WO2013012862A1

WO2013012862A1 - Real-time health data analysis using a mobile device

Info

Publication number: WO2013012862A1
Application number: PCT/US2012/047062
Authority: WO
Inventors: Deepika PARIMI
Original assignee: Nanoviova Llc
Priority date: 2011-07-18
Filing date: 2012-07-17
Publication date: 2013-01-24

Abstract

Real-time capture and analysis of personal health data points is provided. One or more personal health data points are obtained using a peripheral device. Data including the one or more personal health data points is obtained from the peripheral device by a hand-held mobile device. The obtained data is then auto-tagged with one or more tags generated independent from the peripheral device using at least one sensor of the mobile device. The auto-tagged data is then analyzed in real-time on the mobile device to determine at least one health recommendation.

Description

REAL-TIME HEALTH DATA ANALYSIS USING A MOBILE DEVICE

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of United States Provisional Patent Application Serial No. 61/508,776, filed July 18, 2011, which is hereby incorporated herein by reference in its entirety.

BACKGROUND

[0002] Currently, an individual can monitor some of his or her basic health conditions using health monitoring devices such as thermometers, glucometers, and blood pressure monitors. Although data can be obtained instantly, or near-instantly, from such devices, the data is usually not investigated or analyzed to any meaningful degree until the individual attends an annual or required visit to his or her personal physician (doctor or clinic) for deeper understanding and insight into the results of these tests. When the individual visits his or her physician, health factors can be accounted for by the doctor in an analysis of the data. This analysis provides insight into the personal health of the individual, and can be especially beneficial in making recommendations for adjustments to the personal health of the individual. However, there exists inefficiency in this process, particularly between what the consumer desires in the way of health data analysis and what is available to the consumer via current technological offerings.

BRIEF SUMMARY

[0003] The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method for real-time capture and analysis of personal health data points. The method includes, for instance: obtaining one or more personal health data points of a mammal using a peripheral device; obtaining data from the peripheral device by a mobile device, wherein the data includes the one or more health data points; auto-tagging the obtained data with one or more tags generated independent from the peripheral device using at least one sensor of the mobile device; and analyzing in real-time, by a processor, the auto-tagged data on the mobile device to determine at least one health recommendation for the mammal.

[0004] Additionally, a computer program product is provided to facilitate realtime capture and analysis of personal health data points. The computer program product includes a computer readable storage medium readable by a processor and storing instructions for execution by the processor to perform a method including, for instance: obtaining data from a peripheral device, wherein the data includes one or more health data points of a mammal obtained using the peripheral device; auto- tagging the obtained data with one or more tags generated independent from the peripheral device using at least one sensor of a mobile device incorporating the processor; and analyzing in real-time the auto-tagged data on the mobile device to determine at least one health recommendation for a mammal.

[0005] In addition, a system is provided for real-time capture and analysis of personal health data points. The system includes a memory and a processor, in communication with the memory, to execute program code to perform, for instance: obtaining data from a peripheral device, wherein the data includes one or more health data points of a mammal obtained using the peripheral device; auto-tagging the obtained data with one or more tags generated independent from the peripheral device using at least one sensor of a mobile device incorporating the processor; and analyzing in real-time the auto-tagged data on the mobile device to determine at least one health recommendation for a mammal.

[0006] In accordance with one embodiment of the present invention, the peripheral device is in communication with the mobile device using at least one of a Bluetooth, Wi-Fi, and near-field-communication (NFC) communication link, and the peripheral device provides the data to the mobile device across the communication link.

[0007] In accordance with one embodiment of the present invention, the peripheral device is a personal medical device. [0008] In accordance with one embodiment of the present invention, the personal medical device is at least one of a glucometer, a blood pressure monitor, a heart rate monitor, or an implant.

[0009] In accordance with one embodiment of the present invention, the personal medical device is a glucometer, and the one or more health data points include one or more blood glucose readings obtained using the glucometer.

[0010] In accordance with one embodiment of the present invention, the analyzing in real-time includes performing statistical analysis on the auto-tagged data.

[0011] In accordance with one embodiment of the present invention, the method further includes storing the auto-tagged data on the mobile device in encrypted form.

[0012] In accordance with one embodiment of the present invention, the at least one sensor includes at least one of a GPS, compass, ambient light detector, and accelerometer.

[0013] In accordance with one embodiment of the present invention, the at least one sensor includes a software based sensor for determining altitude, date and time, and/or time zone.

[0014] In accordance with one embodiment of the present invention, the one or more tags include at least one of: location, time, time zone, directional orientation, and directional movement.

[0015] In accordance with one embodiment of the present invention, the mobile device includes a smartphone, PDA, or tablet computer.

[0016] In accordance with one embodiment of the present invention, the mammal includes a user of the mobile device, the peripheral device includes an imaging device of the mobile device and the one or more health data points include heart rate data of the user, and the analyzing includes: performing a trend analysis, the trend analysis based at least in part on the heart rate data of the user; and reporting to the user the trend analysis, wherein the reporting identifies, based on the trend analysis, whether a condition of the user is improving or deteriorating.

