Performance prediction for activity

Abstract

Embodiments for performance prediction of an activity are disclosed. In some embodiments, a method includes obtaining personal previous assessment information associated with a user, obtaining historical performance information associated with a previous activity, obtaining activity assessment information for a current activity; and predicting performance of the user for the current activity based on the individual previous evaluation information, the historical performance information, and the activity evaluation information.

Description

Technical Field

[0001] The present disclosure relates generally to health monitoring and fitness applications. Background Art

[0002] Many existing mobile and wearable devices, such as smartphones, smart watches, and earbuds, include sensors that provide data that can be used to determine a user's health and / or fitness. These devices may include fitness applications that allow a user to select a route for walking, running, biking, or hiking. For example, a user may query a hiking application to find the best trails near the user and provide reviews from other users to understand current trail conditions.

[0003] Sensor data such as heart rate and motion data can be used to track a user's vital signs and motion during physical activity, from which health and performance metrics can be calculated, including but not limited to heart rate, respiratory rate, caloric expenditure, recovery rate, etc. Some metrics can provide an indication of a user's ability to participate in physical activity. For example, some mobile devices, such as Apple Inc.'s Provides mobility metrics such as walking speed, step length, double support time, and estimates of walking asymmetry. These mobility metrics can be used to characterize the user's gait and mobility.

[0004] A common problem faced by users who want to participate in physical activities such as hiking is that they cannot assess their activity performance. For example, users may not know whether they can handle a particular hiking trail without overexerting themselves. There are many variables that can affect their performance, such as the user's physical fitness, injuries, trail conditions, trail difficulty, weather, etc. Overexertion while performing an activity may cause the user to feel dizzy, sore, hot, sweaty, or have a high pulse rate, abdominal pain, palpitations, or chest pain. Summary of the invention

[0005] Embodiments for performance prediction of activities are disclosed. In some embodiments, a method includes: obtaining personal previous evaluation information associated with a user, obtaining historical performance information associated with previous activities of the user or other users, obtaining activity evaluation information of a current activity; and predicting the performance of the user on the current activity based on the personal previous evaluation information, the historical performance information, and the activity information.

[0006] In some embodiments, the personal prior assessment information includes health or fitness information calculated from sensor data provided by one or more sensors of the mobile device or wearable device.

[0007] In some embodiments, personal prior assessment information is obtained from user input via a user interface of a mobile device or wearable device.

[0008] In some embodiments, the individual's prior assessment information includes mental health information obtained from user input.

[0009] In some embodiments, the user input includes a description of the user's age and one or more physical characteristics.

[0010] In some embodiments, the user input includes a description of at least one of clothing, footwear, mobility aids, or articles worn or carried by the user.

[0011] In some embodiments, the historical performance information includes at least one of heart rate, breathing rate, body temperature, galvanic skin response, muscle activity, or user survey data.

[0012] In some embodiments, the current activity is one of walking, running, jogging, biking, strolling, or hiking.

[0013] In some embodiments, the activity assessment information includes at least one of route road conditions, elevation, slope, weather conditions, sun exposure, scenery, availability of supporting facilities, or traffic density.

[0014] In some embodiments, the predicted performance is based on at least one of a purpose of the activity, whether the activity was previously completed by the user or other users, or inferences drawn from similar activities performed by the user or other users.

[0015] In some embodiments, the predicted current performance includes a description of how the user is likely to feel during or after completing the activity.

[0016] In some embodiments, a system includes: at least one processor; a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform any of the foregoing methods.

[0017] In some embodiments, a non-transitory computer-readable medium has instructions stored thereon that, when executed by at least one processor, cause the at least one processor to perform any of the foregoing methods.

[0018] Other embodiments relate to apparatus, systems, and computer-readable media.

[0019] Certain embodiments described herein provide one or more of the following advantages. Provide users with more detailed information about a particular activity they intend to perform based on the user's health, fitness level, past performance of the activity, crowdsourced performance data, weather conditions, activity information, and any other information that can be used to predict the user's activity performance before performing the activity. Performance predictions allow users to make informed decisions about whether to participate in a particular activity that may aggravate a physical condition, cause discomfort, and / or lead to excessive exertion. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow diagram illustrating performance prediction for a campaign according to one or more embodiments.

