WO2016040069A1

WO2016040069A1 - Semantic integration of wearable sensors into professional healthcare

Info

Publication number: WO2016040069A1
Application number: PCT/US2015/048087
Authority: WO
Inventors: John Hodges; Mareike KRITZLER; Florian Michahelles
Original assignee: Siemens Aktiengesellschaft; Siemens Corporation
Priority date: 2014-09-10
Filing date: 2015-09-02
Publication date: 2016-03-17

Abstract

Technical solutions are described for semantic integration of wearable sensors into professional healthcare. One general aspect includes a method that includes receiving, by a communication interface, a disease identifier associated with a patient. The method also includes identifying, by an analysis unit, a measurement identifier based on the disease identifier, where the measurement identifier identifies a health parameter of the patient that is to be monitored. The method also includes receiving, by the communication interface, an anatomy identifier that identifies a body- part of the patient to be used to monitor the health parameter. The method also includes identifying, by a device search unit, a wearable device to monitor the health parameter of the patient at the body-part based on the measurement identifier and the anatomy identifier. Other aspects and examples are also described.

Description

SEMANTIC INTEGRATION OF WEARABLE SENSORS INTO PROFESSIONAL

HEALTHCARE

DOMESTIC PRIORITY

[1] This application claims the benefit of U.S. Provisional Application No. 62/048,319, filed September 10, 2014, the contents of which are hereby incorporated in their entirety.

BACKGROUND

[2] The present application relates to search technology, and particularly to integration of a semantic model of wearable devices with semantic models for sensors, physical properties, and medical information (diseases, symptoms, anatomy) to facilitate a medical professional to access integrated information such as to identify and select a device for use by a patient.

[3] Currently, there are a large number of devices available to monitor one or more health parameters in humans. Such devices use one or more sensors to monitor the corresponding health parameters, for example heart rate, blood glucose, blood pressure, temperature, and various others that monitor bodily functions. Further, there are several 'wearable' devices available that include such sensors, and which a person can continuously wear to monitor the corresponding health parameters.

SUMMARY

[4] In one embodiment, a method that includes receiving, by a communication interface, a disease identifier associated with a patient is provided. The method also includes identifying, by an analysis unit, a measurement identifier based on the disease identifier, where the measurement identifier identifies a health parameter of the patient that is to be monitored. The method also includes receiving, by the communication interface, an anatomy identifier that identifies a body-part of the patient to be used to monitor the health parameter. The method also includes identifying, by a device search unit, a wearable device to monitor the health parameter of the patient at the body-part based on the measurement identifier and the anatomy identifier.

[5] In another embodiment, a system that includes an analysis server that identifies a health parameter of a patient to monitor during an activity based on a history of the patient is provided. The system further includes a wearable device server configured to identify a list of wearable devices in response to receipt of an identifier of the health parameter from the analysis server, wherein the wearable devices in the list monitor the health parameter. The system further includes a client device configured to display the list of wearable devices. Accordingly, the system disambiguates or filters history information to support visualization of relevant device information.

[6] Another embodiment includes a product comprising non-transitory computer readable storage medium that contains computer executable instructions. The non-transitory computer readable storage medium includes instructions to receive an identifier of an activity that is to be performed by a patient. The non-transitory computer readable storage medium includes instructions to determine a health parameter of the patient to monitor during the activity. The non-transitory computer readable storage medium includes instructions to receive a selection of a body-part to be used to monitor the health parameter. The non-transitory computer readable storage medium further includes instructions to identify a wearable device that comprises a sensor to monitor the health parameter using the body-part selected.

BRIEF DESCRIPTION OF THE DRAWINGS

[7] The examples described throughout the present document may be better understood with reference to the following drawings and descriptions. The components in the figures are not necessarily to scale. Moreover, in the figures, like -referenced numerals designate corresponding parts throughout the different views.

[8] Figure 1 illustrates a system to identify a wearable device for a patient in accordance with an exemplary embodiment.