[0017] Additional features and advantages are realized through the concepts of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] One or more aspects of the present invention are particularly pointed out and distinctly claimed as examples in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0019] FIG. 1 depicts one example of a mobile device in communication with a peripheral device for obtaining data therefrom;

[0020] FIG. 2 depicts one example of a process for real-time capture and analysis of personal health data points, in accordance with one or more aspects of the present invention; and

[0021] FIG. 3 depicts an example of a process for real-time capture and analysis of personal health data points using a smartphone and a glucometer, in accordance with one or more aspects of the present invention;

[0022] FIG. 4 depicts one example of a system to incorporate and use one or more aspects of the present invention;

[0023] FIGS. 5A and 5B depicts an example of real-time capture and analysis of personal health data points for producing a trend analysis on recorded heart rate, in accordance with one or more aspects of the present invention; and

[0024] FIG. 6 depicts one embodiment of a computer program product to incorporate one or more aspects of the present invention. DETAILED DESCRIPTION

[0025] Current health conditions of an individual may be influenced by many different factors including: the individual's altitude (e.g. distance above sea level), external air pressure, air temperature, recent dietary pattern, current body temperature, current glucose level, current blood pressure, body-mass index (health and weight index), gender, etc.. It may be desirable to monitor a variety of these and other factors in order to monitor the individual's health for purposes of diagnosing health conditions, providing prognoses, and providing health recommendations for the individual.

[0026] As noted, an individual can monitor some of his or her basic health conditions using health monitoring device(s). While these devices may be used at the convenience of the individual and typically do not require a high-level of

understanding to operate and obtain data readings, often times the individual relies on annual or required visits to his or her personal physician (doctor or clinic) for deeper understanding and insight into the results of these tests. There exists inefficiency in this process, particularly between what the consumer desires in the way of health data analysis and what is available to the consumer via current technological offerings.

[0027] Given current and constantly advancing technologies, there is a need for a more comprehensive personal health monitoring system, having abilities beyond the mere monitoring of those factors listed above, that has the capability to enable the consumer to perform much better diagnoses and analyses than capabilities of existing devices.

[0028] One area of technology making significant technological advances in both hardware and software aspects is that of mobile computing, such as technology involving smartphones and tablet computers (tablets). Within the realm of mobile computing, mobile devices are incorporating short-range wireless communication technologies such as WiFi, Bluetooth®, and Near Field Communications (NFC) (including Radio Frequency Identification (RFID)) capabilities and technologies. [0029] Additionally, within the mobile device market, increasing number of individuals are adopting and using smartphones, tablets and other mobile device technology. Although there is an explosion in the domain of mobile devices, there has been considerably lesser technological growth in peripheral device technology of smartphones or tablets, such as, for instance, in peripheral health monitoring devices mentioned above.

[0030] Even with current advancements in data-based personal health analytical models and mobile device technology, there is a need for new technologies that offer complex personal health monitoring solutions for mobile device users.

[0031] Aspects of the present invention relate to personal medical devices.

Personal medical devices refers generally to a category of devices that are typically small, convenient devices used in situ to obtain one or more data points from a user of the device. Data points include biologic measurements/readings obtained from the user. Non-limiting examples of personal medical devices include glucometers, blood pressure monitors, human implants, thermometers, and heart rate monitors. Personal medical devices include not only the above or devices similar to the above, but also include personal health monitoring devices that are enabled using the software and sensor capabilities of a mobile device.

[0032] Mobile devices include handheld electronic computing devices having processor(s), memory, and various input and output components, some of which being input/output devices with which the user interacts. Non-limiting examples of mobile devices include tablet and slate computers, cellular telephones including smartphones, portable media players, personal digital assistants (PDAs), e-Readers, personal navigation devices such as GPS navigation devices, and handheld gaming systems. A mobile device includes any computing device having basic computation power through processor(s) (such as CPUs) and memory (such as RAM), and optionally, network access capability (Network I/O), such that the computing device is capable of functioning while being portable and without limitations through tethering to power cords or other I/O device cables. [0033] External personal medical devices can be used to read and collect data that a mobile device cannot. Conversely, mobile devices possess capabilities that personal medical devices to not. Many mobile devices have unique capabilities provided through various sensors, in addition to sufficient computational power to perform advanced statistical analysis that existing personal medical devices cannot.

Integration between personal medical devices and mobile devices can involve communication between the two devices over a (usually) short-range wired or wireless data connection, where the personal medical device is connected to the mobile device as a peripheral medical device.

[0034] FIG. 1 depicts one example of a mobile device in communication with a peripheral device for obtaining data therefrom. In FIG. 1, peripheral device 101 collects one or more personal health data points. In this particular example, a glucometer is shown. As is commonly understood, a glucometer measures blood sugar level when a user supplies a biologic sample (e.g. blood) to the glucometer. In the example of FIG. 1, blood is supplied to the glucometer via a test strip 102 which is inserted into the glucometer. The peripheral device (in this case glucometer) thereafter obtains one or more personal health data points. As noted, in the case of a glucometer, blood sugar level is obtained from a blood sample on test strip 102. In other examples, instead of a test strip, a needle-like probe is used to extract blood from the user in order to obtain the personal health data points.

