Unless specifically noted, it is intended that the words and phrases herein be given their plain, ordinary, and accustomed meaning to those of ordinary skill in the applicable arts.

In the following description, and for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of the invention. It will be understood, however, by those skilled in the relevant arts, that embodiments of the present invention may be practiced without these specific details. In other instances, known structures and devices are shown and/or discussed more generally in order to avoid obscuring the invention. In many cases, a description of the operation is sufficient to enable one of ordinary skill in the applicable art to implement the various forms of the invention. It should be appreciated that there are many different and alternative configurations, devices and technologies to which the disclosed inventions may be applied. The full scope of the present disclosure is not limited to the examples described below.

FIG.1illustrates embodied stress analysis system100, according to a first embodiment. The system comprises embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, computer140, network150, and one or more communication links160-168. Although a single embodied stress analyzer110, one or more stress monitoring devices120, a single mapping system130, a single computer140, and a single network150are shown and described, embodiments contemplate any number of embodied stress analyzers, stress monitoring devices, mapping systems, computers, and networks, according to particular needs. AlthoughFIG.1illustrates embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140as distinct devices, in other embodiments the functions of embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140may be performed by a single computing device comprising a processor and memory, or networked cloud computing device comprising one or more networked processors and one or more networked memories.

In one embodiment, embodied stress analyzer110comprises server112and database114. As described in more detail below, server112of embodied stress analyzer110comprises one or more modules to, for example, measure and calculate embodied stress of a place and the stress of a locational sequence through a location. Embodiments contemplate designing or altering locations to invoke optimal levels of stress (including, for example, a flow state) according to characteristics of the environment (e.g., crossing an intersection, a factory floor, handling of dangerous materials, environmental hazards, and the like) and according to particular needs.

One or more stress monitoring devices120are electronic devices comprising one or more processors122, memory124, one or more sensors126, and may include any suitable input device, output device, fixed or removable computer-readable storage media, or the like. According to embodiments, one or more stress monitoring devices120comprise one or more electronic devices that measure stress or receive stress measurements from one or more sensors126. Additionally, one or more sensors126of one or more stress monitoring devices120may be located at one or more locations local to, or remote from, the one or more stress monitoring devices120, including, for example, one or more sensors126integrated into one or more stress monitoring devices120and/or one or more sensors126distantly located from one or more stress monitoring devices120and communicatively coupled to the one or more stress monitoring devices120. Sensors126may include sensors coupled to wearable devices of one or more users and configured to detect biometrics and generate a digital signal that indicates, for example, heartbeat, perspiration, voice, eye movement, brain signals, EKG, position, movement, or orientations of body or body parts (including posture), respiration, temperature, and the like. Data received from the one or more sensors may be used to evaluate the current state (e.g., stress) of the user.

One or more stress monitoring devices120may comprise a wearable electronic device capable of monitoring and recording heart rate data or other biometric data. In other embodiments, one or more stress monitoring devices120may be an external location system, such as a radio frequency identification (RFID) system, a light detection and ranging (LIDAR) system, a radio detection and ranging (RADAR) system, or any other external system capable of remotely monitoring and recording heart rate data or other biometric data. In addition, or as an alternative, one or more stress monitoring devices120may comprise, or be communicatively coupled with, a networked communication device, such as, for example, a smartphone, a tablet computer, a wireless device, or the like. One or more stress monitoring devices120may generate a mapping of a recorded stress measurement (or other biometric) by tagging a location associated with a measurement. This may include, for example, a GPS module coupled with one or more stress monitoring devices120that records location data during measurement of the biometric or stress. Embodiments comprise, for example, a wearable electronic device comprising a heartrate monitor that records blood flow or electrical signals of the user and associates the measurements with movement and activity detection and may additionally include, for example, associating user identity data, location data, time data, demographics, and the like. As explained in more detail below, embodied stress analysis system100may use the measurements and associated data mappings to determine, for example, whether a user is oriented toward or away from a particular environmental structure or feature, rate of movement through a location, any waypoints or stops through a location, determination whether a movement or action is in conformity with expected or modeled sequences through an environmental location (e.g., posted directions or other modeled movement or activity in the environment), identify any amount of non-conformity with one or more modeled movements or activities, evaluate progress of movement or activity through a location, and the like.

According to embodiments, mapping system130comprises server132and database134. According to embodiments, one or more modules of server132generates one or more mappings of one or more locations, and provides the one or more mappings to embodied stress analysist110for generating a locational model222(FIG.2) on which to bin the biometrics measured by one or more stress monitoring devices120. In one embodiment, mapping system130generates mappings of environments (e.g., building plans, maps of outdoor spaces, and the like). By way of example only and not by way of limitation, server132of one or more mapping systems130generates a building plan which is utilized by embodied stress analyst110to build locational model222comprising bins by assigning each bin to a room (or other architectural feature) indicated on the building plan received from mapping systems130. In addition, or as an alternative, mapping system120comprises a commercial mapping service (e.g., GOOGLE MAPS commercial mapping service), which generates a map of an outdoor environment and which is then utilized by embodied stress analyst110to generate locational model222, as described in further detail below.

As shown inFIG.1, embodied stress analysis system100operates on one or more computers140that are integral to or separate from the hardware and/or software that support embodied stress analyzer110, one or more stress monitoring devices120, and mapping system130. Embodied stress analysis system100comprising embodied stress analyzer110, one or more stress monitoring devices120, and mapping system130may operate on one or more computers140that are integral to or separate from the hardware and/or software that support embodied stress analyzer110, one or more stress monitoring devices120, and mapping system130. One or more computers140may include any suitable input device142, such as a keypad, mouse, touch screen, microphone, or other device to input information. One or more computers140may also include any suitable output device144, such as, for example, a computer monitor, that may convey information associated with the operation of embodied stress analysis system100, including digital or analog data, visual information, or audio information. Computer140may include fixed or removable computer-readable storage media, including a non-transitory computer readable medium, magnetic computer disks, flash drives, CD-ROM, in-memory device or other suitable media to receive output from and provide input to embodied stress analysis system100.

