Leveraging Wearable Sensors for Context-Aware Personalized Recommendations

Methods, systems, and computer program products for leveraging wearable sensors for context-aware personalized recommendations are provided herein. A computer-implemented method includes generating, based on user-provided information, health-related recommendations to the user; monitoring, based on data derived from wearable sensors worn by the user, user activity; determining deviation information related to deviations from the health-related recommendations by the user, based the monitored activity; determining user-specific context information based on the user-provided information and/or information derived from additional sources; and generating, based on the deviation information and the user-specific context information, (i) additional health-related recommendations and (ii) incentives related to carrying out the additional health-related recommendations.

FIELD

The present application generally relates to information technology, and, more particularly, to user health-related technologies.

BACKGROUND

Costs related to healthcare have been increasing over time, and such increases are commonly assumed by insurance providers and/or passed on to consumers. Accordingly, recommending appropriate user/consumer actions based on the users' health conditions and other factors is of increasing importance. However, users may not follow such recommendations due to various preferences, which creates a number of additional challenges. Consequently, healthcare professionals and/or insurance providers would benefit from an ability to measure the extent to which users follow recommended actions. Nevertheless, existing healthcare management approaches fail to provide personalized techniques that take into account the context and consequence of user deviations from recommended actions.

SUMMARY

In one embodiment of the present invention, techniques for leveraging wearable sensors for context-aware personalized recommendations are provided. An exemplary computer-implemented method can include generating, based at least in part on user-provided information, one or more health-related recommendations to the user, and monitoring, based at least in part on data derived from one or more wearable sensors worn by the user, user activity. Such a method can also include determining deviation information related to one or more deviations from the one or more health-related recommendations by the user, based at least in part on a comparison of the monitored activity to the one or more health-related recommendations, and determining one or more items of user-specific context information based at least in part on one or more of the user-provided information and information derived from one or more additional sources. Further, such a method can additionally include generating, based at least in part on the deviation information and the one or more items of user-specific context information, (i) one or more additional health-related recommendations to the user and (ii) one or more incentives related to carrying out the one or more additional health-related recommendations.

In another embodiment of the invention, an exemplary computer-implemented method can include generating, based at least in part on the deviation information and the one or more items of user-specific context information, (i) one or more additional health-related recommendations to the user, (ii) one or more incentives related to carrying out the one or more additional health-related recommendations, and (iii) one or more penalties related to failing to carry out the one or more additional health-related recommendations. Such a method can also include generating a user score based at least in part on comparing, for the user to that of one or more previous users, (i) the user-provided information, (ii) the deviation information, (iii) the one or more additional health-related recommendations, (iv) the one or more incentives related to carrying out the one or more additional health-related recommendations, and (v) the one or more penalties related to failing to carry out the one or more additional health-related recommendations.

Another embodiment of the invention or elements thereof can be implemented in the form of a computer program product tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein. Furthermore, another embodiment of the invention or elements thereof can be implemented in the form of a system including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps. Yet further, another embodiment of the invention or elements thereof can be implemented in the form of means for carrying out the method steps described herein, or elements thereof; the means can include hardware module(s) or a combination of hardware and software modules, wherein the software modules are stored in a tangible computer-readable storage medium (or multiple such media).

DETAILED DESCRIPTION

As described herein, an embodiment of the present invention includes leveraging wearable sensors for context-aware personalized recommendations. At least one embodiment of the invention includes generating health-related recommendations for a user and detecting the degree and/or type of one or more user-deviations from the recommendations considering the user's profile and context. Additionally, such an embodiment can also include cognitively deriving and recommending user context-specific penalties and/or incentives. Also, one or more embodiments can include predicting one or more penalties for a given period of time (for example, the next month or year) for one or more specific types of deviations, and estimating a score for the user based on personalized cost-consequences for the degree(s) and type(s) of deviations.

At least one embodiment of the invention can include gathering context and consequence data with personalized information for one or more users. Such data can be subsequently used to generate cognitive decisions and/or recommendations pertaining to one or more health-related topics and a given user. By way merely of example, suppose a recommendation for a user includes exercising for one hour every day. If the user has recently undergone a surgery and/or experienced an accident, it may not make sense for him or her to exercise (or exercise for an hour, or exercise every day). In one or more embodiments of the invention, such context information (that is, that the user has recently undergone a surgery and/or experienced an accident), is considered as an input in generating health-related user recommendations and calculating user incentives (such as insurance premium discounts, etc.).