[0035] Peripheral device 101 is in communication with mobile device 103 via communications link 104. Communications link 104 may comprise conventional wired or wireless network communication links implemented via any known protocol or specification. A list of non-limiting examples of communication links include serial, USB, Ethernet, WiFi, Bluetooth®, and/or near field communication (NFC), including RFID (radio frequency readers for reading RFID tags). Those having ordinary skill in the art will appreciate that WiFi includes Wireless Radio Frequency 802.11 a/b/g/n and that Bluetooth® encompasses various Bluetooth® Radio

Frequency protocols (e.g. vl .O, v2.0, EDR etc.). [0036] Peripheral device 101 and mobile device 103 communicate with each other across communications link 104 to transmit data between the two devices. In one embodiment, data is pushed from peripheral device 101 to mobile device 103. In other embodiments, data is pulled from peripheral device 101 by mobile device 103, or the devices may operate using a combination of push/pull. Additionally, in some embodiments, data transfer is initiated by peripheral device 101, while in other embodiments, data transfer is initiated by mobile device 103.

[0037] Mobile device 103 comprises any mobile device as described above that functions as a data processing system, and therefore includes, for instance, one or more processors and one or more memories (not pictured). Memory of mobile device 103 can store program code which can be read and executed by a processor of mobile device 103 to perform functions. Examples of mobile devices include smartphones and tablet computers, such as devices running an iOS-based operating system offered by Apple Inc, Cupertino, CA, USA (i.e. iPhone® and iPad®) or Android-based operating system offered by the Open Handset Alliance. In addition, mobile device includes one or more sensors 105, such as one or more of the following: GPS device, accelerometer, ambient brightness sensor, compass, etc..

[0038] While FIG. 1 depicts a particular embodiment including a glucometer and smartphone, it should be understood that the present invention applies to any type of peripheral device and mobile device. Additionally, peripheral device and mobile device need not be individually self-contained or separate devices. For instance, in some embodiments, peripheral device will be embodied, in whole or in part, within mobile device. In this manner, peripheral device need not be an external peripheral device, i.e. external to the mobile device.

[0039] In some environments, a mobile device serves as an intermediary between the peripheral device and a remote server to transmit data point(s) collected using the peripheral device to the remote server for processing. In the case of a glucometer and smartphone combination, for instance, a blood glucose reading can be transferred via a smartphone to a remote server using the smartphone 's network connectivity. In this approach, data is sent to a remote server using a basic mobile application that simply gathers the data from the peripheral device and transmits it to the remote server. This technique might rely on a network (for instance, cellular and/or Wi-Fi internet) connection of the mobile device, and, in the case of a smartphone, this transfer of data to a remote location can consume a significant amount of a user's data plan allowance. Furthermore, this procedure fails to take advantage of the ever-increasing capabilities of smartphone computing power that is becoming more prevalent today. CPU (central processing unit or the main microprocessor) speeds in smartphone devices today are surpassing the 1.4GHz speed per processing core (in some cases, multi-core processors are provided). Additionally, more working memory, such as Random Access Memory (RAM), is provided which further increases computational power of smartphones and other mobile devices.

[0040] The functionality of many peripheral health devices is relatively primitive in that basic information (data point(s)) is read and transmitted to the mobile device. A glucometer, for example, is typically used only for recording very specific sensor data, that is, blood glucose level. However, the inventor of the present application recognized there would be advantages to complementing that data point with other attributes, such as location (geographical, altitude, etc), time zone, date & time, etc. of the reading and obtained by one or more sensor(s). This additional information is useful in assessing an individual's health and in making health recommendations.

[0041] One option is to provide the required sensors within the peripheral health device so that it can also provide additional data points to the smartphone. This can be an expensive proposition because of the requirement of developing all new devices and because of the time it can take to release a new health device to the market. The manufacturer must follow United States Food & Drug Administration (and/or other countries' health regulations), health-care, and medical regulations in a process that could take years before approval.

[0042] Instead, in accordance with one or more aspects of the present invention, peripheral devices are used with mobile device technology, and the layer of analysis of the data points is pushed to the mobile device itself. This is facilitated, in part, because of developments in technology of mobile devices. In the case of smartphones, many of the devices in the market have sufficient computing power to locally run some of the most advanced and complicated analytical functions on the mobile device itself.

[0043] Personal health data points obtained using a peripheral device may be auto-tagged by the mobile device. This may be performed at the time that the mobile device receives the data points, or at a later time. A mobile application (of the mobile operating system) on the mobile device can perform the auto -tagging function to make the collected data points (from the peripheral device) more meaningful. The mobile device's user interface can organize, categorize, and tag the recorded data to facilitate performance of complex computations on the data. Furthermore, the mobile device can perform analysis of the auto-tagged data points on the mobile device itself.

[0044] As noted, the mobile device may be provided with one or more sensor technologies, such as one or more sensors commonly incorporated into mobile devices. A sensor in a mobile device can include a hardware unit or software -based (e.g. using web-services) application or module, or a combination of the two, which is available or can be made available on a mobile device to collect a specific data point. As an example, many smartphones have built-in GPS, compass, ambient light detector(s) and accelerometer(s), as hardware -based sensors. Additionally, many smartphones use software based sensors for determining, for instance, altitude (using geo location and web-services to compute altitude from latitude and longitude), date and time (using cellular network's settings) and time zone (which is computed using cellular network's settings). In accordance with one or more aspects of the present invention, these sensors can be used to generate tags which are associated with data point(s) collected by peripheral devices, to provide unique and more meaningful health data points which a peripheral device alone is unable to provide. In one particular embodiment, an application provided on the mobile device augments the data points obtained using the peripheral device by using mobile phone's sensors. The sensors can be employed to generate tags which can then be applied (e.g. auto- tagged) to the data points. [0045] The solution can be implemented using an application executing on the mobile device (e.g. executing on/by a processor of the mobile device). The application, commonly known as an "app", is given permissions to access the multitude of sensors available to the mobile device. These sensors can include, but are not limited to: GPS (location), accelerometer (directional motion/movement sensor), compass (directional orientation), time zone (using cellular network) and date and time (using cellular network's settings). As the technology of mobile device evolves, new types of sensors will become available, and one having ordinary skill in the art will recognize that such new types of sensors are encompassed within the scope of the present invention. The mobile device will be able to perform real-time analysis (e.g. computation using analytical and/or statistical models) of the data points and determine/provide health recommendations. This functionality, along with the functionality to auto-tag data points from a peripheral device, can be provided by a mobile application executing on the mobile device.

