WO2016037012A1

WO2016037012A1 - Measuring health and fitness data using proximity sensors and mobile technologies

Info

Publication number: WO2016037012A1
Application number: PCT/US2015/048437
Authority: WO
Inventors: Emre Burak Turhan SOKULLU
Original assignee: Grou.Ps
Priority date: 2014-09-03
Filing date: 2015-09-03
Publication date: 2016-03-10

Abstract

A system allows health and fitness clubs to record the type of equipment used, the duration of usage by each member and/or trainer, and present this information online or offline to the club members or corporate wellness program members. This process is performed in a way that it shows the gym equipment utilization levels including but not limited to the amount of calories burned (per member, per employer, or per location) based on the data collected. The data recording may be performed by a system comprising proximity sensors and the smart devices that the gym member holds. The information then may be used in various additional ways, such as to increase member-to-member interactions and/or motivate the member with earned badges.

Description

MEASURING HEALTH AND FITNESS DATA USING PROXIMITY SENSORS AND

MOBILE TECHNOLOGIES

Inventor:

Emre Burak Turhan Sokullu

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No.

62/045,492, filed September 3, 2014, which is incorporated by reference in its entirety.

BACKGROUND

[0002] This disclosure relates generally to monitoring health, wellness, and fitness, and in particular to measuring health and fitness using proximity sensors and mobile technologies.

[0003] Technological advances in computer hardware, software, and networking have led to increased demand for electronic information interchange. Electronic communications can provide instantaneous, reliable data transfer between disparately situated locations throughout the world. Several industries and consumers are beneficially leveraging these technologies to improve efficiency and productivity and decrease costs.

[0004] As the amount of available electronic data grows, it has become more important to store and employ data in a manner that facilitates user- friendly and quick access, search, and retrieval of such data. In particular, the health and fitness industry presents a tremendous potential with copious amounts of data that can be collected by the use of gym and exercise machines (such as treadmills, stair-climbers, ellipticals, barbells, and smiths) and their physiological connections to the athletes.

[0005] While participating in top sports and other athletic events, such as sports training and fitness training on exercise machines, it is useful for participants to measure certain parameters (such as calories burned and heartbeat) reliably and without interruption during the performance. Various handheld measuring devices based on the measurement of the electrocardiogram (ECG) heartbeat rate signal from body contact points have been designed for this purpose. Other comparable devices include measuring devices based on telemetric wireless transmission.

[0006] Most of the time, athletes forget to wear these devices at the gym, as they assume that these devices are not a crucial part of their fitness routine. Hence, the data related to their physiological health and fitness are usually not collected. A study conducted by a private survey found that a third of U.S. consumers who have owned a wearable device stopped using the device within six months of receiving it, and 50% of users stopped using the device after 18 months. [0007] Measurements based on body contact are often inaccurate, depending on the user's anatomy and the way the device is held in the hand or otherwise placed in contact. Moreover, holding the device in the hand and observing the values on the display also needlessly draw the attention of the user to the device and away from the activity during the performance.

[0008] On account of their reasonable price, measuring accuracy, and ease of use, devices of this type have been used mainly for amateur performances. (For purposes of this disclosure, the term "performance" is used, broadly and without limitation, to refer to exercise routines and cycles of movements, or other activities characterized by predetermined time periods of specified physical exertion.) Wireless devices, in which the heartbeat rate signal is monitored continuously by means of ECG sensors that are stationary fixed to the body, have a measuring accuracy slightly better than that of the above-described hand-held devices.

[0009] Due to the continuous measuring principle, it has been possible to include pulse monitoring functions and statistical functions in these devices. But the price of these devices has been relatively high, which has limited their use mainly to professionals and sports clubs. Other existing methods are usually hardware bound. Some of these hardware bound devices require a user to bring his identity card and plug the device into a suitable port. Usually, none of these activity trackers actually track the type of equipment that was used inside the gym. These activity trackers are considered better fits for running and outdoors sports. But none of these solutions record the use of indoors gym equipment such as barbells, dumbbells, or treadmills in a proactive manner. The information collected from the activity trackers would be useful for not only the gym member, but also for the gym management, because they would be able to identify the type of equipment that would require upgrading or maintenance and then build up data value for them.

[0010] In the light of the above discussion, there appears to be a need for a mobile software based application that can accurately sync and detect one or more proximity sensors to give a precise estimate of how much time is spent on each machine and how many calories were burned.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 is a block diagram of an embodiment of a system for detecting a person's location in the health and fitness club and their proximity to certain gym equipment, according to some of the embodiments disclosed herein. [0012] FIG. 2 is a block diagram of an embodiment of a sensor device, according to some of the embodiments disclosed herein.

[0013] FIG. 3 is an exploded view of the sensor device, detailing the architectural description, according to some of the embodiments disclosed herein.

[0014] FIG. 4 is a flow diagram depicting the process of the proximity sensor system, according to some of the embodiments disclosed herein.

[0015] FIG. 5 is a block diagram of a computer system within which instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed, according to some of the embodiments disclosed herein.

[0016] FIGS. 6a and 6b are screenshots depicting various implementations of the mobile application, according to some of the embodiments disclosed herein.