[9] Figure 2 illustrates an example analysis server in accordance with an exemplary embodiment.

[10] Figure 3 illustrates a semantic network that integrates the information in the various ontologies in accordance with an exemplary embodiment.

[11] Figure 4 illustrates a flowchart of example logic that may be implemented by a system to identify a wearable device in accordance with an exemplary embodiment.

[12] Figure 5 illustrates an example search space generated to identify a wearable device in accordance with an exemplary embodiment.

[13] Figure 6 illustrates an example of part of the search interface in accordance with an exemplary embodiment.

DETAILED DESCRIPTION

[14] Wearable devices come in various shapes and sizes, and have a variety of functions.

Further, wearable devices include one or more sensors that monitor one or more health parameters. For example, a wearable device may include a first sensor that senses a heart rate health parameter and a second sensor that senses a blood pressure health parameter. Thus, a sensor in the wearable device is useful to monitor a health parameter. Monitoring the health parameter may be useful to identify symptoms based on values of the health parameter, and diseases based on symptoms.

[15] Further, a person with an existing condition, such as disease or any other medical condition that calls for monitoring particular health parameters may benefit from continuous monitoring of the health parameters using one or more wearable devices. For example, a diabetic may benefit from using a wearable device that includes a sensor that monitors blood glucose level. In addition, monitoring the health parameters may be even more critical for the person with known condition when the person is doing a strenuous or non-routine activity, such as training for a marathon, a hike, a swim, or any other activity that may cause the pre-existing condition to flare up. Thus, identifying one or more sensors that the person would benefit from using is important. Because of the availability of such a vast number of wearable devices, identifying the correct wearable device is a challenging task. For example, two wearable devices might both measure steps taken. Further, one wearable device may be redundant with another wearable device. Further, one wearable device may not be compatible with another wearable device. For example, two sensors may transmit using wavelengths that are not recommended to be used together, or two sensors, used together, may generate heat, or microwave radiation, or any other side effects above a prescribed threshold. Other examples of incompatibility of sensors are possible. [16] Technical solutions to solve the above described technical problem of identifying a wearable device for a person given predetermined constraints are disclosed herein. The constraints may be based on any related information type. For instance, a medical history of the person, a disease the person might have, a symptom the person might exhibit, a part of the body where a disease symptom may manifest itself, a part of the body that is to be used for the wearable device, a cost, a compatibility with another device, a quantity being measured, a unit of measure, an insurance policy, or any other constraint. In addition, the technical solutions facilitate a medical professional to navigate the information in any direction. Traditional databases force a particular point of view (for example, wearable devices), the technical solutions facilitate the medical professional to navigate the data from any starting point (such as symptom, medical history, wearable device) and in any direction according to a goal. Thus, in an example, the medical professional may determine what quantities and measurements are obtainable for a particular patient based on the patient's use of a specific wearable device(s). Alternatively or in addition, the medical professional may determine a wearable device to recommend to a patient based on his/her medical history. To this end, the examples described throughout the present document facilitate an integrated semantic search of wearable devices based on properties of wearable devices that may be useful based on symptoms, diseases, body parts, and other constraints. The examples use local or distributed models and repositories that may contain data useful to identify the wearable devices.

[17] The examples discussed herein include a semantic storage architecture in which combinations of multiple health-related information models/data are integrated into an aggregated semantic storage mechanism. In an example, the information contained in the different models may be used to select a wearable device for a patient. For example, the storage mechanism includes information sources, such as a semantic model, a database, or the like about diseases, symptoms and other medical or appropriate information. The wearable device may be identified based on the information sources using semantic dependencies and a semantic search based on the semantic dependencies. For example, a semantic dependency may include a layered association of models such as, but not limited to diseases, body parts, and types of

measurements. In an example, a medical professional such as a physician, a physician's assistance, a nurse, or any other person enters, via a user interface, selects among attributes significant to a query relating to a patient. For example, the attributes may include a symptom, an insurance policy, a patient, or any other relevant model attribute. In an example, as described herein, the system includes a model of symptoms associated with a separate model of diseases. In response to the selection of attributes by the medical professional, filtered information relating to the input attributes (such as, potential diseases, conditions, wearable devices, patient anatomy, and any other relevant information) is displayed via the user interface. The displayed data is subsequently used to diagnose the patient's condition and/or monitor the patient's treatment and select the wearable device to help monitor the patient's condition.