[0046] Sensors could also include one or more thermometers to measure body or room temperature, or one or more sensors to measure room air pressure and/or room air humidity. Sensors for human voice-based health detection (e.g. when the user has cold and sounds different) can also be employed. In one example, the above sensors can be software provided or implemented using a combination of hardware and software sensors.

[0047] When a peripheral device provides (e.g. sends, uploads, transfers, etc) one or more data points to the mobile phone, the mobile phone application can be launched by the mobile phone's operating system, either automatically or by user interaction, to obtain/collect those data points. The mobile app gathers additional data points using the available sensors (GPS, date -time, time zone, etc.) provided in/by mobile device to generate tags, which are associated with the collected health data points from the peripheral device as attributes to the collected health data points from the peripheral device. Real-time analysis of the auto-tagged data can thereafter be applied. In one example, as a mobile device collects these data points from a peripheral device, it can automatically tag the data and run analytical models to analyze the data directly on the mobile device. The tagged data can be stored on the phone in data sets, and artificial intelligence (AI) algorithms such as decision trees can be employed to provide advantageous data mining and pattern recognition, as examples.

[0048] In one aspect of the present invention, a process is provided for real-time capture and analysis of personal health data points, as depicted in, and described with reference to, FIG. 2. The process begins by obtaining one or more personal health data points using a peripheral device (202). As described above, peripheral devices for obtaining personal health data points include a wide range of devices such as devices capable of obtaining biological readings from a mammal (e.g. a human user of the peripheral device or for his/her pet). Examples of such peripheral devices include, but are not limited to, implants, glucometers, blood pressure meters, thermometers, data from skin or hair, etc...

[0049] Next, data is obtained from the peripheral device by a mobile device (204). The data is obtained by the mobile device, in one example, across one or more communication links established between the peripheral device and the mobile device, as described above. In other examples, where the peripheral device is incorporated into the mobile device itself, the data is obtained from the peripheral device across, e.g. a bus or similar wired connection to, for instance, memory or a processor of the mobile device. Thus, in some examples, these communications links include wireless communication link(s) existent between the two devices, such as a near-field communication (NFC) link or WiFi connection. The data obtained by the mobile device can include the data point(s) obtained by the peripheral device.

Additionally, the data can include any other data provided by the peripheral device to the mobile device, such as information about the peripheral device (model, version, firmware revision, etc.), or any other data that is or may be useful to provide to the mobile device.

[0050] After the mobile device obtains the data, it auto-tags at least a portion thereof (such as the health data points) with one or more tags (206). As noted above, the tags are generated using one or more sensor capabilities provided by the mobile device. In one example, the tags include data directly obtained from a sensor (for instance direction, using a compass sensor). In other examples, sensor data may be used in conjunction with another service to generate the tags, such as when a GPS sensor is used to determine global position, which position is then compared to some known altitude (provided from a remote web-service, as an example) for that particular global position, in order to determine elevation. The mobile device then stores this auto-tagged data to a local data store, such as a database. In one example, this data is stored in an encrypted form, for security purposes.

[0051] The auto-tagged data may then be analyzed and at least one health recommendation can be obtained/determine (208) for the individual from which the health data points were obtained. As one having ordinary skill in the art would recognize, this may include execution of one or more analytical models to perform statistical analysis of the data. In one particular embodiment, the analysis of the data includes one or more analyses of the tags applied to the obtained data point(s) and these analyses of the tags (and associated data) facilitate determination of beneficial health recommendations to provide to the user. As described above, these health recommendations can be similar or the same as those health recommendations provided by the current methods of analyzing patient data points by a health care provider.

[0052] The analytical models may be stored locally on the mobile device. From time to time, revised or new analytical models can be retrieved (e.g. downloaded) by, or pushed to, the mobile device from a remote server. Alternatively or additionally, the analytical models may be stored on a remote server and obtained on-demand by the mobile device at the time the analysis is to be conducted. The models may be transferred from the remote server to the mobile device by any suitable means, including over one or more network connections of the mobile device or other communication links between the remote server and mobile device.

[0053] As a result of the analysis of the auto-tagged data, the mobile device makes real-time intelligent health recommendations and provides these

recommendations to the user. The recommendations are provided to the user in any appropriate manner, including but not limited to, in email or text messages, through mobile device notifications (badges, popups, etc.), or by saving and/or displaying the recommendations in the application performing the analysis.