[0017] FIG. 6c depicts an image that describes an athlete's interaction with various machines (and beacons attached to those machines) inside a gym club environment in which the athlete's activity is tracked based on the athlete's proximity to a beacon, according to some of the embodiments disclosed herein.

[0018] FIGS. 7a, 7b, 7c, 7d, and 7e depict screenshots of various exercise parameters shown in the mobile application, according to some of the embodiments disclosed herein.

[0019] FIG. 8 is an image illustrating signal strengths and distances associated with different beacons, according to some of the embodiments disclosed herein.

[0020] FIG. 9 is a user interface for installing beacons into the club environment and associating the beacons with machine types, according to some of the embodiments disclosed herein.

[0021] The figures depict various embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.

DETAILED DESCRIPTION

[0022] In various embodiments of the present disclosure, data relating to the

physiological state, the lifestyle, and certain contextual parameters of an individual are collected and transmitted to a site, either subsequently or in real-time. The transmission may be remote from the individual, where it is stored for later manipulation and presentation to a recipient, such as over an electronic network like the Internet.

[0023] As used herein, the term "sensor device" includes a class of low-powered, low- cost transmitters that can notify nearby devices of their presence. The technology enables a smart phone or other device to perform actions when in close proximity to a beacon. The application helps smart phones determine their precise position or context. With the help of a beacon or sensor device, a device's software can pinpoint its own location in a store. The beacons use low energy proximity sensing, such as Bluetooth LE (or BLE), to transmit an identifier, which may be a universally unique identifier. The identifier is picked up by a compatible application ("app") or operating system. The identifier can then be looked up over the Internet to determine the device's physical location or trigger an action on the device, such as a check-in on social media or a push notification.

[0024] A system is disclosed for detecting, monitoring and reporting human proximity to gym equipment and other exercise related equipment. The system includes a sensor device (referred to as a "beacon") that broadcasts a unique ID via Bluetooth LE or similar signals. The system also includes a central monitoring unit, such as cloud servers, and a smart computing device, such as a smart phone, watch, device, or glass app, which can

communicate with the beacon located remotely from the sensor device. The sensor device is either attached to each item of gym equipment or is attached at an optimal location in the center of the gym equipment. The mobile software application receives the unique IDs from the beacons, and the application may use the unique IDs to obtain the associated machine type from the cloud server. This provides the mobile software app with the ability to display physiologically relevant information to the user. The central monitoring unit, which may be a smart computing device, also includes a data storage device for retrievably storing the data it receives and generates.

[0025] The disclosed system also includes a mechanism for establishing electronic communication between the smart computing devices and the cloud servers. Examples include various known types of medium or long range wireless transmission devices, or a physical or a short range wireless coupling to a computer. These wireless devices in turn establish electronic communication with the central monitoring unit over an electronic network such as the Internet. Also included is a mechanism for transmitting the data indicative of one or more physiological parameters, the derived data, and/or the analytical status data to a recipient, such as the individual or a third party authorized by the individual. Hardware and System Related Description General Architecture of the Sensor Beacon

[0026] FIG. 1 is a system block diagram of an embodiment of a system for monitoring physiological data and lifestyle over an electronic network according to the present invention. As illustrated, a sensor device 102 is shown, which is placed in proximity with the

equipment(s) where the user is working out. The sensor device 102 includes one or more sensors, which broadcast uniquely identifiable data (UID) to its close proximity within 20-40 meters range (for example). The proximity sensor system 100 may use a wireless communication protocol, such as BLE. In this technology, advertising is a one-way discovery mechanism. The devices that must be discovered are capable of transmitting a plurality of packets of data in intervals from a range of 20 milliseconds to 10 seconds. The shortening of the interval is proportional to the battery life so that the device can be discovered faster. In this embodiment, each packet can range up to 47 bytes in length and primarily comprise of lbyte preamble, 4 byte access address, 2-39 bytes of advertising channel PDU and 3 bytes CRC. For advertisement communication channels, for example, the access address is 0x8E89BED6, however the connection varies for each data channel.

[0027] The PDU has its own header (for example, 2bytes, which is the size of the payload and its type to check if the device supports connections) and the actual payload (up to 37 bytes). Finally, the first 6 bytes of the payload are the MAC address of the device, and the actual information may comprise up to 31 bytes. The BLE devices are capable of operating in a non-connectable advertisement only mode (where the information is contained in the advertisement), but they can also allow connections.

[0028] By using the proximity sensor system 100, a connection can be established once a device is discovered. Once the connection is established, it is possible to read the services that a BLE device offers. Each service has its own characteristics, and each characteristic provides some value that can be read and/or written. For example, a smart thermostat can expose one service for obtaining the current temperature/humidity readings (as characteristics of that service) and another service and characteristic to set the desired temperature.

[0029] In certain embodiments, the beacons use the Advertisement channel only. As the name "beacon" suggests, these beacons transmit packets of data in regular intervals and thereafter this data can be picked up by devices such as Smartphones. These beacons are considered to be a specific usage of BLE advertisements, with some additional support on the iOS side.