By way of yet another example, suppose a recommendation calls for the user to take a walk for 45 minutes every morning. However, if it rains in the morning (or there is other inclement weather), it may make sense for the user to avoid the morning walk. In such a scenario, the weather information represents context data that can be captured and utilized by one or more embodiments of the invention.

Also, by way of additional example, consider a scenario wherein it is recommended that two people undertake 30 minutes of exercise every day, and wherein the first person is healthier than the second person. Accordingly, while both people can potentially (assuming each person follows the recommendation) undertake approximately the same amount of exercise over a given period of time, the effect of such exercise will likely be different for the two people. In such a scenario, the variable consequences of a recommendation relative to specific users can represent context data that can be captured and utilized by one or more embodiments of the invention.

FIG. 1is a diagram illustrating system architecture and data flow, according to an embodiment of the invention. By way of illustration, step102includes a user registering (for example, with a mobile software application) with profile information. Such profile information can include, for example, health-related information such as existing conditions, age, gender, medical test reports, sleep habits, exercise habits, nutrition habits, etc. Step104includes analyzing the data provided in step102, wherein such analysis is to be used in providing recommendations to the user. Additionally, step106includes generating, and outputting to the user, one or more recommendations under a given user-related context. Further, step108includes monitoring one or more user activities as well as any user deviations from the one or more generated recommendations. Based on step106and step108, at least one embodiment of the invention can include calculating a percentage deviation110from one or more of the generated recommendations by the user.

In one or more embodiments of the invention, based on the user profile information, a software application can provide one or more recommendations to the user (via a version of the application installed on one or more user devices). The software application can also include capabilities to monitor user actions such as, for example, user exercise time, user caloric intake, user calorie burn, user stress level, user sleep patterns, user eating habits, etc. In one or more embodiments of the invention, the software application can be based on one or more Internet of Things (IoT) systems (such as, for example, home and/or wearable sensors), which collect data pertaining to a user for monitoring.

Further, step112includes creating context information based on the analysis carried out in step104. For example, based on the user profile information, at least one embodiment of the invention can include determining various forms of context information (such as, for example, recent health-related developments pertaining to the user (a recent injury, a recent medical procedure, etc.), weather information, variable consequences of a recommendation relative to specific users and/or user characteristics, etc.). The created context information, as well as the user profile data, the one or more generated recommendations, and the calculated percentage deviation(s), can be provided as inputs to a personalized, context-aware, consequence/impact-aware method114, which can be derived based at least in part on historic data116(such as, for example, health-related data including user medical history, user allergies, user behaviors, etc.).

The method114, based on the noted inputs, can provide one or more cognitive recommendations and/or decisions pertaining to the user in step118, as well as provide one or more incentives (such as insurance premium discounts, etc.) to the user in step120. With respect to providing incentives, an example embodiment of the invention can include an insurance company providing monetary incentives (such as premium discounts) to a user based at least in part on the user's current premium, the expected expenditure associated with the user, and an expected margin related thereto. Additionally, such a cognitive recommendation and/or decision can include a revised recommendation (that is, revised in relation to the generated recommendation(s) in step106) based on analysis of the above-noted inputs.

By way merely of illustration, consider an example scenario wherein a recommendation is generated for a user to perform exercise for one hour every day. Using inputs such as the created context information, the user profile data, and the calculated percentage deviation(s), method114can determine that the user has recently undergone a surgery and/or accident that would make it difficult to successfully carry out the recommendation. In such a scenario, it may not be useful and/or effective to penalize the user for failing to carry out the recommendation, and as such, the method114can generate (and output to the user) a new and/or revised recommendation (such as to avoid exercise for a proscribed period of time (relevant to recovery form the recent surgery and/or accident).

FIG. 2is a diagram illustrating pattern matching, according to an exemplary embodiment of the invention. By way of illustration,FIG. 2depicts a user profile202, context information204, a recommendation206, a degree of deviation208from the recommendations206by the user, and sector-based inflation data210, which can all be provided as input to a pattern matching algorithm212. In connection with the above-noted sector-based inflation data210, a sector refers to one or more geographic areas of users used as a parameter for analysis. The pattern matching algorithm212can include historical cost consequence data for deviations from one or more recommended actions for given user profiles and context information, and the pattern matching algorithm212can use such data, in conjunction with the above-noted inputs, to generate a pattern match with respect to a current user. In at least one embodiment of the invention, the pattern matching algorithm212learns one or more correlations between components202,204,206,208, and210, and generates a prediction214for new user data (given components202,204,206,208and210).