Computer140may include one or more processors146and associated memory to execute instructions and manipulate information according to the operation of embodied stress analysis system100and any of the methods described herein. One or more processors146may execute an operating system program stored in memory to control the overall operation of computer140. For example, one or more processors146control the reception and transmission of signals within the system. One or more processors146execute other processes and programs resident in memory, such as, for example, registration, identification, communication, and movement of data into or out of the memory, as required by an executing process. In addition, or as an alternative, embodiments contemplate executing the instructions on computer140that cause computer140to perform functions of the method. Further examples may also include articles of manufacture including tangible computer-readable media that have computer-readable instructions encoded thereon, and the instructions may comprise instructions to perform functions of the methods described herein.

In addition, embodied stress analysis system100may comprise a cloud-based computing system having processing and storage devices at one or more locations, local to, or remote from embodied stress analyzer110, one or more stress monitoring devices120, and mapping system130. In addition, each of one or more computers140may be a work station, personal computer (PC), network computer, notebook computer, tablet, personal digital assistant (PDA), cell phone, telephone, smartphone, wireless data port, or any other suitable computing device. In an embodiment, one or more users may be associated with embodied stress analyzer110, one or more stress monitoring devices120, and mapping system130.

In one embodiment, each of embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140may be coupled with network150using communication links160-166, which may be any wireline, wireless, or other link suitable to support data communications between embodied stress analyzer110and network150during operation of embodied stress analysis system100. Although communication links160-166are shown as generally coupling embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140to network150, any of embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140may communicate directly with each other, according to particular needs.

In another embodiment, network150includes the Internet and any appropriate local area networks (LANs), metropolitan area networks (MANs), or wide area networks (WANs) coupling embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140. For example, data may be maintained locally to, or externally of embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140and made available to one or more associated users of embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140using network150or in any other appropriate manner. For example, data may be maintained in a cloud database at one or more locations external to embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140and made available to one or more associated users of embodied stress analyzer110, one or more stress monitoring devices120, mapping system130, and computer140using the cloud or in any other appropriate manner. Those skilled in the art will recognize that the complete structure and operation of network150and other components within embodied stress analysis system100are not depicted or described. Embodiments may be employed in conjunction with known communications networks and other components.

FIG.2illustrates embodied stress analyzer110, stress monitoring device120, and mapping system130of embodied stress analysis system100ofFIG.1in greater detail, according to an embodiment. As disclosed above, embodied stress analyzer110comprises server112and database114. Although embodied stress analyzer110is shown as comprising single server112and single database114, embodiments contemplate any suitable number of servers112or databases114internal to or externally coupled with embodied stress analyzer110.

Server112of embodied stress analyzer110may comprise measuring module202, modeler204, tracking module206, filtering module208, binning module210, analytics module212, and user interface module214. Although server112is illustrated and described as comprising a single measuring module202, modeler204, tracking module206, filtering module208, binning module210, analytics module212, and user interface module214, embodiments contemplate any suitable number or combination of these located at one or more locations, local to, or remote from embodied stress analyzer110, such as on multiple servers112or computers140at any location in embodied stress analysis system100.

Measuring module202stores stress monitoring data received from one or more stress monitoring devices120in database112. According to embodiments, measuring module202receives biometric data from stress monitoring devices and stores the biometric data as stress measurement data220and associates the stress measurement data220with any associated location data224, demographics, roles, user identity, movement, or other like data associated with the stress measurements, as described in further detail herein. Modeler204of embodied stress analyzer110builds locational model222. According to embodiments, modeler204builds locational model222, which is used by binning module210to calculate the embodied stress of a location. In an embodiment, modeler204is utilized by user interface module214to build a model of an environment using user-interactive visual elements to define locations associated with an environment, place, building plan, map, and the like, as described in further detail below.

Tracking module206stores location data received from either one or more wearable stress monitoring devices120in database112or an external location system that can be corroborated to user stress data. According to embodiments, tracking module206receives location information from stress monitoring devices and stores the location data as location data224and associates location data224with any associated stress measurement data220, demographics, roles, user identity, movement, or other like data associated with the location information, as described in further detail herein. In addition, or as an alternative, location data is received from an RFID system or other Real-Time Location System (RTLS).

Filtering module208sorts, modifies, and cleans measurement data220and location data224to generate filtered data226. According to one embodiment, filtering module208cleans measurement data220. In addition or as an alternative, filtering module208sorts measurement data220and location data224according to one or more of user-selected metrics, which may include, but are not limited to: planned usage of a place or environment, role of user within the location, movement of a user within the location, and the like. By way of example only and not by way of limitation, role-based filtering may comprise filtering measurement data220for a hospital embodied stress analysis differently based on stress measurements received from one or more stress monitoring devices120associated with doctors versus measurements from one or more stress monitoring devices120associated with nurses, patients, visitors, and the like. By way of an additional, non-limiting example, filtering module208filters measurement data220according to movements associated with measurements, such as, for example, movement in a particular direction (e.g., with traffic, against traffic, entering a room, exiting a room, passing through a particular passageway or path between locations, and the like). According to embodiments, one or more stress monitoring devices120detect movements, directions, predicted activities and the like with stress and biometric measurements, which are stored with measurement data220.

