Method and system to track weight without stepping on a weight scale

Embodiments herein provide systems and methods tracking the weight of an individual who is lying in bed. One method generally includes periodically receiving weight data from a weight data sensor that is situated within the bed, wherein the weight data sensor has recognized a change in weight on the bed from a default weight of the bed, and providing to the individual a user interface to track weight patterns of the individual based on the received weight data.

BACKGROUND

Modern day body weight scales not only measure a user's weight but also his body mass index (BMI), body fat, water weight, hydration percentage, heart rate, bone mass, air quality and various other health-related measurements (e.g., all such measurements generally referred to as “weight data” herein). These scales also store such weight data and enable an individual to track and analyze historical measurements. For example, the scales may include Bluetooth and/or Wi-Fi connectivity that enable them to communicate weight data to a corresponding software application (e.g., developed by a the scale's manufacturer or a partner thereof) running on the individual's computer system or mobile device, or to a corresponding service hosted by a third party (e.g., such as the scale's manufacturer) and accessible through the Internet (e.g., through a web application accessed through a browser) that stores the weight data, provides analytics on the weight data (e.g., to provide historical trends information and/or health advice to the individual), and provides a rich user interface for the user to interact with his weight data to gauge his overall health.

Despite the sophistication of these weight scales, many users do not use a weight scale consistently. Users typically experience some phases where they are more health conscious and are more likely to track their weight using a weight scale and other phases where they are less focused on health and are less likely to use the weight scale. Unfortunately, it is during the phases when users as less health conscious (e.g., and therefore are not tracking weight data) that weight and other health related attributes (that may be measured or indicated by weight data) may deteriorate and that the tracking of weight data would have otherwise provided an indication of such deterioration and/or encouragement to improve health.

SUMMARY

Systems and methods for tracking weight data of an individual while the individual is lying in bed, for example, to go to sleep at the end of each day are disclosed herein. By tracking the weight data for an individual while he is sleeping, the individual does not need to consciously weigh himself every time he is interested in learning about his weight. Since the individual often goes to sleep in the same bed every night, his weight will be automatically measured without his conscious involvement.

A weight data sensor situated within the bed silently tracks the individual's weight every time he goes to bed and periodically transmits weight data to a hosted service (in one embodiment) that stores the weight data, performs analytics on the weight data and provides the individual a user interface (e.g., a web application, installed applications on the individual's devices, etc.) to track his weight patterns over time. In certain embodiments, the individual may also wear a fitness device that transmits other health-related data to the hosted service, such as heart rate, so that the hosted service can compare the other health-related data to past measurements (e.g., past heart rates while sleeping) that correspond to past weight measurements of the individual to confirm that the received weight data relates to the individual. The weight data sensor may disregard weight measurements if the fitness device is not located proximate to the weight data sensor and is not actively taking other-health related measurements of the individual.

In this manner, when the individual is interested in understanding his weight patterns, he need only log into the hosted service to see his weight and does not need at that time to step on a weight scale, which may or may not be available at that time. Furthermore, the ability to silently and unconsciously track the individual's weight also enables the hosted service to provide alerts to the individual in the event of too much weight loss or gain during a phase where the individual may not be consciously weighing himself to track such loss or gain.

DETAILED DESCRIPTION

FIG. 1depicts a weight data sensor that can be inserted underneath a mattress in accordance with one embodiment of the invention. A bed frame100supports a box spring105and a mattress110. A weight data sensor115can be inserted underneath mattress110, for example, between box spring105and mattress110. As depicted in the embodiment ofFIG. 1, weight data sensor115may take the form of an elongated pad that is long enough to experience an individual's full weight when the individual is lying on the bed, although it should be recognized that alternative form factors for weight data sensor115may be used consistent with the teachings herein. In addition to weight data, weight data sensor115may also be able to be able to monitor sleep patterns such as body movements, breathing cycles, heart rate, REM sleep patterns, deep sleep patterns, restless sleep patterns, etc.). Weight data sensor115may include Bluetooth, Wi-Fi or other radio wave or wireless communication capabilities that enable weight data sensor115to communicate with a local area network (LAN), a local computing device or with the Internet generally. In certain embodiments, weight data sensor115includes batteries or a power cord to plug into a nearby electric socket to provide power to drive the wireless communication capabilities and other digital functionality. Weight data sensor115also includes sensors to measure the weight (as well as other weight data in certain embodiments) of an individual when the individual is lying on mattress110. For example, embodiments of a weight data sensor115may include a number of strain gauges (e.g., within a Wheatstone bridge, etc.) or other types of load cells (e.g., pneumatic, hydraulic, etc.) that measure compressive resistance change when an individual lies on mattress110and transmits a signal to a CPU or other circuit in weight data sensor115which convert the signal into a weight measurement. In certain embodiments, weight data sensor115may utilized air bladders to recognize change in pressure when an individual lies on the bed. It should be recognized that there are a number of ways to design the form factor as well as the weight-measuring mechanisms of weight data sensor115in order to accurately measure an individual's weight when the individual lies on mattress110.