[0030] In case one tries to intercept an iBeacon advertisement packet, for example one which is coming from an estimate beacon, the following data can be seen:

1 0201061AFF4C000215B9407F30F5F8466EAFF925556B57FE6D 00 49 00 OA C5 The data mentioned above comprises the preamble, fixed access address, advertisement PDU header and the MAC address removed. This data comprises only advertisement related data, which is around 30 bytes and so this fits well in the 31 byte limit. [0031] As depicted in FIG. 1, the proximity sensor system 100 comprises at least one sensor device or sensor beacon 102 (where the terms sensor device 102 and sensor beacon 102 are used interchangeably), a first gym machine 101, a similar type of second gym machine 102, and a similar type of third gym machine 103. Although, FIG. 1 depicts three gym machines, it is understood to a person having ordinary skill in the art that there may be three or more gym machines or at least one machine. These gym machines (generally) 101 may be located in an indoor or outdoor area besides or substantially close to each other. Moreover, only similar types of gym machines 101 are located close to each other, and the sensor beacon 102 is usually a beacon that is configured for the particular type of gym machine 101. For example, there may be three treadmills in a row, and one sensor beacon 102 of a particular type is placed near these treadmills, whereas the sensor beacon 102 placed near the barbell is of a different type compared to the ones placed near the treadmill.

[0032] The sensor beacon 102 is connected to a smart device 104. The smart device 104 can be one of a smart phone, personal digital a (PDA), or any other suitable computing device. The smart device 104 is carried around by the athlete always, when at the gym. The sensor beacon 102 is connected to the Internet which in turn is connected to a cloud server 103. The cloud server 103 comprises of at least one of a load balancer/proxy, an app server luster and a database. The load balancer manages online traffic by distributing workloads across multiple servers and resources-automatically on or demand. The load balancer maximizes the workload performance and helps prevent overload to help give users a seamless experience. Each load balancer is assigned an IP address which remains static while the load balancer is in use.

[0033] The app server cluster is a set of application server instances configured to act in concert to deliver greater scalability and availability than a single instance can provide.

While a single application server instance can only leverage the operating resources of a single host, a cluster can span multiple hosts, distributing application execution over a greater number of CPUs. While a single application server instance is vulnerable to the failure of its host and operating system, a cluster continues to function despite the loss of an operating system or host, hiding any failure from clients.

[0034] The database is created to operate large quantities of information by inputting, storing, retrieving and managing that information. The databases are set up so that one set of software programs provides all users with access to all the data.

[0035] Additionally, the sensor beacons 102 may also generate data indicative of various contextual parameters relating to the environment surrounding the individual. For example, the sensor beacon 102 can generate data indicative of the air quality, sound level/quality, light quality or ambient temperature near the individual, or even the global positioning of the individual. The sensor beacon 102 may include one or more sensors for generating signals in response to contextual characteristics relating to the environment surrounding the individual, the signals ultimately being used to generate the type of data described above. Such sensors are well known, as are methods for generating contextual parametric data such as air quality, sound level/quality, ambient temperature and global positioning.

[0036] Network link(s) involved in the full handheld proximity sensor system 100 may include any suitable number or arrangement of interconnected networks including both wired and wireless networks. By way of example, a wireless communication network link over which mobile devices communicate may utilize a cellular-based communication

infrastructure. The communication infrastructure includes cellular-based communication protocols such as AMPS, CDMA, TDMA, GSM (Global System for Mobile

communications), iDEN, GPRS, EDGE (Enhanced Data rates for GSM Evolution), UMTS (Universal Mobile Telecommunications System), WCDMA and their variants, among others. In various embodiments, network link may further include, or alternately include, a variety of communication channels and networks such as WLAN/Wi-Fi, WiMAX, Wide Area

Networks (WANs), Blue-Tooth and Bluetooth LE.

[0037] The proximity sensor system 100 may be operably connected with (or included within) a private social network (with other athletes and trainers) or a set of educational tools that can teach the athlete or trainer certain exercises, workouts, safety tips regarding the machine in use.

[0038] The private social network may include features such as a shared calendar, newsfeed, personal profiles, private messaging, location and/or machine check-ins, blogs, wiki, forums, comments, likes and more The mobile devices may access or utilize one or more of these private social network systems or associated functionality.

[0039] The proximity sensor system 100 communicates with the user's mobile device. The app, which may be pre-installed on the mobile device, captures the data and looks up its own database to determine if it is a recognized piece of equipment. If it is recognized, then the app shows relevant information to the athlete who is using the mobile device. Otherwise, the app sends the UID to one or more cloud servers using API calls, and cloud servers that communicates with their own database server returns the type of machine, e.g., via internet. The app then displays relevant information to the user. The relevant information may include the types of exercises that the athlete may perform on that particular machine, machine instructions, tutorials, other athletes' and trainers' comments, social network data, and more.

[0040] FIG. 2 is a block diagram of an embodiment of the sensor device 102 of FIG. 1, according to the embodiments as disclosed herein. The sensor device 102 includes data processing capability, here shown as a microprocessor 208. In an embodiment, the sensor device 102 is a Bluetooth LE device, such as estimate iBeacon.