[18] Thus, the technical solutions facilitate more than a simple web browsing experience.

Web browsing is textual, typically based on a 'search string' that implies meaning but carries none. The technical solutions described herein facilitate performing a semantic search based on model attributes that are constituents of a semantic model. The examples of the technical solutions describe using attributes such as an insurance policy identifier, or a patient identifier for identifying relationships among insurance policy, patients, and other data and further determining recommendations for the patient according to his/her particular case based on the semantic model. In another example, the technical solutions facilitate performing a semantic search without specific identifiers, rather based on a type of policy or a person's age or other attributes.

[19] Figure 1 illustrates a system 100 to identify a wearable device for a patient. The system 100 includes a client device 110, a semantic information data server 120, an insurance data server 130, a medical data server 150, and an analysis server 140. The system 100 may include other components not illustrated in Figure 1.

[20] The client device 110 is a communication device that requests identification of a wearable device based on one or more constraints. For example, the medical professional, via the client device 1 10 specifies selection criteria for the wearable device. The selection criteria may be one or more of a predetermined cost threshold, a brand, a weight, a compatibility with the patient's computer, or any other attribute of the wearable device. For example, the selection criteria may include the wearable device to be of a FITBrT™ brand and compatible with an IPHONE^{1 M} that the patient uses. Alternatively or in addition, the selection criteria may specify the wearable device to be below 200 USD. Alternatively or in addition, the selection criteria may specify that the wearable device be below 100 GRAMS and include a strap that is less than 7 INCHES long. In another example, the medical professional using the semantic search, may instruct the system to identify a desired wearable device to measure heart rate in beats/min and be wearable on the ankle and is covered by policy type XYZ from company FOO. For example, the client device 1 10 may be a computer, such as a desktop, a laptop, a tablet or any other computer. Alternatively, the client device 1 10 may be a smart phone, a personal digital assistant, a phone, a media player device, or any other communication device that is capable to communicate over a communication network. The client device 110 includes hardware and electronic circuitry such as a user interface 1 12. The user interface 1 12 includes hardware such as electronic circuitry to receive input and provide output. The client device 1 10 includes additional hardware that is not illustrated, such as hardware to communicate with the other components of the system 100.

[21] The user interface 112 receives and conveys information during identification of the wearable device. The user interface 1 12 may include a display, a speaker, a touch screen, a keyboard, a mouse, or any other input/output device. For example, the user interface 1 12 may provide the constraints within which to identify the wearable device. The user interface 1 12 may accept the constraints as text, audio, images, gestures, or any other input format. The user interface 1 12 may additionally convey result of the identification process. For example, the user interface 1 12 may display the result of the identification process. Alternatively or in addition, the user interface 1 12 may provide the results in audible format. For example, the client device 1 10 may communicate the request for the identification of the wearable device to the analysis server 140.

[22] The analysis server 140 is a server computer that analyzes the request for

identification transmitted by the client device 1 10. The analysis server 140, in response, communicates a result of the identification to the client device 1 10. The analysis server identifies the wearable device according to the request based on information from the semantic information server 120, the insurance data server 130, the medical data server 150, or other information servers.

[23] The semantic information server 120 is a data server that includes semantic models for various ontologies. The information data server 120 provides a service to access a collection of possibly distributed ontologies and vocabularies. An ontology refers both to classes and instances. Although, examples herein describes wearables and symptoms, the system 100 may include and use fewer or additional ontologies/repositories in other examples. For example, the insurance policy information and the medical history information may be additional ontologies accessed by the semantic information server 120.