[0054] The auto-tagged obtained data can be added to a local repository (such a portion of memory of the mobile device), which repository can become a knowledge warehouse for the machine learning or Artificial Intelligence (AI) algorithms facilitating the data analyses. Many such algorithms require that a data point have multiple attributes. This allows the best selection of the algorithm to use and hence makes it possible to determine and provide the best health recommendation(s).

[0055] The mobile device can use one or more of many data analysis models (including statistical analysis) to determine the health of the individual. Analysis can also be used to learn about a user's health risks and patterns of when the user is most likely to obtain subsequent readings from peripheral device(s). The analysis of user data may be provided by way of analytics in order to provide health improvement tips and generate data point charts to provide insight which a doctor may use to make health-related recommendations and/or adjustments, for instance to the user's health monitoring habits or personal lifestyle.

[0056] By way of specific example, consider a situation involving a glucometer (peripheral device) and smartphone (mobile device). As described above, glucometers are used primarily by diabetic patients to measure blood glucose levels. There are many types of glucometers available in the market, with some advanced glucometers capable of integrating (communicating) with a device through

Bluetooth® or USB connectivity.

[0057] This integration can enable the glucometer to send readings directly to the mobile device via the provided communication link. In the case of such advanced glucometers, analysis of recorded data points can be achieved by way of uploading the data points to a remote computer at a clinic or other health-care provider. The remote computer is only aware of the recorded data points but is not provided with data such as geo-location and time -zone, or other attributes, of the context in which the data points were obtained/recorded. [0058] Time zone and location of the user (and consequently the peripheral device) are important factors which affect data readings. Since the standard glucometer lacks a built-in time -zone converter (to accurately represent the time of day at the location of the reading) and location sensor (geo-location indirectly reflects on factors that may likely cause small variance in some health readings - such as altitude, air temperature, air pressure, seasonal allergies, etc.), readings taken across time -zones and in different locations will not account for these factors, using a standard glucometer. Thus, even the most advanced glucometer is deficient in these regards with respect to obtaining a comprehensive glucose reading for the time and context in which the reading was taken.

[0059] Thus, FIG. 3 depicts an example of a process for real-time capture and analysis of personal health data points using a smartphone and a glucometer, in accordance with one or more aspects of the present invention. In this example, a glucometer and smartphone communicate using a Bluetooth® protocol. Initially, Bluetooth® connectivity is activated on the smartphone or tablet (302) and activated on the glucometer (304). As a result of this activation, the two devices become able to communicate with each other across a Bluetooth® communication link established therebetween. Then, a user takes a glucose reading using the glucometer (306), and the glucometer transmits the data points (glucose reading in this case, and optionally additional information) to the smartphone across the Bluetooth® communication link (308). The smartphone (for instance an application executing thereon) auto-tags the obtained data points with tags, such as location, date, and time zone (310). One example of an auto-tagged data set from a glucometer reading after being tagged is as follows:

(I) blood glucose reading (e.g. a numeric value);

(II) tagged attributes:

(a) date and time (tagged from mobile phone); and

(b) Array List <String>: includes (i) time zone (computed using mobile phone's time zone);

(ii) location (computer using mobile phone's GPS);

(iii) meal type [breakfast, lunch, snack, or dinner];

(iv) reading mode [before meal or after meal];

(c) time mode [in minutes, before meal or after meal];

(d) comments [string holding optional user comments].

[0060] The smartphone application stores the new auto-tagged data points to a local encrypted data store (312). The smartphone then executes one or more analytical models to analyze the stored data (314). As a result of this analysis, the personal health application makes real-time smart recommendations and provides these recommendations to the user (316). These can include recommendations about the frequency, location, date, and time about when subsequent readings should be taken. By way of specific example in the case of a glucometer, the recommendations can comprise recommendations about a next location, date, and time of when one or more subsequent glucose readings are to be taken. This information is particularly important for those who relying on blood sugar readings as part of their personal health monitoring routine.

[0061] FIG. 4 depicts one example of a system to incorporate and use one or more aspects of the present invention. The system includes a mobile device 402 and three peripheral devices: glucometer 404, blood pressure monitor 406, and implant 408. Data points are obtained from the peripheral devices: a blood glucose reading is provided to mobile device 402 by glucometer 404 and a blood pressure reading is provided to mobile device 402 by blood pressure monitor 406. Additionally, the mobile device reads data from implant 408 by way of RFID communication.

[0062] Mobile device 402 also utilizes one or more sensors (not pictured) to generate tags for tagging the obtained data points. In FIG. 4, mobile device 402 computes altitude using a combination of geo location (provided via a GPS sensor in mobile device 402) and a public web service provided via internet 410, such as an application programming interface, cellular, or other wireless location markers.

[0063] The system of FIG. 4 also includes one or more remote servers 412. As described above, remote server(s) 412 store modeling algorithms and analysis rules which can be provided to mobile device 402 to facilitate data analysis. The above can be extended in such a way that the mobile application executing on mobile device 402 may determine whether to perform the data analysis itself, or to request that the data analysis instead be performed, in whole or in part, by remote server(s) 412. In one particular example, this decision is based, at least in part, on the amount of data to analyze. Due to the advancements in mobile device technology especially in computation power thereof, in some cases the mobile application will perform the data analysis, complex statistical analysis, etc. on the mobile device itself.