[0041] Depending upon the nature of the signal generated by the sensor device 102, the signal can be sent through one or more of the amplifiers 202, conditioning circuit 204, and an accelerometer 206, before being sent to the microprocessor 208. For example, where the sensor device 102 generates an analog signal in need of amplification and filtering, that signal can be sent to the amplifier 202, and then on to the conditioning circuit 204, which may, for example, be a band pass filter. The amplified and conditioned analog signal can then be transferred to the accelerometer 206, where it is converted to a digital signal. The digital signal is then sent to the microprocessor 208. Alternatively, if the sensor device 102 generates a digital signal, that signal can be sent directly to the microprocessor 208.

[0042] In particular embodiments of the system of FIG. 1, the proximity sensor systemlOO may include a processor, memory, storage, an input/output (I/O) interface, a communication interface, and a bus. In particular embodiments, the processor includes hardware for executing instructions, such as those making up a computer program.

[0043] As an example and not by way of limitation, to execute instructions, the processor may retrieve (or fetch) the instructions from an internal register, an internal cache, memory, or storage; decode and execute the instructions; and then write one or more results to an internal register, an internal cache, memory, or storage . In particular embodiments, the processor may include one or more internal caches for data, instructions, or addresses. In particular embodiments, memory includes main memory for storing instructions for the processor to execute data for processor to operate on. As an example and not by way of limitation, the proximity sensor system 100 loads instructions from storage to memory.

[0044] The microprocessor 208 may then load the instructions from memory to an internal register or internal cache. To execute the instructions, the processor may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, the processor may write one or more results (which may be intermediate or final results) to the internal register or internal cache. The microprocessor 208 may then write one or more of those results to memory. [0045] In particular embodiments, storage includes mass storage for data or instructions. As an example and not by way of limitation, storage may include an HDD, a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage may include removable or non-removable (or fixed) media, where appropriate. Storage may be internal or external to computer system, where appropriate. In particular embodiments, storage is nonvolatile, solid-state memory. In particular embodiments, storage includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), or flash memory or a combination of two or more of these.

[0046] In particular embodiments, communication interface includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet- based communication) between computer system and one or more other computer systems or one or more networks. As an example and not by way of limitation, communication interface may include a network interface controller (NIC) for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface for it. As an example and not by way of limitation, the proximity sensor system 100 may communicate with an ad hoc network, NFC (near field communications), Bluetooth, Bluetooth LE, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless.

[0047] As an example, the proximity sensor system 100 may communicate with a wireless PAN (WPAN) (e.g., a BLUETOOTH WPAN), a WI-FI network (e.g., a

802.1 la/b/g/n WI-FI network), a WI-MAX network, a cellular telephone network (e.g., a Global System for "mobile" Communications (GSM) network, a Long Term Evolution (LTE) network), NFC (near field communications), Bluetooth, Bluetooth LE , or other suitable wireless network or a combination of two or more of these.

[0048] FIG. 2 is a block diagram of an embodiment of sensor device 102 of FIG. 1. The sensor device 102 includes a processing element, here shown as a microprocessor 208. A digital signal or signals representing certain physiological and/or contextual characteristics of the individual user may be used by the microprocessor 208 to calculate or generate data indicative of physiological and/or contextual parameters of the individual user. The microprocessor 208 is programmed to derive information relating to at least one aspect of the individual's physiological state. It should be understood that the microprocessor 208 may also comprise other forms of processors or processing devices, such as a microcontroller, or any other device that can be programmed to perform the functionality described herein.

[0049] The sensor device 102 also includes input/output circuitry, which is adapted to output and receive as input certain data signals in the manners to be described herein. Thus, the memory 210 of the sensor device 102 will build up, over time, a store of data relating to the individual user's body and/or environment.

[0050] That data is periodically broadcasted from the sensor device 102 and beamed to the smart device, as shown in FIG. 1, where it may be stored in a database for subsequent processing and presentation to the user, preferably through a local or global electronic network, such as the Internet. This uploading of data can be an automatic process that is initiated by the app pre-installed on the smart device 104 periodically or upon the happening of an event such as the detection by the sensor device 102 of a heart rate below a certain level, or can be initiated by the individual user or some third party authorized by the user, preferably according to some periodic schedule, such as every day at 10:00 a.m.

Alternatively, rather than storing data in the Memory 210, the sensor device 102 may continuously upload data in real time or in buffers.

Embodiments of the Sensor Device

[0051] FIG. 3 depicts an exploded view of the sensor device 102 of FIGS. 1 and 2, according to some embodiments disclosed herein. The sensor device 102 broadcasts signals using the Bluetooth Low Energy (BLE) standard, allowing precise, indoor-geo location but also contextual interaction/engagement, as proximity to the sensor device can trigger some specific App functionalities.

Micro-Location

[0052] The technology in the sensor device 102 allows a device to understand its position, even in indoor locations where smartphones or tablets are not able to pick up GPS signals from overhead satellites. This is finding geo-location with a very high level of granularity which is conventionally known as micro-location. The beacons-enabled apps on the mobile device are notified when the device moves in and out of the range of beacons and are able to monitor the distance as their proximity changes over time. This procedure allows apps to know precisely where they are not in terms of a map-location using longitude and latitude (like GPS does), but considering where the mobile device is relative to known points. [0053] In an embodiment, the beacons are tiny battery operated radios that can be placed in any location. Once the mobile device gets within the range, it senses the beacon and locates it. For example, a beacon may broadcast an "I am here" message more or less once per second to any device within range of the Bluetooth L radio signal since each beacon has its own ID, the mobile device can tell them apart and recognize the context of the world around itself.