[0021] Figure 2 is a block diagram of a system for performance prediction of an activity according to one or more embodiments.

[0022] Figure 3 is a flow diagram of a process for performance prediction of an activity according to one or more embodiments.

[0023] Figure 4 It is used to implement reference Figures 1 to 3 A block diagram of the device architecture that describes the features and processes. DETAILED DESCRIPTION

[0024] Overview

[0025] Figure 1 100 is a flowchart illustrating performance prediction of an activity according to one or more embodiments. In order to provide performance prediction, various health and fitness information is collected and stored on a mobile device or wearable device and / or on a network. For example, personal previous assessment information 101 may be stored in one or more databases and include, but is not limited to, the user's age, gender, cardiovascular age, height, weight, surgical procedures (e.g., hip implants, etc.) or other health impairments (e.g., pacemakers, etc.), preferences for activity types (e.g., walking, running, cycling, etc.), mobility aids (e.g., knee braces, canes, strollers, wheelchairs, etc.), clothing and footwear (e.g., high heels, sneakers, boots, jeans, sportswear, etc.), carried items (e.g., backpacks, suitcases, etc.), other items moved (e.g., child carriers, strollers, etc.) or pets (e.g., dogs, pet breeds, etc.), personal health, such as mental health determined by users recording their moods and emotions or by taking standardized mental health assessments.

[0026] In some embodiments, the previous personal assessment information 101 is input by the user through a graphical user interface (GUI) of a mobile application (such as a health or fitness application) running on the mobile / wearable device. In some embodiments, the previous personal assessment information 101 can be obtained from one or more mobile applications running on the mobile / wearable device over time. For example, the health application can calculate various health indicators over time, such as heart rate, breathing rate, mobility indicators, etc., based on sensor data provided by one or more sensors (such as a heart rate sensor, a motion sensor, etc.) of the mobile / wearable device.

[0027] In addition to the personal prior assessment information 101, historical performance information 102 associated with the activity is obtained and stored in one or more databases. The historical performance information 102 includes past performance data (e.g., heart rate, breathing rate, body temperature, galvanic skin response, etc.) associated with the activity (e.g., hiking, walking, cycling, strolling, running / jogging, skiing, swimming, etc.). For example, the performance data may include the user's heart rate and / or breathing rate associated with the activity (e.g., hiking on a path or route, cycling on a path or route, etc.), user-reported / rated survey data (e.g., reports on comfort, effort, enjoyment, etc.), and performance data (e.g., heart rate, breathing rate, galvanic skin response, survey data, muscle activity, etc.) of other users who have performed the same activity in the past, which performance data may be obtained, for example, through crowdsourcing.

[0028] In addition to the personal previous assessment information 101, the historical performance information 102, and the activity assessment information 103, information related to the activity that the user intends to perform is also obtained. The activity can be any physical activity, including hiking, walking, jogging, running, strolling, cycling, swimming, aerobic training, shopping, dancing, sightseeing, participating in any sports (e.g., skiing, tennis, scuba diving, etc.), etc. The type of activity assessment information may depend on the activity. In an exemplary hiking scenario, a user may call a hiking mobile application on their mobile / wearable device and initiate a search query for nearby hiking paths. The user may enter various filter conditions for the search query, such as outdoors (e.g., national parks, cycling paths, etc.), outdoor cities (e.g., Venice, London, etc.), indoors (e.g., shopping malls, running tracks, etc.), etc. The user may apply the search filter to the road conditions (e.g., paved roads, cobblestone roads, boardwalks, gravel roads, elevations (e.g., flat, inclined, steps, etc.), etc.). In some embodiments, users may enter the conditions they observe on the path, which may be crowdsourced and provided to other users in the search results. An exemplary hiking application is A mobile application is available on the World Wide Web at https: / / alltrails.com. All or some of the above information is an example of activity assessment information 103 for a hiking activity. Other activities may have additional or different assessment information.