Binning module210generates binned data228based, at least in part, defined locations within locational model222. In one embodiment, binning module220generates bins of aggregated stress scores for each location (e.g., area, space, landmark, environment, and the like) to be analyzed. Bin allocation by binning module210may be user-defined as based, at least in part, on the type of analysis to be performed, such as, for example, an analyst comprising a hospital administrator may bin rooms of a hospital that have similar designs and function, an analyst comprising an architect may bin rooms that have specific architectural features, an analyst comprising an engineers in charge of designing a roadway may bin data by the various functions of the roadway and adjacent properties (sidewalk, bike path, vehicular lane, green space, and the like), and the like. By way of an additional, non-limiting example, bin allocation by binning module210may be user-defined as based, at least in part, on the type of setting, such as, for example, a setting comprising a hockey arena whose sections may be binned based on expected interactions with other users, groups of users (checked by another player, crowd participants pounding the glass, and the like), and any number of stimuli happening at the venue (lighting, music, event happenings, etc.) and the like.

Analytics module212generates embodied stress analytics230and locational sequence analytics232. Analytics module212generates stress analytics230and locational sequence analytics232which are utilized by user interface module214to display visualizations of stress embodied in a place or of a locational sequence, as described in further detail below. User interface module214of embodied stress analyzer110generates and displays a user interface (UI), such as, for an example, a graphical user interface (GUI), that displays one or more interactive visualizations identifying and quantifying embodied stress analytics230and locational sequence analytics232. According to embodiments, user interface module214displays a GUI comprising interactive graphical elements for selecting locations of locational model222for binning, selecting and applying various filters to selected sets of data from measurement data220, and, in response to (and based at least in part on) the selection, displaying one or more graphical elements identifying embodied stress, biometrics, and other data and analytics, as disclosed herein.

Database114of embodied stress analyzer110may comprise one or more databases or other data storage arrangements at one or more locations, local to, or remote from, server112. Database114may comprise, for example, stress measurement data220, locational model222, location data224, filtered data226, binned data228, embodied stress analytics230, and locational sequence analytics232. Although database114is shown and described as comprising stress measurement data220, locational model222, location data224, filtered data226, binned data228, embodied stress analytics230, and locational sequence analytics232, embodiments contemplate any suitable number or combination of these, located at one or more locations, local to, or remote from, embodied stress analyzer110according to particular needs.

Stress measurement data220may comprise stress measurements using one or more sensors136of one or more stress monitoring devices120. According to embodiments, stress measurement data220comprises biometric data from one or more stress monitoring devices120, stress calculations, and/or any associated location data224, demographics, roles, user identity, movement, or other like data associated with the stress measurements, as described in further detail herein.

Locational model222comprises a digital model of the embodied location for which stress is determined. According to one embodiment, locational model222is built over a map or architectural plan of locations. By way of example only and not by way of limitation, locational model222may represent indoor locations of a building, outdoor environment, and the like.

Location data22comprises data associating stress measurements with a physical location. Location data224may comprise, for example, GPS, cell tower triangulation, Bluetooth, coordinates, distance from a beacon, waypoint, environmental feature, or the like. Filtered data226comprises measurement data220and/or location data224filtered by filtering module208. According to embodiments, filtered data226comprises sorted and/or cleaned data modified according to one or more filters, as disclosed herein.

Binned data228comprises measurement data220, location data224, and/or filtered data226assigned to a location of locational model222. According to embodiments, binned data228is organized according to locations defined in the locational model222set by embodied stress analyzer110. By way of example only, and not by way of limitation, embodied stress analyzer110establishes limits on where the emotional response of place exists, such as, for example, a room of a building, a location along a roadway, or other types of interior and exterior environments.

Embodied stress analytics230comprises stress scores and biometric calculations based, at least in part, on embodied stress of an interior or exterior environment, as described in further detail with embodied stress visualization400ofFIG.4. Locational sequence analytics232comprises stress scores and biometric calculations based, at least in part, on embodied stress of an interior or exterior environment along a locational sequence, as described in further detail with locational sequence stress visualization500ofFIG.5.

As disclosed above, stress monitoring device120comprises processor122, memory124, and sensor126. According to embodiments, embodied stress analyzer110assigns autonomic stress to locations by tracking movement of one or more subjects wearing a one or more stress monitoring devices120comprising sensor126configured to measure biometric data and a location tracker (e.g., GPS tracker, indoor positioning system or other location-based techniques) configured to track location of measured biometrics. Sensor126may measure one or more biometrics (such as, for example, one or more of heart rate, heart rate variability, blood pressure, oxygenation, galvanic response, facial sentiment analysis, and the like). As disclosed in further detail below, biometric data from sensor126comprises subject changes to heart rate calculated using an algorithm and based, at least in part, on heart rate variability, rapidity of change, heart rate fluctuations. In one embodiment, sharp increases or decreases in heart rate fluctuations indicate stress and lower fluctuations combined with lower heart rate, comfort. Accordingly, stress may be measured according to improvements or decline of heart rate fluctuations, and embodied stress analyzer110may determine alterations to the environment based, at least in part, on stress measurements. Embodiments contemplate pooling many measurements of stress from the same, or different, one or more stress monitoring devices120and/or aggregating measurements from all locations within a predetermined or calculated distance from a particular location (i.e., all locations within one foot, five feet, ten feet, or any other distance, according to particular needs). In addition or as an alternative, location-correlated, biometric measurements received from one or more stress monitoring devices120may be augmented by other information development techniques, such as, for example, surveys, interviews, observation, traffic data, and the like.

As disclosed above, mapping system130comprises server132and database134. Although mapping system130is shown as comprising single server132and single database134, embodiments contemplate any suitable number of servers132or databases134internal to or externally coupled with mapping system130.

Server132of mapping system130comprises mapping module240and system interface module242. Although server132is illustrated and described as comprising a single mapping module240and a single system interface module242, embodiments contemplate any suitable number or combination of these located at one or more locations, local to, or remote from mapping system130, such as on multiple servers132or computers140at any location in embodied stress analysis system100.