[0064] The mobile device can, though it is not required, store a user's personal health data in an encrypted fashion onto a remote server using, as an example, a cloud service provided via internet 410. One method of encryption involves the use of a combination of public and private keys. The keys may be of any length depending on user preference and/or security desired. Stored data can be provided or accessible to a health care practitioner. The following describes an example for creating a secure personal health data sharing network between a user and practitioner (e.g. physician or health-care provider):

[0065] i. To access the data-store on the remote storage server, the mobile device authenticates the user by using the user's private key over a secure channel

(communication link(s)) that is established using the public key. This technique ensures that unless the user is authenticated using the private key, the mobile device application is unable to store personal health data onto the remote storage server. Because communication occurs over a secure channel and keys may be "strong", the system assures that the probability of accessing someone else's personal health data is very low. For instance, as one having ordinary skill in the art would recognize, usage of keys of length 2048-bytes or more will yield a less than 0.01% chance of being identified and leading to a data breach. The only practical way that the personal health data can be compromised is if the private key, which is known only to the user or his/her device, is compromised.

[0066] ii. The public key may be built-in to the mobile device's mobile application and/or in secure storage in hardware of the mobile device.

[0067] iii. The private key is generated by the mobile device application for the particular individual user, and/or is obtained from the secure storage in hardware of the mobile device.

[0068] iv. The private key is unique and only known the specific individual and/or the mobile device or secure storage thereof.

[0069] v. All personal health data for a user is encrypted using the user's private key.

[0070] vi. To access the personal health data of a user, the practitioner requesting access must be granted permission by the user. The user will be prompted by the mobile device application with a notification that the remote storage system has received a third-party request to access some or all of user's personal health data. The request can come from a practitioner, such as the user's physician or health-care provider, which may establish its own secure connection with the remote storage server. If the user does not have an active secure connection with the remote server through the user's mobile device, the third-party request is logged as a pending request by the remote server. The remote server can log all incoming requests from all sources to access the user's personal health data and provide the

notifications/requests to the user upon the user's next secure login.

[0071] vii. Thereafter, when the user creates a secure channel with the remote storage server using the mobile device, the user is informed of all pending requests (from above). The user can choose what request(s) to grant permissions for access. Each request can be optionally associated with a limited time -window that defines how much time the requestor has to access the personal data from the time when the request is granted by the user. After the time -window expires, the request expires. This technique ensures that the personal health information being shared is shared with only the requestor and for only the defined duration. The time -window is defined and/or controlled by the user, and may vary among the requests. The user can also have a standard/default time -window that he/she wants to apply to each request.

[0072] viii. The user can initiate an outgoing request (self-initiated request) to initiation sharing some or all personal health data with the user's physician or health care provider. In this case, that third-party practitioner is notified that the user is sharing the personal health data with them.

[0073] ix. The mobile application can have a dashboard that shows granted requests, pending requests, denied requests and self-initiated requests etc. The dashboard can also allow the user to manage previously recorded personal health data and take actions such as to archive, delete, reorder, or tag data, or to store the data in personal folders.

[0074] By way of another specific example of real-time capture and analysis of personal health data points, a process is described in which heart rate is observed to perform trend analysis for diabetes assessment. By observing the degree to which an individual is able to raise his/her heart rate merely by deep breathing, it is possible to determine whether the individual is progressing, in terms of the diabetic condition, from normal (non-diabetic) to intermediate diabetic to pre-diabetic to Type-2 diabetic, and also whether, within the Type-2 diabetic condition, progression or improvement is being made.

[0075] There is a lack of simple, efficient access for a Type-2 diabetic to know whether his/her diabetic condition is improving, deteriorating, or remaining stable, without using the commonly available (invasive) glucometer. Many Type-2 diabetics do not perform the (invasive) glucose detection self-test, which is an increasingly prevalent problem, causing hardship for health insurance companies, the individual, and the individual's family. An easy and low cost solution is provided to help a Type-2 diabetic determine whether his/her condition is improving, deteriorating, or remaining the same without (i.e. absent) using the commonly available (invasive) glucometer. Aspects of this solution are built upon the universally approved and recognized (in the United Kingdom and the United States of America) DESIR study (P. Valensi et al., "Influence of blood glucose on heart rate and cardiac autonomic function. The DESIR study", Diabet. Med. 28, 440-449 (2011)), which is hereby incorporated herein by reference in its entirety. The DESIR study is recognized in the United States by the Centers for Disease Control and Prevention (CDC), and the National Institutes of Health (NIH), as examples.

[0076] In the DESIR method, "Four hundred and forty-seven participants in the Data from an Epidemiological Study on the Insulin Resistance syndrome (DESIR) study were classified according to glycaemic status over the preceding 9 years. All were free of self-reported cardiac antecedents and were not taking drugs which alter heart rate. During five consecutive periods: rest, deep breathing, recovery, rest and lying to standing, heart rate and heart rate variability were evaluated and compared by ANCOVA and trend tests across glycaemic classes. Spearman correlation coefficients quantified the relations between cardio-metabolic risk factors, heart rate and heart rate varability." (see Abstract of DESIR).