[0029] In some embodiments, the user can also search for weather conditions along the route (e.g., rain, fog, UV exposure, humidity, etc.) and / or weather-induced road conditions (e.g., wet, slippery, icy, dry, snowy, etc.) on the hiking application or in a separate weather application.

[0030] In some embodiments, the user can filter the user traffic density on the route (e.g., how many people are currently using the route). For example, the traffic density information can be based on crowdsourced information.

[0031] Other miscellaneous assessment information 103 related to the hiking activity may be input into the hiking application, such as whether the path is narrow or wide, winding or straight, and whether there are supporting facilities (e.g., restrooms, fountains, benches for resting, views or viewpoints, etc.). Based on the search filters selected by the user, a list of suggested hiking paths is presented to the user on the GUI.

[0032] In some embodiments, the user's performance on the suggested activity is predicted 104 based on the personal previous evaluation information 101, the historical performance information 102, and the activity evaluation information 103. The user's predicted performance 104 on the activity may include data or indicators indicating whether the user can complete the activity safely and comfortably based on his personal previous evaluation information 101 (e.g., physical condition) and the historical performance information 102 of the suggested activity. The performance prediction may be affected by additional information such as the purpose of the activity. For example, the purpose may be sightseeing, exercise, daily commuting, chores, etc.

[0033] In some embodiments, performance predictions may be influenced using crowdsourced performance information from other users with similar prior personal assessment information 101. For example, other users may have reported to the hiking application that they felt tired or sweaty during their hike, suggesting that certain gear / supplies should be brought along for the hike (e.g., wear hiking boots, a hat, bring water, bring sunscreen, bring insect repellent, etc.).

[0034] Some examples of performance predictions include providing a qualification of how the user will feel after the activity (e.g., comfortable, exhausted / sweating, overexerted (need to rest). In some embodiments, the performance prediction may qualify whether the user is suitable for the activity based on the user's personal previous evaluation information 101, historical performance information 102, and activity evaluation information 103 (e.g., search results for suggested hiking trails). If not suitable, the application may warn the user not to perform the activity, provide alternative activities at the same location or other locations (e.g., nearby locations), or provide any other guidance to the user (e.g., consult a doctor first, bring water or food).

[0035] In some embodiments, the performance prediction can be in the form of text description and / or indicators. For example, the performance score of the suggested route can be provided to the user, wherein the score is a personalized score for the user. For example, the performance score can be calculated based on the sum of weighted factors, the user's reported / monitored physical condition, the user's reported / monitored injuries, the user's historical performance of the activity (e.g., the user's performance on the same path in previous hikes), route characteristics (e.g., paved roads, dirt roads, muddy, slippery roads, etc.), weather conditions (e.g., rain, heat, fog, snow, etc.), and other information such as the user's clothing (e.g., whether wearing suitable hiking shoes, hats, sun protection, etc.), the purpose of the activity (e.g., sightseeing, exercise, etc.), the existence of supporting facilities (e.g., wheelchair access, fountains, restrooms, first aid stations or ranger stations, etc.). The weights applied to these factors can be based on the impact on the user's activity performance. For example, reported injuries may be weighted more than weather conditions because it may have a greater impact on the user's activity performance. In some embodiments, the weight can be a number between 0.0 and 1.0. In some embodiments, the performance score can be used to index a database of text and / or visual information displayed on the GUI of their mobile / wearable device. In some embodiments, the prediction score can be converted into a probability that the user will feel comfortable (e.g., pain-free, within energy expenditure range, etc.) during their hike.

[0036] In some embodiments, supervised or unsupervised machine learning can be used to generate performance predictions. For example, a neural network can extract feature vectors from personal previous evaluation information 101, historical performance information 102, and activity evaluation data 103, and input the feature vectors into a multi-layer convolutional neural network or other deep learning network that can be trained based on crowdsourced data. The output of the neural network can be a performance score for the activity. In some embodiments, the performance score can define whether the user is suitable for the activity.