Mapping module240receives the physical location of one or more stress monitoring devices120from location data224, identifies one or more environments (e.g., building plans, maps of outdoor locations, and the like) stored in mapping data250that correspond to the received physical locations, generates mappings comprising the corresponding environments, and transmits the generated mappings to embodied stress analyzer110. In addition, or as an alternative, mapping module comprises an application server that transmits mapping data250to embodied stress analyzer110.

System interface module242comprises an API that transmits mapping data250between embodied stress analyzer110, one or more stress monitoring devices120, and mapping system130. According to embodiments, system interface module242transmits and receives electronic communication with any number of external sources of data.

Database134of mapping system130comprises mapping data250. Although database134is shown and described as comprising mapping data250, embodiments contemplate any suitable number or combination of data, located at one or more locations, local to, or remote from, embodied stress analyzer110according to particular needs.

Mapping data250comprises any number of blueprints, plans, building plans, architectural layouts, maps, or other layout of an indoor or outdoor environment.

FIG.3illustrates embodied stress analysis method300, according to an embodiment. Embodied stress analysis method300proceeds by one or more activities, which although described in a particular order may be performed in one or more permutations, combinations, orders, or repetitions, according to particular needs.

At activity302, measuring module202receives stress data from sensor126of one or more stress monitoring devices120. As an example, one or more users within a particular location may each be wearing one or more stress monitoring devices120while moving through (such as working in) the location. The one or more stress monitoring devices120monitor and record heart rate information for the one or more users while they move through the location. In other embodiments, the one or more users may have their heart rate and other biometric information monitored and recorded using an external location system, such as an RFID, a LIDAR or a RADAR system.

At activity304, modeler204builds locational model222from mapping data250. Locational model222comprises a model of the location based on data defining the dimensions of the location as well as any subdivision (such as rooms) of the location. For example, if the location is a hospital, locational model222may include floors of the hospital, and rooms present on those floors, indicating entries and exists from those rooms as well as paths from one floor to another (such as stairs or an elevator).

At activity306, tracking module206receives location information for one or more stress monitoring devices120. For example, the location information received form the one or more stress monitoring devices may include a sub-location (such as a room) within the location that the one or more users wearing one or more stress monitoring devices120has passed through, or is currently in.

At activity308, filtering module208filters stress measurement data220and location data224. Stress management data220and location data224are filtered by comparing location data224within to locational model222to see if it should be applied within the bounds of an existing project associated with locational model222, or stored in a general, worldview for locational model222. The data is further filtered to eliminate any anomalies that would prevent the calculation of stress based on our algorithm, such as read errors recorded by one or more stress monitoring devices120.

At activity310, binning module210bins filtered data226to create binned data228. Once stress management data220and location data224have been assigned to a project or view, the data is binned by comparing all points that fall within the confines of a user defined grid that covers the project limits within locational model222. The user defined grid is a scalable variable that allows a user of embodied stress analyzer110to change the view of binned data228in real-time.

At activity312, analytics module212performs analytics on binned data228. As discussed in further detail above, binned data228is analyzed through one of several techniques to determine various levels of stress, such as a minimum stress, an average stress, a maximum stress, stress percentiles, etc. The result of this analysis is an embodied stress of the location corresponding to locational model222. For example, certain rooms of the location may be indicating as “high stress” or “low stress” areas of the location.

At activity314, user interface module214generates visualizations comprising embodied stress analytics230and/or locational sequence analytics232. A formal visualization is developed using the embodied stress of the location. For example, if the location is a floor with rooms, the formal visualization may include a color-coded visualization of a floor-map, with certain colors indicating high stress areas of the floor and other colors indicating low stress areas of the floor.

At activity316, embodied stress analyzer110derives an emotion of place for the location modeled by locational model222. The emotion of place may be derived by reference to the embodied stress of the location determined at activity312. For example, a low stress area or room may be determined to have a “calm” emotion of place while a high stress area or room may be determined to have a “stressful” or “focused” emotion of place. Continuing this example an area or room located between high stress and low stress areas may be determined to have a “recovery” or “ramp-up” emotion of place depending on if traffic is more commonly from the high stress area to the low stress area (a recovery space) or if traffic is more commonly from the low stress area to the high stress area (a ramp-up space).

FIG.4illustrates embodied stress analysis visualization400, according to an embodiment. Embodied stress analysis visualization400comprises modeled locations402a-402i, location labels404a-404f, and location stress scores406a-406f. As disclosed above, locational model222comprises a computer-modeled environment that may be based, at least in part, on a map, building plan, or other model of an environment. In this illustrated example of the embodied stress analysis visualization400, modeled locations402a-402icomprise rooms in a building modeled over a building plan. In this example, each of modeled locations402a-402icomprise a room of the modeled building. Modeled locations402a-402imay be associated with location labels404a-404findicating a name or key to stress scores406a-406fassociated with the modeled locations402a-402i. According to embodiments, modeled locations402a-402icomprise stress scores406a-406fand/or are displayed using different patterns, colors, or visual elements to indicate the embodied stress of modeled locations402a-402i.

According to embodiments, stress scores406a-406fof modeled locations402a-402fare calculated by first aggregating collected individual stress scores, binning the data based on specific location data and locational boundary conditions (in this case a room), and then data is normalized across all collected, binned data to come up with a unique score for a locationally bound place. The visualization can include numeric score of binned stress conditions, or color coded to easily derive visual representations of stress data.

In one embodiment, binning comprises aggregating measurements attributable to a modeled locations402a-402i. By way of example only and not by way of limitation, modeled locations402a-402icomprises a grid overlaid on a building plan, wherein particular coordinates on the grid are associated with a particular modeled location. When data is binned to a particular modeled location402a-402iby falling within the modeled location on the grid, the stress measurement is attributed to the physical location represented by the modeled location. As disclosed above, embodiments contemplate one or more of measurement data220, location data224, and filtered data226assigned to a particular modeled location402a-402ibased, at least in part, on a distance from a particular environmental feature of the analyzed environment. By way of example only, and not by way of limitation, data assigned to a particular modeled location402a-402iof embodied stress visualization400may comprise all data located within a particular room or within a particular distance from a modeled environmental feature.