FIG. 2depicts an alternate embodiment of weight data sensor115that is embedded into mattress110in accordance with one embodiment of the invention. In one embodiment, mattress110may be an adjustable air mattress that utilizes air chambers and the load cells of weight data sensor115are incorporated as part of the air chamber mechanism such that the load cells experience compressive resistance change (e.g., to measure an individual's weight) in response to changes in the air pressure of the air chamber (as opposed to a separate elongated pad or similar insert as depicted inFIG. 2).

FIG. 3depicts a communication architecture for obtaining data from a weight data sensor in accordance with one embodiment of the invention. In the embodiment ofFIG. 3, weight data sensor115in bed frame100wirelessly communicates over a LAN's Wi-Fi network through the Internet320to a hosted service300that stores weight data collected by weight data sensor115in database305. Hosted service300stores the weight data collected by weight data sensor115, performs analytics on the weight data and presents such weight data and analytics to the individual through a user interface, for example, through a web application accessible on a web browser (e.g., on laptop310or mobile device315) or through an application installed on laptop305or mobile device310. In an alternative embodiment, weight data sensor115in bed frame100may wirelessly communicate over a Bluetooth connection established with a user's laptop or mobile device (e.g., smartphone, tablet, etc.), which in turn, transmits weight data collected by weight data sensor115to hosted service300(e.g., through the LAN's Wi-FI network and through the Internet). In certain embodiments, hosted service300may also receive other health-related data from other devices. For example, fitness bands or similar devices worn on the body of the individual may transmit heart rate, body temperature, ambient temperature, calories burned, steps walked, sleep patterns, location-based data and other data to hosted service300which can than combine such data with weight data collected from weight data sensor115and perform analytics thereon including as further discussed below. Such other health-related data is also stored in database305, in addition to the weight data stored in database305.

FIG. 4depicts a flow chart for obtaining data from a weight data sensor in accordance with one embodiment of the invention. In step400, weight data sensor115recognizes an increase of weight on top of mattress110due, for example, to an individual going to bed for the evening. In step405, weight data sensor115may begin to take periodic measurements (e.g., every 30 seconds, every minute, every 5 minutes, every 30 minutes, or any similar period depending on embodiment) of the weight of the individual in order to calculate an accurate measurement of the individual's weight based on multiple measurements. The individual may need to fall asleep or otherwise stop moving on mattress110before weight data sensor115can determine or otherwise calculate a consistent and/or stabilized weight of the individual. In step410, weight data sensor115transmits the individual's weight measurements to hosted service300, which receives them in step415. In the embodiment ofFIG. 4, these transmitted weight measurements are the raw periodic measurements taken by weight data sensor115. In step420, hosted service300then performs heuristics and/or calculations to smooth the raw periodic measurements and determine whether a consistent and/or stabilized weight of the individual can be determined for a particular time based on the raw measurements. If, in step420, a stable weight can be determined, then in step425, hosted service300stores the stable weight measurement in database305in association with the individual's identity and the date (and possibly the particular time) and in step430, provides such weight information to the individual through the user interface (e.g., web application, installed application on individual's device) as previously discussed. In certain embodiments, hosted service300is able to determine, based on comparisons with historic data (e.g., both weight data or other health-related data) stored in database305, whether the determined weight is related or unrelated to the individual if, for example, multiple persons may sleep on mattress110. In one example, if historic weight measurements are significantly different from the determined weight, hosted service300determines that the weight is unrelated to the individual. In certain embodiments, hosted service300may also receive other health-related data from other devices worn on the individual at generally the same time that weight measurements are received in step415. If the received health-related data, such as a heart rate measurement from a fitness device worn by the individual, is significantly different than the historic measurements of the individual's heart rate while the individual is sleeping (as stored in database305), then hosted service300could determine that the received weight measurement from weight data sensor115is unrelated to the individual (e.g., the individual is not sleeping in the bed, but rather actively awake elsewhere). In other embodiments, weight data sensor115may recognize when a fitness device that is worn by the individual is in close proximity to weight data sensor115(e.g., using proximity technologies such as Bluetooth or other similar radio communication technologies) and report such recognition to hosted service300. If the fitness device is actively reporting other health-related data of the individual to hosted service300but is not in proximity to weight data sensor115when the determined weight is received in step115, hosted service300may conclude that the determined weight is unrelated to the individual. In alternative embodiments, if the fitness device is not taking active measurements from the individual and is not in proximity to weight data sensor115, weight data sensor115does not send any weight measurements to hosted service305in step410. In yet other embodiments, weight data sensor115may recognize whether the individual's mobile device (such as a smart phone) is proximate to it before transmitting weight measurements to hosted service300.