[0037] In some embodiments, the recommendation engine can be used to recommend routes for activities to users based on personal previous evaluation information 101, historical performance information 102, and activity evaluation information 103. As used herein, routes refer to hiking paths, walking paths, bicycle paths, etc. In some embodiments, the recommendation engine can be implemented using collaborative filtering (e.g., such as singular value decomposition (SVD)) and / or content-based filtering. In some embodiments, crowdsourced fitness scores can be used as input to the recommendation engine. The recommendation engine can be used to limit multiple suggested routes provided in the search results. The recommendation engine can view the user's predicted performance score and use the performance score to match the user with a specific route from multiple suggested routes.

[0038] Figure 2 is a block diagram of a system 200 for fitness prediction of activities according to one or more embodiments. The system 200 includes a personal health / fitness database 204, an activity recommender 206, a map / route database 205, an activity history database 208, and a performance predictor 207.

[0039] Sensor data 201 output by one or more sensors of a mobile / wearable device is ingested by a fitness application 202 and a health monitoring application 203, which use the sensor data 201 to automatically calculate fitness and health data and / or indicators. For example, the health monitoring application 203 can calculate the user's mobility indicators as an indication of the user's injuries, disabilities, and short-term and long-term health. An example of a health monitoring application is Apple's Health application. The user's walking mobility is affected by various health conditions including muscle degeneration, neurological diseases, and cardiopulmonary fitness. Some examples of mobility indicators include, but are not limited to, walking speed, step length, two-foot support time, and walking asymmetry. Walking mobility indicators are more fully described in the May 2022 publication "Measuring Walking Quality Through Mobility Metrics" (https: / / www.apple.com / healthcare / docs / site / Measuring_Walking_Quality_Thr ough_iPhone_Mobility_Metrics.pdf).

[0040] Some examples of fitness metrics include, but are not limited to, heart rate, stride length, ground contact time, vertical oscillation, average power, and VO2 max. An exemplary fitness application 202 is Apple's Workout application, which uses the Apple Watch (Apple ) to provide an estimate of the user's aerobic fitness level (VO2 max estimate) during an outdoor walking, outdoor running, or hiking workout. To estimate the user's aerobic fitness level, the fitness training application takes into account the user's age, gender, weight, height, and any medications that may affect the user's heart rate. Mobility metrics and fitness metrics can be calculated throughout the day and stored in a personal health / fitness database 204, which can be located on the mobile / wearable device and / or on a network storage device, where it can be viewed from other devices (e.g., a desktop computer).

[0041] Activity suggester 206 suggests activities such as hiking, biking, walking, running, jogging, sightseeing, skiing, tennis, swimming, etc. to the user. In some embodiments, personal prior information 101 stored in personal health / fitness database 204 is used to provide activity suggestions, as well as map / route data from map / route database 205 (if the activity requires a route) and activity history data from activity history database 206. In some embodiments, a single database may be used. Map-route data includes map data (e.g., polylines with metadata) required to generate a route based on user input (such as a filtered search query). Activity suggester 206 may include a search engine for performing searches or receiving search results. Activity suggester 206 may be implemented in a mobile application (e.g., a hiking application), as a network or operating system service, or in any other suitable manner.

[0042] The output of activity suggester 206 is a list of one or more suggested activities based on the user's search query. If information from personal health / fitness database 204 is used to filter the search, the suggested activities will take into account the user's health and fitness information. For example, if the user has a low fitness score or mobility score, the suggested activities will require less physical exertion (e.g., shorter distance, lower grade, paved surface, etc.).

[0043] In some embodiments, the activity recommender 206 also uses the activity history data from the database 208 to select one or more previous activities that were associated with the user in the past. The activity history data may include performance data (e.g., performance scores) output by the performance predictor 207. In some embodiments, the activity history data may include crowdsourced data, such as, for example, an average performance score based on performance scores achieved by other users who have participated in the activity and have similar health and fitness indicators. In some embodiments, similarity may be determined using a similarity metric (e.g., a distance metric, a path, a road, etc.) and / or a classifier (e.g., a k-nearest neighbor (KNN) algorithm). Crowdsourced information may be received from a crowdsourcing device 209 (e.g., a smart phone, a smart watch, a fitness band implementing the system 200) via a network 210 (e.g., the Internet) and a network interface 211 (e.g., a database manager).