[24] The ontologies may include or reference upper (or foundational) ontologies and lower

(target, or domain) ontologies. For example, the semantic information server 120 accesses a quantities, units, dimensions, and data types (QUDT) set of ontologies 121 , a wearable devices ontology 123, a human anatomy ontology 125 (such as FMA), a symptom ontology 127 (such as SYMP), and a human disease ontology 129 (such as DOID). The ontologies include but are not limited to biological and biomedical ontologies. For example, ontologies that model patient medical data or insurance information could reside or be referenced by the semantic information server 120. The ontologies represent respective information in a machine parseable and navigable manner. For example, the QUDT ontology specifies quantities, units, dimensions, data types, enumerations, and other data structures using precise semantically grounded specifications in an ontology model in machine -processable representations. The wearable device ontology 123, which makes use of the Semantic Sensor Network (SSN) ontology, represents sensors, observations, and other attributes of wearable devices. The human anatomy ontology 125, such as Foundational Model of Anatomy (FMA) represents classes or types and relationships for symbolic representation of the phenotypic structure of the human body. The symptoms ontology 127, such as the SYMP ontology, is a representation of symptoms as perceived change in function, sensation, or appearance reported by a patient indicative of a disease. The human disease ontology 129 is a hierarchical controlled vocabulary for human disease representation, such as the DOID ontology.

[25] The semantic information server 120 may include other ontologies that are not illustrated. Although specific examples of ontologies are illustrated, the ontologies used may be different in other examples. In other examples, the information server 120 may access the one or more ontologies from respective servers separate from the information server 120. [26] The insurance data server 130 may be a data server that provides access to insurance related data. For example, the insurance data server 130 may be a data server provided by an insurance provider. The insurance data server 130 includes patient insurance data 132 such as insurance policy details of the patient for whom the client device communicates the request to identify the wearable device. In response to a request from the analysis server 140, the insurance data server provides the patient insurance data 132 to the analysis server 140. The insurance data server identifies the patient insurance data 132 based on a patient identifier such as the patient's name, username, social security number, insurance policy number, or any other criteria. The patient insurance data 132 includes a predetermined cost threshold that the insurance policy covers for the patient for the wearable device. Alternatively or in addition, the patient insurance data 132 includes a list of wearable devices that the insurance policy covers for the patient. In an example, the wearable devices that are covered by the insurance policy may be part of a information source, such as a server, a database, a model, or a table, that is separate from the information source of the insurance policy. The patient insurance data 132 may include additional information. The patient insurance information maps to the ontologies in the semantic information server 120.

[27] The medical data server 150 may be a data server that provides access to the patient's medical data. For example, the medical data server 150 is an electronic medical data repository maintained by the patient's health provider. The medical data server 150 includes the patient's medical history 152. In response to a request from the analysis server 140, the medical data server 150 provides the patient medical history 152 to the analysis server 140. The medical data server 150 identifies the patient medical history 152 based on a patient identifier such as the patient's name, usemame, social security number, insurance policy number, or any other criteria. The patient medical history 152 includes diseases, symptoms and other pre-existing conditions of the patient. For example, the patient medical history may identify that the patient suffers from diabetes, asthma, or any other condition. The patient medical history 152 may further identify wearable devices that the patient is already using. For example, if the patient is a diabetic, s/he may already be using a blood glucose monitor. The patient medical history 152 may include additional information. The patient medical information maps to the ontologies in the semantic information server 120. [28] In another example, the data servers described above may be part of a single data server or any other possible combination different from as illustrated. For example, the analysis server 140 and the medical data server 150 may be one data server in communication with the other components of the system. Any other combination is possible. Further yet, the data servers each may include more than a single data server. For example, the semantic information server 120 may be a distributed server with each separate data server in the distributed server including a respective ontology. Other combinations are possible. In yet another example, the client device 1 10, and the analysis server 140 may be one and the same device. Further yet, as illustrated in Figure 1 , the information from one or more of the data servers may be integrated to identify the relationships in the ontologies and vocabularies. For example, each of the a quantities, units, dimensions, and data types set of ontologies 121 , a wearable devices ontology 123, a human anatomy ontology 125, a symptom ontology 127, and a human disease ontology 129, may be integrated with the insurance data server 130 and the medical data server 150. The information models of the various information sources are part of the semantic information server 120, thus facilitating integration of the data from the various information sources.