Interaction/Engagement/Context

[0054] The beacons signals enable interaction with mobile apps, for example triggering some app functionality to perform a specific action on a specific mobile device, at exactly a specific time and in a specific location. The beacons thus make it possible to effortlessly engage with people in a physical space through their mobile devices. Creating a smart location-oriented infrastructure provides mobile devices with contextual info based on the environment they move through (as shown in FIG.6a). This technology can be leveraged to make apps aware of the user's context and so this feature allows a new level of interaction and engagement.

[0055] In an embodiment, the mobile devices will automatically react when they come within range of the sensor device 102. This eliminates the need to remove the mobile device from the pocket of eth user to start the beacon-enabled app manually. An app can register with iOS/Android to be started when specific types of beacons move in the range of the device. In alternative embodiment, connection to the Internet is not necessary, so the mobile device need not consume Wi-Fi or cellular data services.

[0056] A beacon identifies itself using three customizable values that are proximity UUID (128 bit), major and minor (16 bit each), and there is also an additional internal identifier. The beacon signals allows one to calculate distances in quite an approximate and qualitative way which can monitor three ranges/regions. The ranges are in between immediate (less than 50 centimeters), near (approximately between 50 centimeters and 2/5 meters), and far (more or less between 2/5 meters and 30/50 meters, depending on walls), as shown in FIG. 8.

[0057] The proximity UUID is an identifier which distinguishes a user's beacon from others. In case beacons are used to present special offers to customers in a chain of stores, all the beacons belonging to the chain have the same proximity UUID. The dedicated application for that chain will be scanning in the background for the beacons in the given UUID. The major number (2 bytes, here: 0x0049, so 73) is used to group a related set of beacons. For example, all beacons in a store will have the same major number. That way the application will know in which specific store the customer is located. The minor number (again 2 bytes, here: OxOOOA, so 10) is used to identify individual beacons. Each beacon in a store will have a different minor number, so that one knows where the customer is exactly situated.

Measuring Distance

[0058] The parameter TX power, is used to determine how close one is to a beacon. This can be presented either as rough information (immediate/far/out of range) or as a more precise measurement in meters. The TX power (here: 0xC5 = 197, 2's complement = 256- 197 = -59 dBm) is the strength of the signal measured at 1 meter from the device (RSSI - Received Signal Strength Indication). As the strength of the signal decreases predictably with distance, knowing the RSSI at 1 meter, and the current RSSI (we get that information together with the received signal), it is possible to calculate the difference.

Context of the Present Subject Matter

[0059] Each sensor device 102 or beacon has a built-in wireless communication chip (e.g., Bluetooth LE). The beacon can run for up to 5 years on a single included battery. The hardware is covered with the soft silicone case housing which also has a sticky base side, allowing it to be easily attached to any flat surface like wood, concrete, or glass. When installed the beacons begin to transmit a 2.4 GHz Bluetooth signal. The signals are capable of communicating with smartphones that are as close as four inches away, or as far as 200 feet away.

[0060] In the context of the present subject matter, mobile applications with an accelerometer sensor may work as a pedometer or step counter for counting the number of steps one takes while walking, running or step aerobics. These applications work with the devices which have the accelerometer 206 sensors built in it. For example while running, the app might monitor the change in accelerometer 206 position and based on it, it will conclude if the step is taken. The app may provide the information about the angle at which one is holding the device, direction, speed at which it is moved and the gravity. The accelerometer 206 provides the values whenever they are changed along with the time at which the event occurred. The Application stores these values in the database and looks for a pattern.

[0061] In an embodiment, the accelerometer 206 provides three values, which are x-axis value, y-axis value, and z-axis value. As the person starts taking the first step, the x-axis value will increase at a rate depending upon the force at which the person starts to move, y- axis which will indicate the relative change in height of the device along with the force. This process shows an increase in value as while taking the step person will rise from the ground and then this will decrease till the step touches the ground.

[0062] In some embodiments, beacons can calculate the proximity of the athlete to a device at the gym, which can be any gym equipment such as treadmills, barbells, dumbbells, elliptical, stair climbers, stationary bikes, pools, bosu balls, and so on. Using this information plus the time that is spent on each of these devices, the system can estimate the calories burned by the user.

[0063] For example, if the system knows that a person who weighs 200 pounds spent 15 minutes on a treadmill, then using the beacon and the app it can be determined that the person was in 5 meters proximity of the treadmill for about 16 minutes. If the clock is started about half a minute after the person gets to his proximity position, he may burn about 108 calories. If the user wishes to enter further parameters, such as his favorite treadmill speed and incline, then the app may be able to fix it. Similarly 1 hour of medium intensity weightlifting makes one to lose around 200-250 calories. Therefore, a person who spends half an hour near the barbells may be regarded as having burned 100-125 calories.