[0044] The performance predictor 207 calculates prediction data (also referred to herein as a "performance prediction") that can be presented to the user, for example, on a GUI of the user's mobile / wearable device, before performing an activity associated with a route. In some embodiments, the performance predictor 207 calculates a performance score based on personal health / fitness data, activity data, and weather conditions (e.g., provided by a weather application). The activity data may include, but is not limited to, any information related to the activity to be performed by the user. For example, if the activity involves a route, the activity data may include the length of the route, elevation, road conditions, number of turns, slopes, availability of supporting facilities (e.g., restrooms, wheelchair access, fountains).

[0045] As previously disclosed, all of the foregoing information may be included in some factors that are weighted to calculate a performance score that may be presented to the user on a GUI along with text giving the reason for the score, warnings, recommendations for items to bring (e.g., bring water and a hat), etc. In some embodiments, the performance score may be used to filter out activities that are suggested for the user, either automatically or in response to an option selected by the user in the mobile application.

[0046] The foregoing will now be illustrated by way of example. Bob decides that he wants to go hiking. He travels to a national park and discovers that there are many different hiking trails. He uses the hiking app on his smartwatch to determine which route to take. He uses a filter that requests scenic routes between 5 miles and 10 miles in length. He also indicates by input into the GUI that he is not wearing hiking shoes.

[0047] The hiking application generates multiple paths based on Bob's search criteria. In some embodiments, Bob's personal previous evaluation information is also used to search for paths suitable for Bob based on the personal previous evaluation information 101 stored in the personal health / fitness database 204. In some embodiments, the personal previous evaluation information 101 includes an aerobic fitness level score calculated by a health application running on his smart watch, and also includes a mobility index calculated by a health monitoring application running on his smart watch. Bob does not exercise often, and for his age, gender, height and weight, he has a fitness score below the average level. Bob also takes high blood pressure medication. Bob has also injured his left leg, which makes him prefer to use his right leg. The hiking application also checks the stored historical performance information 102 stored in the activity history database 208 to determine whether Bob has hiked on any trails in the national park in the past, or the average performance scores of other hikers who have hiked on these trails in the past. In this example, although Bob has not been to the national park below, there are average performance indicators for other users based on similar personal prior evaluation information 101 (e.g., same age, gender, weight, height, high blood pressure, no hiking shoes, low fitness level, and leg injury) based on similarity indicators and / or classifier algorithms.

[0048] The hiking application also requests and obtains local weather conditions indicating a temperature of 85 degrees Fahrenheit and sunny weather in addition to the prior assessment information 101. All of this information is used to generate a list of suggested hiking trails in the national park, as well as a performance forecast indicating how Bob is likely to perform on the suggested hiking trails.

[0049] In this example, the list of suggested hiking trails includes shorter paved paths to accommodate Bob's lower aerobic fitness level score and mobility index due to a leg injury and his lack of good hiking shoes and high blood pressure.

[0050] Thus, system 200 outperforms conventional applications by providing Bob with more information that he can use to select a hiking path that will be safe and comfortable for Bob based on his physical fitness and any health issues he may have.

[0051] Figure 3 is a flow chart of a process 300 for fitness prediction of an activity according to one or more embodiments. The process 300 may be performed, for example, using reference Figure 4 The described device architecture 400 is implemented.

[0052] Process 300 includes obtaining evaluation information associated with a user (301), obtaining historical performance information associated with previous activities of the user or other users (302), obtaining activity information of a current activity (303); and predicting the user's performance for the current activity based on the evaluation information, the historical performance information, and the activity evaluation information (304). Each of these steps is previously responsive to Figure 1 and Figure 2 Described.