[29] Figure 2 illustrates an example analysis server 140. The analysis server 140 includes an analysis unit 210, a device search unit 220, a processor 230, a memory 240, and a request receiver 250. The components of the analysis server 140 may communicate with each other. In other examples, the analysis server 140 may include different components.

[30] The processor 230 is a central processor of the analysis server 140 responsible for execution of an operating system, control instructions, and applications installed on the analysis server 140. The processor 230 may be one or more devices operable to execute logic. The logic may include computer executable instructions or computer code embodied in the memory 240 or in other memory that when executed by the processor 230, cause the processor 230 to perform the features implemented by the logic. The computer code may include instructions executable with the processor 230. The computer code may include embedded logic. The computer code may be written in any computer language now known or later discovered, such as C++, C#, Java, Pascal, Visual Basic, Perl, HyperText Markup Language (HTML), JavaScript, assembly language, shell script, or any combination thereof. The computer code may include source code and/or compiled code. The processor 230 may be a general processor, central processing unit, server, application specific integrated circuit (ASIC), digital signal processor, field programmable gate array (FPGA), digital circuit, analog circuit, or combinations thereof.

[31] The memory 240 is non-transitory computer storage medium. The memory 240 may be DRAM, SRAM, Flash, or any other type of memory or a combination thereof. The memory 240 may store control instructions and applications executable by the processor 230. The memory 240 may contain other data such as images, videos, documents, spreadsheets, audio files, and other data that may be associated with operation of the analysis server 140.

[32] The communication interface 250 receives the request to identify some desirable information, for example, the wearable device for the patient. The communication interface 250 receives the request from the client device 1 10 via a network communication interface 254. Alternatively, the communication interface 250 receives the request from a user via the user interface 252. The communication interface 250, in addition, communicates information to and from the other data servers described throughout the present document. The network

communication interface 254 communicates using wired and/or wireless communication. The user interface 252 may include a display, a speaker, a touch screen, a keyboard, a mouse, or any other input/output device.

[33] The analysis unit 210 and the device search unit 220 are subsystems of the analysis server 140. The subsystems are circuitry, such as processor, memory, communication interfaces, integrated circuits, antennas, resistors, capacitors, and any other hardware components. The subsystems may also include software. For example, the subsystems may include instructions and/or data that may be stored on the memory 240. The instructions and/or data may control operations of the subsystems. The instructions may be executable by the processor 230.

[34] The analysis unit 210 integrates the semantic information of the ontologies available via the semantic information server 120. For example, the analysis unit 210 maps the ontologies using semantic distance, inverse semantic distance, semantic similarity, or any other technique. For example, the analysis unit 210 may use parse and identify similarities between elements in the ontologies based on the Web Ontology Language (OWL). Thus, the analysis unit 210 combines data or information from multiple heterogeneous sources, namely the ontologies and vocabularies accessible via the semantic information server 120. In one example, the analysis unit 210 may use the wearable devices ontology 123 as a reference model for the other ontologies and maps the structure of all the ontologies based on the wearable devices ontology 123. In other examples, the analysis unit 210 may use any other ontology as the reference model.