Similarly, hosted service300may, in step425, disregard raw data measurements that cannot be smoothed to generate a stable weight or are otherwise inconsistent with past measurements for the individual (e.g., a pet has jumped onto the bed, a guest is sleeping on the bed, etc.) In the embodiment ofFIG. 4, in step435, if weight data sensor115continues to experience the increased weight of the individual (e.g., while the individual is sleeping throughout the night), the flow will return to step405and continue to take measurements until the increased weight has been removed (or otherwise stop taking the measurements in step440). It should be recognized that alternative flows for obtaining data from a weight data sensor in accordance with embodiments different fromFIG. 4. For example, rather than having hosted service300perform the calculations on the raw periodic weight data measurement in step420, in alternative embodiments, weight data sensor115may itself perform such calculations (and related smoothing techniques) and determine a stable weight for a particular point in time prior to transmitting the stable weight data to hosted service100. Similarly, in alternative embodiments, weight data sensor115may transmit the weight data (either raw or stabilized) to a corresponding application running on a local device, such as laptop310or mobile device315(e.g., via Bluetooth, etc.) which in turn transmits the received data to hosted service300.

FIG. 5depicts a user interface for an application that receives weight data in accordance with one embodiment of the invention. The user interface ofFIG. 5may be displayed in an application installed on a computing device such as a tablet, smartphone, laptop, desktop or other computer system that communicates with hosted service300or may be displayed in a web application running on hosted service300that is accessible through a web browser running on any of the foregoing. Hosted service300performs all the analytics and other computing needed to provide the information that the user interface ofFIG. 5displays to the individual. As depicted inFIG. 5, user interface500depicts a historic trends and statistics of weight measurements for an individual. While the granularity of the weight measurements is shown as a daily measurement, embodiments of user interface500may enable an individual to change the granularity (e.g., weekly, monthly, etc.), for example, to view trends at different granularities. User interface500also enables the individual to set alerts to notify the individual (e.g., via text message, email, etc.) if certain criteria related to weight data are recognized. For example, the individual may wish to be alerted if he gains more than 5 pounds over the course of a few weeks. User interface500may also identify trends regarding an individual's weight data. For example, user interface500has identified a trend that the individual tends to gain weight during the weekdays and also tends to be 5-10 pounds heavier during the winter months. User interface500may also provide comparisons of an individual's weight data against similarly situated individuals. For example, user interface500has identified that the individual is in the top 85% in weight for persons with similar height and age. In embodiments in which weight data sensor115also measures sleep patterns such as REM sleep patterns (or where host server300is able to combine such sleep pattern-related data from other devices such as fitness bands that transmit data to host server300as previously discussed), user interface500may also combine actual weight data with such other data to provide trends and/or statistics relating to a combination of the data. For example, as depicted inFIG. 5, hosted service300recognizes that the individual loses an average of 2.1 pounds when the individual has at least 5 hours of REM sleep in a night. The embodiment ofFIG. 5also provides advice to the individual regarding his weight depending upon the weight data trends and the statistics, for example, encouraging the individual to exercise on certain days or eat light meals on certain days given weight gain patterns. It should be recognized that the user interface ofFIG. 5is merely exemplary and many other user interfaces may be developed consistent with the teachings herein.