[0053] Example device architecture

[0054] Figure 4 It is used to implement reference Figures 1 to 3 4. The system architecture 400 is a block diagram of a device architecture 400 for the features and processes described herein. The architecture 400 may include a memory interface 402, one or more hardware data processors, image processors, and / or processors 404, and a peripheral device interface 406. The memory interface 402, one or more processors 404, and / or the peripheral device interface 406 may be separate components or may be integrated into one or more integrated circuits. The system architecture 400 may be included in any suitable electronic device for collision detection, including but not limited to: smart watches, smartphones, fitness bands, and any other device that can be attached, worn, or held by a user.

[0055] Sensors, devices, and subsystems may be coupled to the peripheral device interface 406 to provide multiple functions. For example, one or more motion sensors 410, light sensors 412, and proximity sensors 414 may be coupled to the peripheral device interface 406 to facilitate motion sensing (e.g., acceleration, rotation rate), lighting, and proximity functions of the wearable device. A location processor 415 may be connected to the peripheral device interface 406 to provide geolocation. In some specific implementations, the location processor 415 may be a GNSS receiver, such as a global positioning system (GPS) receiver. An electronic magnetometer 416 (e.g., an integrated circuit chip) may also be connected to the peripheral device interface 406 to provide data that can be used to determine the direction of magnetic north. The electronic magnetometer 416 may provide data to an electronic compass application. The motion sensor 410 may include one or more accelerometers and / or gyroscopes, which are configured to determine the speed and direction of movement. The barometer 417 may be configured to measure atmospheric pressure (e.g., pressure changes inside a vehicle). The biosignal sensor 420 can be one or more of the following: a PPG sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an electromyogram (EMG) sensor, a myoelectric (MMG) sensor for measuring muscle activity / contraction (e.g., a piezoresistive sensor), an electrooculogram (EOG) sensor, a galvanic skin response (GSR) sensor, a magnetoencephalogram (MEG) sensor, and / or other suitable sensors configured to measure biosignals.

[0056] Communication functions may be facilitated by wireless communication subsystems 424, which may include radio frequency (RF) receivers and transmitters (or transceivers) and / or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of communication subsystems 424 may depend on the communication network or networks over which the mobile device is expected to operate. For example, architecture 400 may include a device designed to communicate over a GSM network, a GPRS network, an EDGE network, a WiFi network, or a wireless network. TM Network and Bluetooth TM Network-operated communication subsystem 424. Specifically, the wireless communication subsystem 424 may include a host protocol so that the collision device can be configured as a base station for other wireless devices.

[0057] The audio subsystem 426 may be coupled to the speaker 428 and the microphone 30 to facilitate voice-enabled functions such as voice recognition, voice replication, digital recording, and telephony functions. The audio subsystem 426 may be configured to receive voice commands from a user.

[0058] I / O subsystem 440 may include touch surface controller 442 and / or other input controller 444. Touch surface controller 442 may be coupled to touch surface 446. Touch surface 446 and touch surface controller 442 may detect contact and motion or interruption thereof, for example, using any of a variety of touch-sensitive technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch surface 446. Touch surface 446 may include, for example, a touch screen or a digital crown for a smart watch. I / O subsystem 440 may include a haptic engine or device for providing tactile feedback (e.g., vibration) in response to commands from processor 404. In one embodiment, touch surface 446 may be a pressure-sensitive surface.

[0059] Other input controllers 444 may be coupled to other input / control devices 448, such as one or more buttons, rocker switches, thumbwheels, infrared ports, and USB ports. One or more buttons (not shown) may include up / down buttons for volume control of speaker 428 and / or microphone 430. Touch surface 446 or other controllers 444 (e.g., buttons) may include or be coupled to fingerprint identification circuitry for use with fingerprint authentication applications to authenticate a user based on the user's fingerprint.

[0060] In one implementation, pressing the button for a first duration may unlock the touch surface 446, and pressing the button for a second duration longer than the first duration may turn the mobile device on or off. The user may customize the functionality of one or more buttons. For example, touch surface 446 may also be used to implement virtual or soft buttons.