For an outdoor location mapped to locational model222comprising a grid the location of exhibited stress may be binned to all measurements within a particular distance from a coordinate of the grid, such as, for example, one foot, five feet, ten feet, or any other distance, according to particular needs.

For an indoor environment mapped to locational model222comprising a building plan, the location of exhibited stress is binned according to the walls of a room or other type of architectural feature. For the indoor environment of embodied stress visualization400, the grid of the locational model may not comprise each room corresponding to modeled locations402a-402iis a grid cell. According to embodiments, bounds of a bin are defined by architectural features (such as, for example, walls or other types of architectural edges (e.g., the edge of a sidewalk or the bounds of a cubicle in an open work environment). In addition, or as an alternative, bounds of bins comprise a mathematical abstraction such as, for example, a grid overlay with cells assigned to one or more bins. Embodiments contemplate cells having the same, or different, geometric shapes, which may be user-defined and/or statistically calculated, according to particular needs.

FIG.5illustrates locational sequence analysis visualization500, according to an embodiment. As disclosed above, embodied stress of a location creates an ambient stress level which elicits stress responses in users of the location. Using locational sequence analysis, embodied stress analyzer110calculates the stress embodied in a sequence of locations that elicits a similar stress trend in end users and generates locational sequence analysis visualization500. Locational sequence analysis visualization500provides for planning a sequence of locations that elicits specific stress outcomes in end users, such as, for example, determining how various routings through locations may have a positive or negative effect on a user of the location.

By way of example and not by way of limitation, locational sequence analysis visualization500of the illustrated embodiment comprises various modeled locations402a-402ithat comprise embodied stress, which may be measured according to embodied stress scores404a-404f. After determining an ambient level of stress, the embodied stress per location, locational model222provides for creating locational sequence through particular locations. By varying the sequence and timing of locations along a locational sequence, the locational sequence analysis provides for planning a sequence that causes reduction (locational sequence502), reduction (locational sequence504) and/or maintenance (locational sequence506) of a stress level, according to particular needs. Embodiments contemplate using locational sequence analysis to determine when a respite area is needed or determining if a particular one or more of modeled locations402a-402iis a respite along a locational sequence.

Locational sequence analysis comprises locational sequences502-506. Locational sequences502-506may be user defined in modeler204and/or based, at least in part, on location data224of users as they move through locations. By way of further non-limiting example, embodied stress analyzer110may generate locational sequences502-506by modeling through locational model222, and the locational sequence analysis may generate a predicted trend along each of the one or more locational sequences502-506based on the differences in the measurements of the embodied stress of the locations along its length. For example, moving from a first modeled location402awith a high level of stress, along locational sequence502comprising a neutral embodied stress (hallway) indicated by waypoint510may comprise a high-level of stress along locational sequence502. Moving from waypoint510in hallway to modeled location402fwith a low level of embodied stress along locational sequence506may be associated with a neutral level of stress, and moving along locational sequence504from waypoint512in a low-stress modeled location402fto another low-stress modeled location402eis associated in this example with a low level of stress. Continuing with the illustrated example, based on the locational sequence analysis visualization500, modeled location402ais identified as an elevated stress environment, modeled location402fis identified as an environment that reduces stress, and modeled location402eis identified as a location where a respite-level of stress is achieved. By utilizing the locational sequence analysis, the stress response along locational sequences502-506may be calculated along with determining the amount of change in embodied stress of modeled locations402a-402i(such as, for example, whether one or more of modeled locations402a-402iis a recovery or respite location). In addition, or as an alternative, one or more locational sequences502-506may be associated with a travel time or average speed of travel which modify the amount of stress added to (or subtracted from) one or more locational sequences502-506. In addition, waypoints510-512may be added to one or more locational sequences502-506so that less time spent in a high stress location or more time spent in a low stress location are factored into locational sequence analysis, and differences in travel times between and through a location can be factored into the model of the analysis.

By way of example only and not by way of limitation, locational sequence analysis is utilized in the design of a building, such as, for example, For instance if we monitor stress and derive emotional response for a room used for a highly stressful function and then route that user to a room used for respite, we can trigger lighting or sound interventions that may yield a better individual user response.

In addition, or as an alternative, locational sequence analysis includes outdoor environments, such as, for example, streets, parks, and the like, as disclosed in further detail below.

FIG.6illustrates high-polling heartrate stress analysis chart600, such as from an existing heartrate monitoring system. High-polling heartrate stress analysis chart600comprises heartbeat602(y-axis, millivolt) of a heartbeat over time604(x-axis, seconds). Shaded area610indicates a high-polling, millisecond analysis, and distance612indicates the inter-beat interval (RR interval), which is typically expressed in terms of milliseconds.

Typically, in existing high-polling heartrate stress analysis systems, measurements of stress are based, at least in part, on a calculation relying on autonomic stress from EKG-level data, measuring inter-beat intervals to calculate stress response. Modern wearables (such as, for example, a FITBIT® wearable health monitor, APPLE WATCH® electronic internet-connected watch, and the like) may provide for short burst recording of EKG-level heartrate data to determine a snapshot of stress over a brief amount of time (typically thirty seconds worth of data or less). However, battery life limitations of such wearables allow only for the recording of heartbeat data in short intervals. Existing high-polling heartrate stress analysis systems will poll for heartrate data several times in a single second, which consumes significant battery life for a wearable device. For example, high-polling heartrate stress analysis chart600shows a single polling event for heartbeat data, which takes place within two heartbeats. Existing high polling heartrate stress analysis systems thus cannot accurately track heartbeat data over a longer interval, such as the time it may take to walk from one area of a building to another.