[0077] DESIR results showed that, "Heart rate differed between glycaemic groups, except during deep breathing. Between rest and deep-breathing periods, patients with diabetes had a lower increase in heart rate than others (P(trend) < 0.01); between deep breathing and recovery, the heart rate of patients with diabetes continued to increase, for others, heart rate decreased (P(trend) < 0.009). Heart rate was correlated with capillary glucose and triglycerides during the five test periods. Heart rate variability differed according to glycaemic status, especially during the recovery period. After age, sex and BMI adjustment, heart rate variability was correlated with triglycerides at two test periods. Change in heart rate between recovery and deep breathing was negatively correlated with heart rate variability at rest, (r=-0.113, P < 0.05): lower resting heart rate variability was associated with heart rate acceleration." (see Abstract of DESIR). The DESIR concluded that "Heart rate, but not heart rate variability, was associated with glycaemic status and capillary glucose. After deep breathing, heart rate recovery was altered in patients with known diabetes and was associated with reduced heart rate variability. Being overweight was a major correlate of heart rate variability." (see Abstract of DESIR).

[0078] In accordance with one or more aspects of the present invention, a capability to measure heart rate using a mobile device and performing trend analysis on recorded heart rate is provided using an analytical technique, such as that presented in the DESIR study. The provided capability is valuable to, for instance, Type-2 diabetics, such as those who avoid taking traditional glucometer readings. An apparatus to measure heart rate with the accuracy level of +/- 1% (equivalent to a medically approved electrocardiogram) advantageously provides ease-of-use and efficiency for Type-2 diabetics.

[0079] A commercially available pulse oximeter (peripheral device) operated by a mobile device is one example device that can be used for heart rate measurement and real-time capture and analysis of personal health data points in accordance with one or more aspects of the present invention. Alternatively, the prevalence of mobile devices (for instance those running an Android-based operating system offered by the Open Handset Alliance, or an iOS-based mobile operating system offered by Apple Inc.) can be exploited with an accurate algorithm for reading the heart rate of an individual using peripheral device(s), such as a camera and (if available) a flashlight, of the mobile device. Known algorithms for reading heart rate using a mobile device's camera (and optionally in conjunction with the device's flashlight) can be employed, for instance as set forth in P. Pelegris et al, "A novel method to detect heart beat rate using a mobile phone," Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pp . 5488-5491 (Aug./Sept. 2010), which is hereby incorporate herein by reference in its entirety.

[0080] After recording the heart rate of the individual, the mobile application can perform trend analysis and provide information/recommendations to the Type-2 diabetic regarding whether the individual is improving, deteriorating, or remaining the same with regards to the diabetic condition. The change in heart rate of the individual is a valuable aspect of this analysis. Some mobile applications employing the method can observe changes within +/- 2% accuracy, or even better, in some conditions. [0081] Thus, FIGS. 5A and 5B depicts another example of real-time capture and analysis of personal health data points for producing a trend analysis on recorded heart rate, in accordance with one or more aspects of the present invention. The process begins by launching the mobile application (502). The user places a finger on the camera of the mobile device (504) which will be used in reading the user's heart rate. At this point, the user configures the reading profile for the application. Two alternative, different configurations are available in this example, though in other examples, the user may employ a combination of the two to configure the reading profile.

[0082] In the first configuration, the user provides the length of time to perform repeated deep breathing (DB), and resting phase of breathing (RP), and further provides the number of times to repeat (CC) these two breathing cycles (506). In the second configuration, the user provides the length of time for the total test (508), and the application automatically selects DB, RP, and CC for the user (510). Once the profile is configured, the camera (and flashlight if available) are activated (e.g.

turned-on) (512), and a pre -test heart rate is measured (514).

[0083] Once the pre-test heat rate is measured, the test can begin. The application prompts the user to breathe deeply as many times as possible for length of time DB (516), then prompts the user to breathe normally for length RP (518). If the number of times to repeat (CC) indentifies that these two steps are to be repeated, then the process can return to 516 and then 518. Eventually, when the test concludes, the fastest heart rate and slowest heart rate are determined and these determined heart dates, and optionally all monitored heart rate during each of the phases, are recorded for trend analysis (520). Then, a trend report can be determined, based at least in part on the recorded heart rates, and the trend report can be provided (reported) to the user. The trend report can include an indication as to whether the diabetic condition of the user is improving, deteriorating, or remaining the same. Additionally or alternatively, one or more health recommendations can be determined and provided to the user as part of the reporting of the trend report. [0084] As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product.

Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," "module" or "system." Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

[0085] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0086] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0087] Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java,

Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code 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).

[0088] Referring now to FIG. 6, in one example, a computer program product 600 includes, for instance, one or more computer readable media 702 to store computer readable program code means or logic 604 thereon to provide and facilitate one or more aspects of the present invention.

[0089] 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 program instructions.

[0090] These computer 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.

[0091] These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

[0092] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

[0093] The fiowchart 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 fiowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block 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 fiowchart illustration, can be implemented by special purpose hardware -based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0094] Further, a data processing system suitable for storing and/or executing program code is usable that includes at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements include, for instance, local memory employed during actual execution of the program code, bulk storage, and cache memory which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.

[0095] Input/Output or I/O devices (including, but not limited to, keyboards, displays, pointing devices, DASD, tape, CDs, DVDs, thumb drives and other memory media, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems, and Ethernet cards are just a few of the available types of network adapters.