[0064] The number of calories that are burned in different kind of physical activities can be determined from a variety of reference sources. In particular, many sources provide such data, including well known sources such as myfitnesspal, prohealth, nutribase, Harvard Medical School, Mayo Clinic, and NutriStrategy. In addition, rules of thumb are available for more exercises, and the mobile device of the present disclosure may makes use of this data combined with the sensory information collected in to estimate total calories burned. Exemplary System Architecture

[0065] FIG. 4 is a flow diagram depicting the process of the proximity sensor system, according to the embodiments as disclosed herein. As depicted in FIG. 4, initially the gym staff or any person places the beacon or the sensor device/beacon 102 near the gym equipment at Step 402. The sensor device 102 is similar to a small beacon that's installed in a fixed location and broadcasts its presence to all the Bluetooth LE receptive devices around. The beacon could, for instance, be as small as 2 inches, and as far away as 230 feet. The exact maximum range depends on the environment where the Bluetooth uses the same type of radio waves, such as 2.4 Ghz WiFi routers. The signal can be diffracted, interfered, or absorbed by water. Bluetooth LE receptive smart devices in the range can pick up the Bluetooth LE radio signal and estimate their distance to the beacon by measuring the received signal strength (RSSI). Depending on the implementation, devices could probe the signal every second (1Hz) or 10 times a second (10 Hz). [0066] At step 403, club associates beacon IDs with machine types. The association can be made through a web or mobile user interface as depicted in the FIG 10. Each beacon ID, which can be found in a sticker on the beacon or through a mobile app, is manually associated with a machine type like barbell, stationary bike, bosu ball, etc. The process can be automated in a variety of ways, including using a mobile phone app that allows the gym staff to associate the beacon in closest proximity with a set of machine type options. This way, the staff would not need to look up the UID of the beacon manually. The club also may also designate certain areas as rest zones so that not only they are not counted in the calories burned but the data is also fed into the app, which may present additional value-added information and services to the user/athlete.

[0067] At step 404 _^ the user installs the application of the club, which enables synchronization with the sensor device 102. The app is similar to any smart phone application, which can be downloaded from a mobile app store. In an embodiment, the smartphone can pick up a signal from more than one beacon at the same time. If there are three or more sensor devices 102 within the range, the smartphone can calculate the distance to each beacon and use this data to estimate its relative location. Such beacons can be uniquely identified as well since each beacon broadcasts its own ID.

[0068] At step 406, the app calculates the amount of time the user spent using the gym equipment based on the duration of beacon signals received in close proximity. The term "close proximity" may change by device type. For example, while a pool would consider "close proximity" in a very large zone, the stationary bike would consider "using the machine" state only if the athlete is within the 0.5 meters range of the beacon.

[0069] Once the app of the club and the beacon are in communication, the application calculates how many calories are at step 408. For example, if the user is working out using the bench press barbell for around 20 minutes and weighs 200 lbs, then using these parameters the mobile device app calculates the amount of calories as indicated in the table above. The app calculates the amount of calories burned, using time spent as a parameter, and provides an estimation. For example, if it is known that the person weighs 205 pounds (this information was entered in the app) and the user spent around 30 minutes running on treadmill would have burned 372.5 calories. The user then is allowed to enter more details about their exercise, like the number of sets and repetitions, or their speed in the case of running on treadmill. These additional details may be used to make the estimations even more accurate. For example, in the case of treadmill, if the user enter that h ran at 8 mph, then the calories burned would jump to 628 calories based on the rule of thumb data provided described herein. FIG. 7b illustrates a user interface of the app for entering these details.

[0070] In an embodiment, the smartphone continuously monitors the strength of the signal (or RSSI), which is compared to three predefined ranges that divide the area around a beacon into three zones (e.g., far, near, and immediate). The application is also notified of which zone the phone is currently in. Whenever the user enters a zone, their context may change, so the OS notifies the application about this event as well.

[0071] The beacon UID is transmitted to the cloud services through API calls, and the servers return the function of the beacon based on the previously entered role of the beacon. Finally, at step 410, the results are presented in a dashboard on the app, a web page via the Internet, a kiosk, or a private social network. The results include all relevant parameters related to fitness or wellness related environment.

System Block Diagram

[0072] FIG. 5 is a block diagram of a machine in the example form of a computer system 500, within which instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine.

Further, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0073] The example computer system 500 includes a processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 504, and a static memory 506, which communicates with each other via a bus 508. The computer system 500 may further include a video display unit 510 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 500 also includes an alphanumeric input device 512 (e.g., a keyboard), a user interface (UI) navigation device 514 (e.g., a mouse), a disk drive unit 516, a signal generation device 518 (e.g., a speaker), and a network interface device520. The computer system 500 may also include an environmental input device 526 that may provide a number of inputs describing the environment in which the computer system 500 or another device exists, including, but not limited to, any of a global positioning sensing (GPS) receiver, a temperature sensor, a light sensor, a still photo or video camera, an audio sensor (e.g., a microphone), a velocity sensor, a gyroscope, an accelerometer, and a compass.

[0074] The disk drive unit 516 includes a machine-readable medium 522 on which is stored one or more sets of data structures and instructions 524 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 524 may also reside, completely or at least partially, within the main memory 504 and/or within the processor 502 during execution thereof by the computer system 500, the main memory 504 and the processor 502 also constituting machine -readable media.