FIG.7illustrates low-polling heartrate stress analysis chart700, according to an embodiment. Low-polling heartrate stress analysis chart700comprises heartbeat602(y-axis, millivolt) of a heartbeat over time604(x-axis, seconds). Shaded area710indicates a low-polling, second-based analysis, and distance712indicates a heartrate calculation based on the number of beats that fall within a second-based time period. In an embodiment, stress monitoring device120utilizes an empathic algorithm, which uses low-polling (e.g., one-second interval recordings) heartrate trends to establish reliable stress response over longer periods of time than high-polling heartrate measurements used by existing heartbeat stress analysis systems. The empathic algorithm is based, at least in part, on slope analysis and rate of change comparison of heartrate. The empathic algorithm provides for more accessible stress data calculation with a fraction of device battery life, which allows for heartbeat data to be tracked over longer intervals compared to existing wearable heartbeat tracking technology. The empathic algorithm may be used to calculate various heart rate variability metrics based on heart rate variability, rapidity of change, heart rate fluctuations or other heart rate variability metrics.

FIG.8illustrates embodied stress of location chart800, according to an embodiment. Stress of location chart800comprises embodied stress of space802on the x-axis and space/location804on the y-axis. As an example only and not by way of limitation, a stress journey810starts in Space A (depicted in Column A), which depicts a very high user stress exhibited. That user then is routed to respite Space C (depicted in Column C), where we observe very low user stress. The user then is routed to Space E (depicted in Column E) where the user might be in a very high stress area or function, but there relative stress is lower because they have spent time in Space C just prior.

FIG.9illustrates stress timeline and recovery chart900, according to an embodiment. Stress timeline and recovery chart900comprises line906representing raw heart rate, line904which represents output from our stress algorithm, and line902which is the historic calculation of heart-rate variability. According to embodiments, stress timeline and recovery chart900further comprises stress event920, stress plateau922, recovery phase begins924and potential new recovery with intervention926.

Embodiments contemplate embodied stress analyzer110algorithmically detecting flow state and/or recovery. In some embodiments, embodied stress analyzer110detects flow state and/or recovery of a locational context, such as, for example, a park, a street, a construction zone, a medical facility, or the like. In addition, or as an alternative, embodied stress analyzer110detects flow state and/or recovery of an individual. According to embodiments, embodied stress analysis system110and embodied stress analysis method300may utilize data collected according to biometric feedback method1000, according to particular needs and as described in further detail below.

FIG.10illustrates an exemplary biometric feedback method1000, according to an embodiment. Biometric feedback method1000proceeds by one or more activities, which although described in a particular order may be performed in one or more permutations, combinations, orders, or repetitions, according to particular needs. A flow diagram illustrating exemplary operation of the biometric feedback method is included inFIG.10with activities1002,1004,1006,1008, and1010, as indicated.

In emotionally stressful situations, the Sympathetic Nervous System automatically accelerates the production of adrenaline, leading to an immediate and involuntary increase in blood and oxygen flows to the brain and muscles. This is called an autonomic response, a form of emotional stress, which is different (and measurably distinguishable) from physical stress. In the following examples, biometric feedback method1000focused on autonomic (emotional) stress (not physical stress) and how factors in the built environment impacted autonomic responses. Embodiments contemplate including, or filtering out, autonomic emotional states that may be described as either good stress (known as eustress—e.g., the thrill of competition) or bad stress (known as distress—e.g., the sense of inability to control stimuli in one's environment), according to particular needs.

The presence or absence of autonomic stress can be detected and measured by analyzing heart rate data. Most consumer-grade fitness sensors capture heart rate data (measured in beats per minute) over a period of time. According to an embodiment, the biometric feedback method1000isolates emotionally-induced stress by filtering out physically-induced stress. This may be done by calculating the individual's baseline heart rate, and applying a mathematical analytical algorithm as described above.

FIG.11illustrates homeostasis and a stress event chart1100, according to an embodiment. According to embodiments, homeostasis and a stress event chart1100illustrates an exemplary heart rate variability of a subject when in homeostasis and when subjected to a stress event, wherein upper chart1102is homeostasis and lower chart1104is a stress event.

FIG.12illustrates exemplary biometric feedback system1200, according to an embodiment. Embodiments provide for digital linkages to move heartrate data from chest strap sensors to the fitness app on a smart phone and then to the fitness app's cloud server. In addition, embodiments access the data from the fitness app's cloud via an application programming interface (API). If the study involves multiple users/subjects (i.e., a sample size greater than 1), the “Stress Score” can also be compared across multiple users/subjects and further averaged to better identify outliers for specific users/subjects and establish broader patterns of stress for a larger userbase. In other words, if the study involves several users/research subjects and locations A and B, the Stress Scores for each user at the same location can be averaged (with or without removing outliers) to develop an average user Stress Score for location A and an average user Stress Score for location B.

FIG.13illustrates a heatmap visualization1300of an exemplary commute route, according to an embodiment. Heat map visualization1300highlights locations of elevated autonomic stress where elevated stress is shown in lighter shading utilizing data logged by a staff member on his daily bicycle commutes to and from the office. Many of these locations are at street intersections requiring negotiations with vehicles, which happened more frequently near the city center. More interestingly, “hot spots” emerged that coincided with the memories of a repaired pothole and an accident along otherwise relatively calm stretches of the commute. For example, at location1302the location of a former pothole still created stress even after the pothole was repaired. The lowest stress was recorded at location1304where there is an abundance of green space and a lack of conflicts. At location1306, the memory of a collision with a car six months ago still creates a successful event at the location of the accident. In the city center, location1308, the stress levels are higher even though the terrain is flatter because the environment is not very user-friendly.