[35] Figure 3 illustrates a semantic network 300 that integrates the information in the various ontologies. The semantic network 300 illustrated in Figure 3 identifies relations among the several pieces of information contained in the ontologies. The semantic network 300 illustrates identifying associations between attributes of a wearable device (with prefixes representing Universal Resource Identifiers - URIs - such as ssn, ssf, and wearable -prop) and attributes from other ontologies such as a body-part (prefixed with fma), a symptom (prefixed with symp), and a QUDT entry (prefixed with qudt). The semantic network 300 may be stored in the memory 240.

[36] The device search unit 220 identifies the wearable device in response to the request to identify the wearable device. The device search unit 220 identifies the wearable device based on the semantic network 300. The device search unit 220 may identify the wearable device further based on the patient insurance data 132, patient medical history 152, or other information/data. In an example, in response to a semantic query, without serialization of the query, the device search unit 220 specifies the logical relationships of the returned set of results. For example, the device search unit 220 determines and specifies the aggregate information to identify a device (or devices) given a disease. In another example, the device search unit 220 may serialize the query and use logic associated with each table, row, or record to return the information queried.

[37] Figure 4 illustrates a flowchart of example logic that may be implemented by the system 100 to identify the wearable device. For example, a medical professional, such as a physician, might be interested in the relationship between symptoms and measurements as they relate to wearable devices. For example, a patient might indicate to the medical professional that s he plans to undertake running as an activity. Accordingly, the medical professional may input a query to identify a wearable device that the patient may benefit from using, as shown at block 505. In exemplary embodiments, the communication interface 250 receives the query. In an example, the analysis unit 210, in response to the query generates a search space related to the specific scenario, as shown at block 510. The search space is based on an integration of various models, taxonomies, and repositories relating to a specific sample scenario.

[38] Figure 5 illustrates an example search space 600. The search space illustrated is specific to the above example scenario of a patient undertaking running as an activity. In the illustrated search space 600, the rounded rectangles refer to models, the cylinders to repositories of taxonomies and data, white rectangles to instances of those repositories, and arcs between these items as the relationships constructed by the analysis unit 210. Figure 5 illustrates an information pathway that associates a specific disease with its symptoms, quantities, and devices to locate a heart rate monitor.

[39] The analysis unit 210 identifies the patient based on the patient identifier and accesses the patient's insurance data 132 and medical history 152, as shown at block 515. The analysis unit 210 may identify a pre-existing condition 605 of the patient according to this data, as shown at block 520. For example, the analysis unit 210 identifies that the patient is a diabetic. The analysis unit 210 further determines symptoms 610 associated with the pre-existing condition, as shown at block 525. Alternatively or in addition, the analysis unit determines symptoms to monitor according to other criteria, such as age, weight, or any other information input by the medical professional. The analysis unit 210 further determines measurement quantities 615 associated with the symptoms to monitor, as shown at block 530. For example, the analysis unit 210, in the above scenario may determine that for the diabetic patient, blood glucose or heart rate may be quantities to monitor. The medical professional specifies one or more body parts 620 that the patient may prefer to use for monitoring the quantities. The device search unit 220 identifies the wearable device 625 that satisfies the constraints provided by the medical professional and identified by the device search unit 220 based on the patient's medical history 152 and insurance data 132.

[40] For example, the device search unit 220 uses the patient insurance data 132 for additional constraints. For example, the patient insurance data 132 identifies a list of wearable devices that the patient's insurance covers or a predetermined cost threshold for the wearable device 625, as shown at block 535. The predetermined cost threshold may also be provided by the medical professional during input. In another example, the analysis unit 210 adds constraints for the device search unit 220 based on another wearable device that the patient may already be using. For example, if the patient uses a blood glucose monitor that is attached to a certain body part, the analysis unit 210 may constrain the device search unit 220 to identify the wearable device 625 that does not attach to the same certain body part. Alternatively or in addition, the analysis unit 210 specifies compatibility related constraints to the device search unit 220, as shown at block 540. For example, if the other wearable device that the patient already uses operates at a certain wavelength or emanates a certain heat, the device search unit 220 may be constrained to determining the wearable device 625 that operates at a wavelength compatible with the existing wearable device. Thus, the wearable device 625 measures the quantities 615 using the body part 620 for the symptoms 610 for the disease 605. The wearable device 625 satisfies additional constraints such as cost constraints and compatibility constraints.