Mobile Application

[0075] FIGS. 6a and 6b are screenshots depicting various implementations of the mobile app, according to the embodiments as disclosed herein. As depicted in FIGS. 6a and 6b, the proximity sensor system 100 is capable of detecting on a real time basis that how far particular gym equipment is located from the user when using the machines tab. The app can also notify the user of newsfeeds or messages. Such newsfeed or messages can include trainer messages, other athlete's status updates, blogs, check-ins, comments fitness tips such as six pack secrets or how to build a beach body.

[0076] FIG. 6c shows a map of a gym, illustrating how far or near a user is from each one of the items of gym equipment. In this example, the user is very close to the stationary bike, aboutlO m away from the elliptical, 25 m away from the pool, and 18m from the barbells.

[0077] FIGS. 7a, 7b, and 7c depict screenshots of various exercise parameters, according to the embodiments as disclosed herein. As depicted in FIG. 7a, when the user is working out on the treadmill, the app may question the user as to how tough the workout is. The workout may be either one of soft, okay, and hard. These parameters can vary from machine to machine. For example, for a treadmill, the questions that are asked can be like "how fast did you run," whereas with dumbbells, it may be "how much weight did you lift." To make things easier, a generic "soft," "hard," and "ok" is shown, and these generic ideas can be associated with very rough estimates.

[0078] FIG. 7b depicts a screenshot of the mobile app when the user is using the treadmill during the manual mode. Unlike the auto mode, during the manual mode the user has complete control over the treadmill with respect to all its functions. This area of the app can be accessed through a menu such as that shown in FIG. 7c. In this way, not only can the user check in to a machine for his current or future exercises, but the user can also modify the automatically recorded or manually entered logs of their exercises.

[0079] FIG. 7d depicts another potential educational benefit of the app. In this example, the athlete (user) is shown with all possible exercises with the machine in close proximity. They could choose a machine manually and fetch this information as well. The data here may store on cloud servers or the app's device itself.

[0080] FIG. 7e shows an example exercise. In this example, since the user is close to barbells, the user is shown multiple exercises related to this machine. In this example the user chose barbell deadlift, so the app displays an educational video, preparation and execution information, and more information such as muscles that work with this type of exercise.

[0081] More functionality can be added, such as workouts. Workouts may include multiple exercises bundled together assigned automatically by the app or the trainer of the athlete.

[0082] As the user moves about or changes location in the club, the distance covered will be updated on a real time basis on the mobile device.

Algorithm to Measure If a Person Is Actually Working on a Machine

[0083] The system determines whether someone is close to a machine, such as by using threads. In one embodiment, a thread for performing this function is disclosed in U.S.

Provisional Application No. 62/045,492, filed September 3, 2014, which is incorporated by reference in its entirety. While this example uses client-side calculations to find out whether the user has checked in or checked out to a machine, an alternative approach is to buffer and load the beacon id, time, and position data in real time or near real time to the cloud servers. Thereafter, heavier pattern recognition and clustering algorithms (such as k-means clustering, k-nearest neighbors, Gaussian distribution, or density-based spatial clustering) may be used to find data patterns that show whether a particular device was actually in close proximity (based on the machine type) and so if it can be considered in operation by the user. Also, while the algorithm sets a standard 10 cm threshold for machine use, the algorithm and parameters may depend on the type of machine.

[0084] Further, since the algorithm uses the data that is provided by the Bluetooth LE stack of the operating system that it sits on, the Bluetooth LE stack of the operating system is preferably capable to cancel noises sufficiently. The received signal strength indicator (RSSI) is a value used for estimating proximity. The sensor beacon 102 signal is simply a radio wave and is therefore is susceptible to factors such as diffraction, multipath propagation, interference, and absorption (especially by human bodies). Accordingly, proximity measurements based on radio waves should take all those factors into account.

[0085] Based on the disclosed thread (which keeps track of time spent on each machine), cluster graphs are obtained for each machine in the gym.

[0086] FIG. 8 is a screenshot showing how the beacons are identified with machine types automatically, according to the embodiments as disclosed herein. As depicted in FIG. 8 and FIG. 9, a mobile device captures the beacon signal, sends the UID to the cloud service, and then returns the machine type based on the mobile device's proximity to the beacon. The mobile device is assigned a value, such as "immediate," "near," or "far." The exercise recording usually occurs at "immediate" proximity based on the machine type. For example, "immediate," can relate to staying within touching distance of the gym equipment, "near" can relate to staying within 2 meters of the equipment, and "far" can relate to being within the same room as the equipment.

[0087] FIG. 6c is a screenshot that depicts a club environment with multiple machines and associated beacons, where a user's activity tracking is based on their proximity to a beacon, according to the embodiments as disclosed herein. As depicted in FIG. 6c, the beacons are located at pivotal points in the gym close to the equipment. A gym can possess both indoor and outdoor equipment and exercise areas as depicted in FIG. 6c. The beacons situated in outdoor areas may be made of waterproof and other atmosphere resistant material. FIG. 6c also depicts the close proximity of the athlete to the particular equipment and the corresponding auto check in of the user with his mobile device having the application installed.