As also depicted inFIG. 2, the pattern matching algorithm212can generate and output a projected cost for the user. Such a projected cost can be represented in the form of an estimated health score for the user based on personalized cost-consequences for the degree and type of deviation(s) captured as inputs to the pattern matching algorithm212. At least one embodiment of the invention can include crowd-sourcing health-related data, personalized cost-consequences for the degree and type of deviations made, risks underwent, etc. Such an embodiment can additionally include applying the (unsupervised) pattern matching algorithm212to such data to discover one or more patterns between user risk (related to health-related issues) and the personalized cost-consequences for the degree and type of deviations made. Based on the results thereof, such an embodiment can include estimating a health score for the one or more users in question.

FIG. 3is a flow diagram illustrating techniques according to an embodiment of the present invention. Step302includes generating, based at least in part on user-provided information, one or more health-related recommendations to the user. The user-provided information can include one or more existing conditions, user age, user gender, one or more medical test reports, one or more user sleep habits, one or more user exercise habits, and/or one or more user nutrition habits.

Step304includes monitoring, based at least in part on data derived from one or more wearable sensors worn by the user, user activity. Monitoring the user activity can include monitoring one or more user exercise habits, user caloric intake, user stress level, and/or one or more user sleep patterns.

Step306includes determining deviation information related to one or more deviations from the one or more health-related recommendations by the user, based at least in part on a comparison of the monitored activity to the one or more health-related recommendations. The deviation information can include a degree of deviation from the one or more health-related recommendations by the user and/or a type of deviation from the one or more health-related recommendations by the user.

Step308includes determining one or more items of user-specific context information based at least in part on one or more of the user-provided information and information derived from one or more additional sources. The one or more items of user-specific context information can include one or more recent health-related developments pertaining to the user, current weather information, forecasted weather information, and/or information detailing variable consequences of a recommendation relative to multiple users.

Step310includes generating, based at least in part on the deviation information and the one or more items of user-specific context information, (i) one or more additional health-related recommendations to the user and (ii) one or more incentives related to carrying out the one or more additional health-related recommendations. The techniques depicted inFIG. 3can also include determining one or more cost-consequences for each deviation from the one or more health-related recommendations by the user, wherein this determination can be based at least in part on (i) a type of deviation attributed to each deviation, (ii) a degree of deviation attributed to each deviation, (iii) the user-provided information, and (iv) the one or more items of user-specific context information.

Also, an additional embodiment of the invention includes generating, based at least in part on the deviation information and the one or more items of user-specific context information, (i) one or more additional health-related recommendations to the user, (ii) one or more incentives related to carrying out the one or more additional health-related recommendations, and (iii) one or more penalties related to failing to carry out the one or more additional health-related recommendations. Such an embodiment can also include generating a user score based at least in part on comparing, for the user to that of one or more previous users, (i) the user-provided information, (ii) the deviation information, (iii) the one or more additional health-related recommendations, (iv) the one or more incentives related to carrying out the one or more additional health-related recommendations, and (v) the one or more penalties related to failing to carry out the one or more additional health-related recommendations.

A data processing system suitable for storing and/or executing program code will include at least one processor402coupled directly or indirectly to memory elements404through a system bus410. The memory elements can include local memory employed during actual implementation of the program code, bulk storage, and cache memories 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 implementation.

Input/output or I/O devices (including, but not limited to, keyboards408, displays406, pointing devices, and the like) can be coupled to the system either directly (such as via bus410) or through intervening I/O controllers (omitted for clarity).

Additionally, it is understood in advance that implementation of the teachings recited herein are not limited to a particular computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any type of computing environment now known or later developed.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Virtualization layer70provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers71; virtual storage72; virtual networks73, including virtual private networks; virtual applications and operating systems74; and virtual clients75. In one example, management layer80may provide the functions described below. Resource provisioning81provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing82provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.

In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal83provides access to the cloud computing environment for consumers and system administrators. Service level management84provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment85provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

At least one embodiment of the present invention may provide a beneficial effect such as, for example, leveraging wearable sensors to generate context-aware personalized health-related recommendations.