[41] The analysis server 140 displays the identified wearable device 625. Alternatively or in addition, the device search unit 220 may identify more than one wearable device 625 that satisfy the constraints. The analysis server 140 may display a list of the wearable devices 625 identified. Additionally or alternatively, the analysis server 140 facilitates the medical professional to navigate the search space 600 using a search interface to.

[42] Figure 6 illustrates an example of the search interface 700. In the example search interface 700, a first interface element 710 provides a hierarchical decomposition of the items that are being searched; in this case the wearable devices. A faceted search area 720 is also shown. The faceted search area 720 facilitates the medical professional to select constraining filters on related characteristics. For example, the professional might be interested in looking for wearable devices 625 that attach to a specific body part 620, such as the arm and that measure heart rate 615. The search interface 700 displays the list of identified wearable devices 625 that meet the constraints in a result area 730. Selecting one of the wearable devices 625 in the result area 730 provides a view of the selected wearable device 625 and all of its connections to other models/data in the semantic network 600. In other examples, the faceted search area 720 facilitates filtering based on other constraints than those illustrated. In yet other examples, the medical professional may search symptoms based on wearable devices as a filtering criteria.

[43] Thus, the examples described in the present document facilitates the medical professional to identify the wearable device 625 based on the semantic network 600 without multiple searches in different repositories/sources consequently saving time and effort. Further, the medical professional gets direct access to multiple sources of information in a single interface, such as the search interface 700. Accordingly, the medical professional, who is working with the patient that need to monitor their medical conditions, for example chronic diseases such as diabetes, use this tool to select the wearable device 625 that collects data about a diagnosed disease / symptom and suits the patients' needs.

[44] The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[45] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[46] Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

[47] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state -setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[48] Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. [49] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

[50] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

[51] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware -based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. [52] Furthermore, although specific components are described above, methods, systems, and articles of manufacture described herein may include additional, fewer, or different components. For example, a processor may be implemented as a microprocessor,

microcontroller, application specific integrated circuit (ASIC), discrete logic, or a combination of other type of circuits or logic. Similarly, memories may be DRAM, SRAM, Flash, or any other type of memory. Flags, data, databases, tables, entities, and other data structures may be separately stored and managed, may be incorporated into a single memory or database, may be distributed, or may be logically and physically organized in many different ways. The components may operate independently or be part of a same program or apparatus. The components may be resident on separate hardware, such as separate removable circuit boards, or share common hardware, such as a same memory and processor for implementing instructions from the memory. Programs may be parts of a single program, separate programs, or distributed across several memories and processors.

[53] A second action may be said to be "in response to" a first action independent of whether the second action results directly or indirectly from the first action. The second action may occur at a substantially later time than the first action and still be in response to the first action. Similarly, the second action may be said to be in response to the first action even if intervening actions take place between the first action and the second action, and even if one or more of the intervening actions directly cause the second action to be performed. For example, a second action may be in response to a first action if the first action sets a flag and a third, action later initiates the second action whenever the flag is set.

[54] To clarify the use of and to hereby provide notice to the public, the phrases "at least one of <A>, <B>, ... and <N>" or "at least one of <A>, <B>, ... <N>, or combinations thereof or "<A>, <B>, ... and/or <N>" are to be construed in the broadest sense, superseding any other implied definitions hereinbefore or hereinafter unless expressly asserted to the contrary, to mean one or more elements selected from the group comprising A, B, ... and N. In other words, the phrases mean any combination of one or more of the elements A, B ... or N including any one element alone or the one element in combination with one or more of the other elements, which may also include, in combination, additional elements not listed. [55] The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application, or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method comprising:

receiving, by a communication interface, a disease identifier associated with a patient; identifying, by an analysis unit, a measurement identifier based on the disease identifier, wherein the measurement identifier identifies a health parameter of the patient that is to be monitored;

receiving, by the communication interface, an anatomy identifier that identifies a body- part of the patient to be used to monitor the health parameter; and

identifying, by a device search unit, a wearable device to monitor the health parameter of the patient at the body-part based on the measurement identifier and the anatomy identifier.