[0088] In an advantageous embodiment, the information collected by the mobile software app may be useful for employers that sponsor employees' gym memberships, since this information allows the employer to check if their corporate programs are being effectively used.

[0089] In case of members, the proximity information recorded can be used to create a dashboard that shows the amount of calories burned and the time spent at the gym without the need of wearing an additional device on the athlete's side. The user/athlete just needs to go to the gym with their mobile phone and carry it with them. By using the information collected, a private or public social network can be fed with member's activity data (based on the user's privacy preferences) which would then result in improving the social relationship within the club and potentially increase the member retention rates for the gym. [0090] In an embodiment, badges may be earned as certain thresholds when goals are reached and broadcasted to the members of the health and fitness clubs through the private or public social network(s) for motivational and marketing purposes.

Summary

[0091] The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.

[0092] Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.

[0093] Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.

[0094] Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. [0095] Embodiments of the invention may also relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a non-transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein.

[0096] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

Claims

What is claimed is:

1. A system for tracking use of exercise equipment in a gym, the system comprising:

a plurality of exercise machines, each exercise machine having a type, and at least two of the plurality of exercise machines having different types;

for each of a plurality of sets of the exercise machines having the same type, a proximity sensor co-located with the set of exercise machines having the same type, the proximity sensor configured to broadcast an identifier unique to the set of exercise machines;

computer program product executable on a mobile device, the computer program product comprising a non-transitory computer-readable storage medium containing computer program code for:

receiving the broadcasted identifiers unique to each of the sets of

exercise machines,

determining the type of the exercise machines based on the received identifier,

measuring a time period during which the mobile device is in

proximity to the proximity sensor,

computing, based on the type of the exercise machines and the time period, one or more metrics describing a user's use of one or more of the exercise machines, and

reporting the determined metrics; and

an application server configured to receive and store the reported metrics.

2. The system of claim 1, wherein each of the proximity sensors is configured to broadcast an identifier using a low-power wireless communication protocol.

3. The system of claim 1, wherein one or more of the proximity sensors is further configured to broadcast data indicative of one or more contextual parameters relating to the environment surrounding the proximity sensor.

4. The system of claim 1, wherein the instructions for determining the type of the exercise machines based on the received identifier comprise instructions for requesting the type of the exercise machines from a cloud server using the identifier.

5. The system of claim 1, wherein one of the plurality of proximity sensors is associated with multiple of the plurality of exercise machines having the same type.

6. The system of claim 1, wherein the computed one or more metrics comprise calories burned by a user determined based on the type of the exercise machine and the determined time period.

7. A computer program product for tracking a use of exercise equipment in a gym, the computer program product comprising a non-transitory computer-readable storage medium containing computer program code for:

receiving, by a mobile device, a signal from a proximity sensor co-located with a set of exercise machines having the same type;

decoding from the received signal an identifier unique to the set of exercise

machines associated with the proximity sensor;

determining the type of the exercise machines based on the identifier; measuring a time period during which the mobile device is in proximity to the proximity sensor;

computing, based on the type of the exercise machines and the time period, one or more metrics describing a user's use of one or more of the exercise machines; and

reporting the determined metrics to an application server.

8. The computer program product of claim 7, wherein determining the type of the exercise machines based on the identifier comprises for requesting the type of the exercise machines from a cloud server using the identifier.

9. The computer program product of claim 7, wherein the computed one or more metrics comprise calories burned by a user determined based on the type of the exercise machine and the determined time period.

10. The computer program product of claim 7, wherein the computer-readable storage medium further containing computer program code for:

receiving, by the mobile device, a signal from each of a plurality of proximity sensors; and

selecting a nearest proximity sensor from the plurality of proximity sensors based at least in part on a signal strength of the received signals, the selected proximity sensor used as the proximity sensor co-located with a set of exercise machines having the same type.

11. The computer program product of claim 7, wherein the computer-readable storage medium further containing computer program code for: receiving manually input information from a user of the mobile device regarding the user's use of the exercise machine of the determined type.

12. The computer program product of claim 7, wherein the computer-readable storage medium further containing computer program code for:

providing instructional information regarding use of the exercise machine of the determined type.

13. A method for tracking use of exercise equipment in a gym, the method comprising:

maintaining a plurality of proximity sensors in different locations in a gym, each proximity sensor located near a set of exercise machines of a same type; associating each proximity sensor with the set of exercise machines near the

proximity sensor;

broadcasting identifiers unique to each of the sets of exercise machines from the corresponding proximity sensors;

receiving from an application on a mobile device one or more metrics describing a user's use of one or more of the exercise machines; and storing the received metrics in a memory.

14. The method of claim 13, wherein the identifiers are broadcast using a low- power wireless communication protocol.

15. The method of claim 13, further comprising:

broadcasting, from the corresponding proximity sensors, data indicative of one or more contextual parameters relating to the environment surrounding the proximity sensor.

16. The method of claim 13, wherein one of the plurality of proximity sensors is associated with multiple of the plurality of exercise machines having the same type.

17. The method of claim 13, wherein the received one or more metrics comprise calories burned by a user determined based on the type of the exercise machine and the determined time period.