[0061] In some implementations, the mobile device can present recorded audio files and / or video files, such as MP3, AAC, and MPEG files. In some implementations, the mobile device can include the functionality of an MP3 player. Other input / output and control devices can also be used.

[0062] Memory interface 402 may be coupled to memory 450. Memory 450 may include high-speed random access memory and / or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and / or flash memory (e.g., NAND, NOR). Memory 450 may store an operating system 452, such as the iOS operating system developed by Apple Inc. of Cupertino, California. Operating system 452 may include instructions for handling basic system services and for performing hardware-related tasks. In some implementations, operating system 452 may include a kernel (e.g., a UNIX kernel).

[0063] The memory 450 may also store communication instructions 454 that facilitate communication with one or more additional devices, one or more computers, and / or one or more servers, such as instructions for a software stack that implements wired or wireless communication with other devices. The memory 450 may include graphical user interface instructions 456 to facilitate graphical user interface processing; sensor processing instructions 458 to facilitate sensor-related processing and functions; phone instructions 460 to facilitate phone-related processes and functions; electronic message processing instructions 462 to facilitate electronic message processing; web browsing instructions 464 to facilitate web browsing-related processes and functions; media processing instructions 466 to facilitate media processing-related processes and functions; GNSS / location instructions 468 to facilitate general GNSS and location-related processes and instructions; and reference Figures 1 to 3 The memory 450 also includes other application program instructions 472, including but not limited to instructions for implementing Figure 2 Instructions for an application (eg, a hiking application, a cycling application, a running / jogging application) of the illustrated system 200.

[0064] Each of the instructions and applications identified above may correspond to an instruction set for performing one or more of the functions described above. These instructions need not be implemented as separate software programs, processes, or modules. Memory 450 may include additional instructions or fewer instructions. In addition, various functions of the mobile device may be performed in hardware and / or software, including in one or more signal processing and / or application specific integrated circuits.

[0065] Although this specification includes many specific implementation details, these specific implementation details should not be understood as limitations on the scope of any invention or possible content to be protected, but should be understood as descriptions of features specific to a particular embodiment of a particular invention. Certain features described in the context of different embodiments in this specification may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in the form of any suitable sub-combination. In addition, although some features may be described above as working with certain combinations and even initially claimed in this way, one or more features of the claimed combination may be removed from the combination in some cases, and the claimed combination may involve a sub-combination or a variation of the sub-combination.

[0066] Similarly, although the operations are shown in a specific order in the accompanying drawings, this should not be understood as requiring such operations to be performed in a sequential order or in the specific order shown, or requiring the execution of all the operations shown to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. In addition, the division of each system component in the above-mentioned embodiment should not be understood as requiring such division in all embodiments, and it should be understood that the program components and the system can be generally integrated together in a single software product or encapsulated in multiple software products.

[0067] As described above, some aspects of the subject matter of this specification include the collection and use of data from various sources to improve the services that mobile devices can provide to users. This disclosure contemplates that, in some cases, the collected data may identify a specific location or address based on device usage. Such personal information data may include location-based data, addresses, subscriber account identifiers, or other identifying information.

[0068] The disclosure also contemplates that entities responsible for the collection, analysis, disclosure, transmission, storage or other purposes of such personal information data will comply with established privacy policies and / or privacy practices. Specifically, such entities should implement and adhere to the use of privacy policies and practices that are recognized as meeting or exceeding the industry or government requirements for maintaining the privacy and security of personal information data. For example, personal information from users should be collected for the legal and reasonable purposes of the entity, and not shared or sold outside these legal purposes. In addition, such collection should only be carried out after the user's informed consent. In addition, such entities should take any necessary steps to safeguard and protect access to such personal information data, and ensure that others who can access personal information data comply with their privacy policies and procedures. In addition, such entities can subject themselves to third-party assessments to prove that they comply with widely accepted privacy policies and practices.