FIG.14illustrates exemplary stress charts1400, according to an embodiment. Stress charts1400indicate measurements of stress may not correlate to physical exertion stressors. The Stress chart is the result of the heart rate fluctuations analysis of the heart rate data. Elevation ascents seem to show some correlation to increases in heart rate while descents correlate to increases in speed. However, the autonomic stress metrics in these locations seem relatively stable which points to successful measurements of autonomic stress based on the context at that location after removal of stress attributable to physical exertion. Conversely, the early stages of the route (in the city center) are on relatively flat terrain but show the highest amounts of autonomic stress. Also, the highest heart rates—all recorded in the second half of the commute—correlate to the lowest autonomic stress levels. Thus, it was observed that the platform was successfully isolating emotional (autonomic) stress from physical stress. The horizontal axis in the charts below refers to time (in seconds from start). In the top graph, the vertical axis refers to the stress score (the stress score being a normalized value provided on a scale of 0-100 of the measurement of the stress response). In the heart rate chart, the vertical axis refers to heart rate in beats per minute. In the elevation chart, the vertical axis refers to elevation above sea level in feet. In the speed chart, the vertical axis refers to speed in miles per hour.

Biometric feedback method1000may be further applied to redesign of the Eastern Parkway in Louisville, Ky. There were a number of pre-design strategies employed to collect information and data, including: 1) an online survey which gathered information from the public about their opinions and impressions of the Parkway; 2) a town-hall-style public forum in which the community could interact with the design team about the Parkway; 3) a walking workshop tour of the Parkway with about 20 members of the group, in which they were able to record answers on iPad surveys at specific points along the Parkway walk; and 4) analysis of various types of third-party data, for example, vehicle crash data. During the “walking workshop” on the Parkway, a small number of users wore chest straps to capture their heart rate data.

FIG.15illustrates “Parkway” heat map visualization1500, according to an embodiment. “Parkway” heat map visualization1500indicates lower stress levels associated with locations where: 1) the sidewalks are further away from the street, or shielded from the street with natural vegetation; and 2) there are heavier tree canopies with mature trees. Conversely, higher stress was associated with locations where: 1) the sidewalks are closer to the street; and 2) there were highly active street intersections and the crossing durations were longer. More particularly, at location1510, some of the lowest levels of stress were located in the areas with abundant landscaping and good separation from vehicles. A favorite section of the parkway is bounded by very stressful access points1512where it is necessary to cross traffic to get to the sidewalk in the median. The western terminus1514of the Parkway merges with a major thoroughfare, with minimal consideration given to pedestrian experience, resulting in very high stress levels.

The stress data appeared to be well-aligned with the other datasets that were collected. Correlation with the crash data was especially interesting, seeing that locations of the highest crash counts coincided with some of the highest recorded stress levels, even in the absence of any actual crash events during the workshop.

According to embodiments, the method for deriving autonomic stress from heart rate variability data provided a useful tool to assess the level of latent stress in a physical environment. In addition, or as an alternative, biometric feedback method maps which ambient settings cause stress and quantifies settings that lead to lower stress and homeostasis. This analysis provides quantifiable data for phenomena that have only been qualifiable up to this point.

According to embodiments, an example of a subject/user using the biometric feedback method follows. The subject begins by recording location and heart rate as the subject moves around outdoor or indoors. The subject walks, bikes, or rides in a car around town or in public space. Alternatively, the subject may move about an indoor space, such as a building. The subjects, for example, may feed the data to the software platform or alternatively the software platform may, for example, automatically retrieve the data. The platform may remove outliers (any bad data where the heart rate monitor may disconnect from the user). The platform may remove duplicate location data (optional, but used where a subject stands still at a certain location for an extended amount of time and forgets to pause his/her recording). The platform may convert the heart rate to heart rate variability. The platform may normalize and average readings across multiple subjects (if multiple subjects are present). The platform may export location data coupled with a stress score which can be plotted on, for example, a map.

In some embodiments, the present disclosure provides a biometric feedback method of ascertaining biometric stress to an environmental condition comprising: activity a: using a plurality of biometric sensors (e.g., at least one sensor worn by each subject) to collect biometric data (e.g., heart rate, heart rate variability, blood pressure, oxygenation, galvanic response, facial sentiment analysis, and/or eye movement) over time from a plurality of subjects while the subjects move about a plurality of locations; activity b: using a plurality of location sensors to track the locations of the plurality of subjects over time while the subjects move about the plurality of locations, at least some of said subjects moving about at least partially overlapping locations (e.g., coming within five feet of the same location so that each location has readings from more than one subject); and activity c: grouping/segregating/sorting the biometric data, with or without filtering the data, by location (e.g., to assign a biometric score to each location).

In addition to, or as an alternative, the biometric method may comprise one or more of the following embodiments: (1) biometric data comprises heart rate data of the respective subjects over time; (2) the biometric feedback method further comprises the activity of filtering out physically-induced stress (so that the system only measures autonomic stress for each location); (3) the activity of filtering out physically-induced stress occurs prior to grouping/segregating/sorting the biometric data by location; (4) the biometric data collected in activity a comprises the heart rate data of the respective subjects over time and wherein the method further comprises applying an algorithm to the biometric data to calculate heart rate variability over time for the respective subjects (e.g., by calculating the baseline heart rate of each respective subject within the plurality of subjects and applying the root mean square of the successive differences to the biometric data); (5) activity c further comprises displaying the biometric data segregated/sorted by location on an electronic screen (e.g., a computer screen); (6) activity c further comprises displaying the biometric data segregated/sorted by location and a map on an electronic screen; (7) using the biometric feedback system in an outdoor or indoor environment (thus, the term “map” as used herein includes, for example, reference maps as well as floorplans); (8) the biometric feedback method further comprises the activity of filtering the biometric data (e.g., to remove occasions where the user was standing still or the sensor fell off the subject); (9) the biometric data comprises data about one or more of heart rate, heart rate variability, blood pressure, oxygenation, galvanic response, facial sentiment analysis, and/or eye movement, etc.; (10) the biometric sensor and the location sensor are located on a wearable (e.g., watch or other wrist strap, arm band, chest strap, etc.); (11) the biometric method further comprises activity d: assigning a biometric stress score to each of the plurality of locations; (12) the biometric sensor comprises a chest strap, arm band, watch or other wrist strap or other wearable configured to measure the subjects' heart rates; (13) the location sensor is a GPS tracker, indoor positioning system, or a device that employs other location based techniques; (14) the biometric method further comprises using one or more power sources (e.g., a battery) to power the location sensor and the biometric sensor and the location sensor and biometric sensor are electronic; (15) the biometric method further comprises activity e: altering the environment at a location (e.g., adding trees, a sidewalk, adding width to a street, modifying architectural details, installing art, rearranging furniture, or changing lighting in response to a high stress reading); (16) two or more biometric sensors comprise two or more heart rate monitors; (17) two or more biometric sensors and the two or more location sensors may be located in different devices; (18) these different devices also record temporal data along with the biometric data or location data; (19) temporal data may be used to group/associate the biometric data with the corresponding location data at the same time interval; (20) a GPS unit may record a subject's location at time 1, and a wearable device worn by a user may record the subject's heart rate at time 1; (21) the biometric data and location data may be merged, and the location at time 1 and the heart rate at time 1 may be grouped together.