[0096] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprise" (and any form of comprise, such as "comprises" and "comprising"), "have" (and any form of have, such as "has" and "having"), "include" (and any form of include, such as "includes" and "including"), and "contain" (and any form contain, such as "contains" and "containing") are open-ended linking verbs. As a result, a method or device that "comprises", "has", "includes" or "contains" one or more steps or elements possesses those one or more steps or elements, but is not limited to possessing only those one or more steps or elements. Likewise, a step of a method or an element of a device that "comprises", "has", "includes" or "contains" one or more features possesses those one or more features, but is not limited to possessing only those one or more features. Furthermore, a device or structure that is configured in a certain way is configured in at least that way, but may also be configured in ways that are not listed.

[0097] The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form 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 invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiment with various modifications as are suited to the particular use contemplated.

Claims

CLAIMS What is claimed is:

1. A method for real-time capture and analysis of personal health data points, the method comprising: obtaining one or more personal health data points of a mammal using a peripheral device; obtaining data from the peripheral device by a mobile device, wherein the data comprises the one or more health data points; auto-tagging the obtained data with one or more tags generated independent from the peripheral device using at least one sensor of the mobile device; and analyzing in real-time, by a processor, the auto-tagged data on the mobile device to determine at least one health recommendation for the mammal.

2. The method of claim 1, wherein the peripheral device is in

communication with the mobile device using at least one of a Bluetooth, Wi-Fi, and near-field-communication (NFC) communication link, and wherein the peripheral device provides the data to the mobile device across the communication link.

3. The method of claims 1 or 2, wherein the peripheral device is a personal medical device.

4. The method of claim 3, wherein the personal medical device is at least one of a glucometer, a blood pressure monitor, a heart rate monitor, or an implant.

5. The method of claim 3, wherein the personal medical device is a glucometer, and wherein the one or more health data points comprise one or more blood glucose readings obtained using the glucometer.

6. The method of claims 1, 2, 3, 4, or 5, wherein the analyzing in realtime comprises performing statistical analysis on the auto-tagged data.

7. The method of claims 1, 2, 3, 4, 5, or 6, wherein the method further comprises storing the auto-tagged data on the mobile device in encrypted form.

8. The method of claim 1, 2, 3, 4,5, 6, or 7, wherein the at least one sensor comprises at least one of a GPS, compass, ambient light detector, and accelerometer.

9. The method of claim 1, 2, 3, 4, 5, 6, 7, or 8, wherein the at least one sensor comprises a software based sensor for determining altitude, date and time, and/or time zone.

10. The method of claim 1, 2, 3, 4, 5, 6, 7, 8, or 9 wherein the one or more tags comprise at least one of: location, time, time zone, directional orientation, and directional movement.

11. The method of claim 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, wherein the mobile device comprises a smartphone, PDA, or tablet computer.

12. The method of claim 1, wherein the mammal comprises a user of the mobile device, wherein the peripheral device comprises an imaging device of the mobile device and the one or more health data points comprise heart rate data of the user, and wherein the analyzing comprises: performing a trend analysis, the trend analysis based at least in part on the heart rate data of the user; and reporting to the user the trend analysis, wherein the reporting identifies, based on the trend analysis, whether a condition of the user is improving or deteriorating.

13. A computer program product to facilitate real-time capture and analysis of personal health data points, the computer program product comprising: a computer readable storage medium readable by a processor and storing instructions for execution by the processor to perform a method comprising: obtaining data from a peripheral device, wherein the data comprises one or more health data points of a mammal obtained using the peripheral device; auto-tagging the obtained data with one or more tags generated independent from the peripheral device using at least one sensor of a mobile device incorporating the processor; and analyzing in real-time the auto-tagged data on the mobile device to determine at least one health recommendation for a mammal.

14. The computer program product of claim 13, wherein the personal medical device is at least one of a glucometer, a blood pressure monitor, a heart rate monitor, or an implant.

15. The computer program product of claim 14, wherein the personal medical device is a glucometer, and wherein the one or more health data points comprise one or more blood glucose readings obtained using the glucometer.

16. The computer program product of claim 13, 14, or 15, wherein the one or more tags comprise at least one of: location, time, time zone, directional orientation, and directional movement.

17. The computer program product of claim 13, wherein the mammal comprises a user of the mobile device, wherein the peripheral device comprises an imaging device of the mobile device and the one or more health data points comprise heart rate data of the user, and wherein the analyzing comprises: performing a trend analysis, the trend analysis based at least in part on the heart rate data of the user; and reporting to the user the trend analysis, wherein the reporting identifies, based on the trend analysis, whether a condition of the user is improving or deteriorating.

18. A system for real-time capture and analysis of personal health data points, the system comprising: a memory; and a processor, in communication with the memory, to execute program code to perform: obtaining data from a peripheral device, wherein the data comprises one or more health data points of a mammal obtained using the peripheral device; auto-tagging the obtained data with one or more tags generated independent from the peripheral device using at least one sensor of a mobile device incorporating the processor; and analyzing in real-time the auto-tagged data on the mobile device to determine at least one health recommendation for a mammal.

19. The system of claim 18, wherein the personal medical device is a glucometer, and wherein the one or more health data points comprise one or more blood glucose readings obtained using the glucometer.

20. The system of claim 18, wherein the mammal comprises a user of the mobile device, wherein the peripheral device comprises an imaging device of the mobile device and the one or more health data points comprise heart rate data of the user, and wherein the analyzing comprises: performing a trend analysis, the trend analysis based at least in part on the heart rate data of the user; and reporting to the user the trend analysis, wherein the reporting identifies, based on the trend analysis, whether a condition of the user is improving or deteriorating.