[0069] In the case of an advertisement delivery service, the present disclosure also contemplates implementation schemes in which a user selectively blocks the use of or access to personal information data. That is, the present disclosure contemplates hardware elements and / or software elements to prevent or block access to such personal information data. For example, in the case of an advertisement delivery service, the techniques of the present invention may be configured to allow a user to opt-in or opt-out of participating in the collection of personal information data during registration for the service.

[0070] Thus, while the present disclosure broadly covers the use of personal information data to implement one or more of the various disclosed embodiments, the present disclosure also contemplates that various embodiments may also be implemented without access to such personal information data. That is, various embodiments of the present technology will not fail to function properly due to the lack of all or a portion of such personal information data. For example, content may be selected and delivered to a user by inferring preferences based on non-personal information data or an absolute minimum amount of personal information, such as content requested by a device associated with the user, other non-personal information available to a content delivery service, or publicly available information.

Claims

1. A method, comprising: obtaining, using at least one processor of the mobile device or the wearable device, personal prior assessment information associated with the user; obtaining, with the at least one processor, historical performance information of previous activities associated with the user or other users; obtaining, using the at least one processor, activity assessment information associated with a current activity; as well as The at least one processor is used to predict the user's performance for the current activity based on the personal previous evaluation information, the historical performance information, and the activity evaluation information.

2. The method of claim 1, wherein the personal prior assessment information comprises health or fitness information calculated from sensor data provided by one or more sensors of the mobile device or the wearable device. 3 . The method according to claim 1 , wherein the personal previous assessment information is obtained from user input through a user interface of the mobile device or the wearable device.

4. The method of claim 1, wherein the personal prior assessment information includes mental health information obtained from user input.

5. The method of claim 3, wherein the personal prior assessment information includes a description of the user's age and one or more physical characteristics of the user.

6. The method of claim 3, wherein the personal prior assessment information includes a description of at least one of clothing, footwear, mobility aids, or articles worn or carried by the user.

7. The method of claim 1, wherein the historical performance information comprises at least one of heart rate, breathing rate, body temperature, galvanic skin response, muscle activity, or user survey data.

8. The method of claim 1, wherein the current activity is one of walking, running, jogging, biking, strolling, or hiking.

9. The method of claim 1, wherein the activity assessment information includes at least one of route or path road conditions, altitude, slope, weather conditions, sun exposure, scenery, availability of supporting facilities, or traffic density.

10. The method of claim 1, wherein the predicted performance is based on at least one of a purpose of the current activity, whether the current activity was previously completed by the user or other users, or inferences drawn from similar activities performed by the user or other users.

11. The method of claim 10, wherein the predicted performance includes a description of how the user is likely to feel during or after completing the current activity.

12. A system, comprising: at least one processor; a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations including: Obtaining personal prior evaluation information associated with a user; obtaining historical performance information of previous activities associated with the user or other users; Obtaining activity evaluation information associated with the current activity; and The user's performance on the current activity is predicted based on the personal previous evaluation information, the historical performance information, and the activity evaluation information.

13. The system of claim 12, wherein the personal prior assessment information comprises health or fitness information calculated from sensor data provided by one or more sensors of the mobile device or the wearable device.

14. The system according to claim 12, wherein the personal previous assessment information is obtained from the user input through a user interface of the mobile device or the wearable device.

15. The system of claim 12, wherein the individual's prior assessment information includes mental health information.

16. The system of claim 12, wherein the personal prior assessment information includes a description of the user's age and one or more physical characteristics of the user.

17. The system of claim 12, wherein the personal prior assessment information includes a description of at least one of clothing, footwear, mobility aids, or articles worn or carried by the user.

18. The system of claim 12, wherein the historical performance information includes at least one of heart rate, respiratory rate, body temperature, galvanic skin response, muscle activity, or user survey data.

19. The system of claim 12, wherein the current activity is one of walking, running, jogging, biking, strolling, or hiking.

20. The system of claim 12, wherein the activity assessment information includes at least one of route or path road conditions, elevation, slope, weather conditions, sun exposure, scenery, availability of supporting facilities, or traffic density.

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