In still further embodiments, the present disclosure provides a method of assigning autonomic stress to a location comprising: a) using a plurality of heart rate monitors to collect heart rate data from a plurality of subjects over time while the subjects move about a plurality of locations, each subject wearing a heart rate monitor; b) using a plurality of location sensors to track the location of the plurality of subjects over time while the subjects move about the plurality of locations, at least some of said subjects at least partially overlapping locations; c) applying an algorithm to the heart rate data for each subject to determine heart rate variability for each subject; and d) grouping/segregating/sorting heart rate variability by location. In addition, or as an alternative, this method further comprises displaying said heart rate variability for each location on an electronic screen; and/or displaying said heart rate variability for each location together with a map on an electronic screen.

In still further embodiments, the present disclosure provides a method of assigning autonomic stress to a location comprising: a) using a plurality of heart rate monitors to collect heart rate data from a plurality of subjects over time while the subjects move about a plurality of locations, each subject wearing a heart rate monitor; b) using a plurality of location sensors to track the location of the plurality of subjects over time while the subjects move about the plurality of locations, at least some of said subjects at least partially overlapping locations; c) filtering out physically-induced stress in the heart rate data, said activity of filtering out physically induced stress comprising calculating each subject's baseline heart rate and applying an algorithm comprising root mean square of the successive differences to the heart rate data; and d) grouping/segregating/sorting the filtered heart rate data by location.

Optionally, the method further comprises: e) after activity d), displaying on an electronic screen autonomic stress levels for each of the plurality of locations.

In still further embodiments, the present disclosure provides a method of assigning autonomic stress to a location comprising: a) using a plurality of heart rate monitors to collect heart rate data from a plurality of subjects over time while the subjects move about a plurality of locations, each subject wearing a heart rate monitor; b) using a plurality of location sensors to track the location of the plurality of subjects over time while the subjects move about the plurality of locations, at least some of said subjects move about at least partially overlapping locations; c) grouping/segregating/sorting the heart rate data based on location and filtering out physically-induced stress from the heart rate data, said activity of filtering out physically-induced stress comprising calculating each subject's heart rate variability and applying an algorithm comprising root mean square of the successive differences to the heart rate data; and d) displaying on an electronic display screen autonomic stress levels for the plurality of locations based, at least in part, on activity c).

In still further embodiments, the present disclosure provides a method of assigning a biometric stress score to a location comprising: a) using at least one biometric sensor and at least one location sensor to simultaneously collect biometric data and location data for at least one subject over time as the at least one subject moves about a plurality of locations; and b) using the biometric data and the location data, with or without filtering the biometric data, to assign a biometric stress score to some or all of the plurality of locations.

Optionally, in activity b), the biometric data is filtered to remove physical-induced stress. Optionally, the method further comprises the activity of displaying the biometric stress scores on an electronic screen (e.g., optionally with a map).

In still further embodiments, the present disclosure provides a method of assigning autonomic stress to a location comprising: a) using a plurality of heart rate monitors and a plurality of location sensors to simultaneously collect heart rate data and location data for a plurality of subjects over time as the plurality of subjects move about a plurality of locations, each subject wearing a heart rate monitor; b) processing the heart rate data and the location data of each subject to assign a biometric stress score to some or all of the plurality of locations for each subject; and c) for each location, combining (e.g., averaging with or without removing outliers) the subject-level biometric stress scores to determine a cumulative biometric stress score for each location.

Optionally, the method further comprises the activity of displaying the cumulative biometric stress scores on an electronic screen (e.g., optionally with a map). Optionally, activity b) comprises applying an algorithm comprising root mean square of the successive differences to the heart rate data to filter out physically-induced stress.

In still further embodiments, the present disclosure provides a method of ascertaining biometric stress to an environmental condition comprising: a) presenting images of different locations or interactive 3D models on an electronic display to one or more subjects through virtual or augmented reality over time (e.g., through a head-mounted display worn by the subjects); b) using a plurality of biometric sensors to collect biometric data (e.g., heart rate, heart rate variability, blood pressure, oxygenation, galvanic response, facial sentiment analysis, and/or eye movement) over time from the one or more subjects while the subjects are presented the images; and c) grouping/segregating/sorting the biometric data, with or without filtering the data, by location (e.g., to assign a biometric score to each presented location).

Reference in the foregoing specification to “one embodiment”, “an embodiment”, or “another embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

While the exemplary embodiments have been shown and described, it will be understood that various changes and modifications to the foregoing embodiments may become apparent to those skilled in the art without departing from the spirit and scope of the present invention.