2. The method of claim 1 , wherein the measurement identifier is a first measurement identifier, the health parameter is a first health parameter, and the wearable device is a first wearable device, and the method further comprising:

identifying, by the analysis unit, a second measurement identifier that identifies a second health parameter of the patient that is to be monitored; and

identifying, by the device search unit, the first wearable device that monitors both the first health parameter and the second health parameter.

3. The method of claim 2, further comprising:

receiving a predetermined cost threshold and wherein identifying the first wearable device is based on a determination that the wearable device costs less than the predetermined cost threshold; and

in response to the first wearable device being costlier than the predetermined cost threshold, identifying, by the device search unit, a combination of a second wearable device and a third wearable device that monitor the first health parameter and the second health parameter respectively.

4. The method of claim 3, wherein both the second wearable device and the third wearable device use the body-part of the patient specified by the anatomy identifier.

5. The method of claim 4, wherein the combination of the second wearable device and the third wearable device costs lesser than the predetermined cost threshold.

6. The method of claim 5, wherein the predetermined cost threshold is based on an insurance policy of the patient.

7. The method of claim 6, wherein the insurance policy of the patient is identified based on a patient identifier.

8. A system comprising:

an analysis server configured to identify a health parameter of a patient to monitor during an activity based on a history of the patient;

a wearable device server configured to identify a list of wearable devices in response to receipt of an identifier of the health parameter from the analysis server, wherein the wearable devices in the list are configurable to monitor the health parameter; and

a client device configured to display the list of wearable device.

9. The system of claim 8, wherein the client is further configured to transmit, for receipt by the analysis server, an identifier of the activity.

10. The system of claim 9, wherein the history of the patient specifies an identifier of a disease of the patient.

11. The system of claim 10, wherein the analysis server is further configured to identify a symptom of the disease based on the disease identifier, and identify the health parameter to be monitored based on the symptom.

12. The system of claim 8, wherein the client device is further configured to transmit, for receipt by the wearable device server, an identifier of a body-part.

13. The system of claim 12, wherein the wearable device server is further configured to identify the wearable devices that monitor the health parameter using the body-part specified by client device.

14. The system of claim 8, wherein wearable device server is further configured to identify the wearable devices that monitor the health parameter and that satisfy a selection criteria.

15. The system of claim 14, wherein the selection criteria comprises at least a criterion that the wearable devices cost lesser than a predetermined cost threshold, and wherein the predetermined cost threshold is received by the wearable device server from the client device.

16. The system of claim 15, wherein the predetermined cost threshold is received by the wearable device server from an insurance provider server, wherein the insurance provider server is identified based on an insurance identifier of the patient.

17. A product comprising non-transitory computer readable storage medium that contains computer executable instructions, wherein the non-transitory computer readable storage medium comprises:

instructions to receive an identifier of an activity that is to be performed by a patient; instructions to determine a health parameter of the patient to monitor during the activity; instructions to receive a selection of a body-part to be used to monitor the health parameter; and

instructions to identify a wearable device that comprises a sensor to monitor the health parameter using the body-part selected.

18. The product of claim 17, wherein the non-transitory computer readable storage medium further comprises:

instructions to determine another wearable device that is being used by the patient; and instructions to identify the wearable device that is compatible for use in conjunction with the other wearable device.

19. The product of claim 18, wherein the other wearable device is identified based on a patient history.

20. The product of claim 17, wherein the wearable device identified based on a selection criteria corresponding to the patient, the selection criteria comprising one of a predetermined cost threshold, a brand, a weight, a compatibility with a patient's computer.