Volume and intensity based activity assessment for devices

Abstract

Devices, systems, and methods for conducting a quantity and intensity based activity assessment are provided. A method can include determining, by a device, a heart rate. The method can include determining, based on the heart rate, a threshold amount of movement for a time period. The method can include determining movement data. The method can include comparing the movement data to the threshold amount of movement for the time period. The method can include determining, based on the comparison of the movement data to the threshold amount of movement for the time period, an activity intensity level associated with the heart rate and the movement data. The method can include determining, based on the activity intensity level, an activity score. The method can include transmitting data indicative of the activity score for presentation on a second device.

Description

[0001] Cross-references to related applications.

[0002] This application relates to and claims priority to U.S. non-provisional patent application No. 16 / 899,464, filed June 11, 2020, the disclosure of which is incorporated herein by reference as if it were described in its entirety. Background Technology

[0003] People are increasingly monitoring their activities and consumption habits to improve their health. Some activities that can be monitored include exercise, rest, and sedentary periods. People are interested in the amount of time they spend engaging in certain activities. However, some activity tracking methods using devices do not take into account activity intensity and the relationship between activity volume and intensity. Therefore, people can benefit from improved activity assessments using devices. Attached Figure Description

[0004] Figure 1 An exemplary system is shown that uses a device to perform activity evaluation based on quantity and intensity according to one or more exemplary embodiments of this disclosure.

[0005] Figure 2 An exemplary flowchart for conducting quantity and intensity-based activity evaluation is shown according to one or more exemplary embodiments of this disclosure.

[0006] Figure 3 An exemplary flowchart for conducting quantity and intensity-based activity evaluation is shown according to one or more exemplary embodiments of this disclosure.

[0007] Figure 4A An exemplary flowchart for conducting quantity and intensity-based activity evaluation is shown according to one or more exemplary embodiments of this disclosure.

[0008] Figure 4B An exemplary flowchart for conducting quantity and intensity-based activity evaluation is shown according to one or more exemplary embodiments of this disclosure.

[0009] Figure 5A A flowchart is shown of a process for conducting an activity assessment based on quantity and intensity, according to one or more exemplary embodiments of this disclosure.

[0010] Figure 5B A flowchart is shown of a process for conducting an activity assessment based on quantity and intensity, according to one or more exemplary embodiments of this disclosure.

[0011] Figure 6 A block diagram of an exemplary machine on which one or more technologies (e.g., methods) can be performed according to one or more exemplary embodiments of the present disclosure.

[0012] Certain embodiments will now be described more fully with reference to the accompanying drawings, in which various embodiments and / or aspects are illustrated. However, these aspects may be implemented in many different forms and should not be construed as limiting the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art. The same numerals in the drawings consistently denote the same elements. Thus, if a feature is used in several drawings, the numerals used to identify that feature first appearing in one drawing will be used in subsequent drawings. Detailed Implementation

[0013] Overview

[0014] The exemplary embodiments described herein provide certain systems, methods, and apparatuses for performing quantity- and intensity-based activity assessments.

[0015] Human activity can be assessed in a variety of ways. For example, user device data, such as accelerometer or other motion and / or location data, can provide an indication of a person's activity level (e.g., whether a person with the user device has moved a certain amount over a period of time). Biometric data, such as heart rate (HR), respiratory rate, pulse oximetry, etc., can indicate whether a person is asleep, inactive, or active. A combination of device and biometric data can provide an indication of a person's activity level over a period of time (e.g., a day or a week). Some activity monitoring technologies can be used for activity analysis without combining device and biometric data.

[0016] Not all activities are the same and contribute the same amount to human health. For example, an hour of light exercise can provide a different level of physical benefit than an hour of vigorous exercise. In this way, the duration of activity can provide an indication of a person's activity level, and the intensity of activity can provide additional insights.

[0017] Multiple thresholds can be used to measure activity levels. For example, activity exceeding a threshold (e.g., multiple steps) can indicate the level of activity a person is experiencing, and changes in heart rate (HR) or respiratory rate can also indicate the level of activity. In particular, more vigorous exercise over a longer period corresponds to more activity than less vigorous exercise over the same period or more vigorous exercise over a shorter period. The thresholds used in some activity measurement techniques may not take into account specific information about the individual or their environment, such as time of day, demographic information (e.g., the individual's age), the individual's health status, the individual's level of health, and other factors.

[0018] Tracking and presenting user activity can help users monitor their health and track activity goals. Some activity measurement technologies do not track multiple types of activity over a multi-day period and do not provide activity assessments that allow people to consider the different amounts of different activities over multiple days to achieve activity goals.

[0019] Therefore, people can benefit from enhanced methods that use quantity and intensity-based activity assessments to determine and present a person's activity intensity levels. In one or more embodiments, activity scores can account for different quantities and types of activity. For example, an activity score can measure a person's activity level over a period of time, including a period comprising multiple days (e.g., weeks). An activity score can account for the time a person spends at rest / sitting, the time a person spends at a low-intensity level of activity, the time a person spends at a moderate-intensity level of activity, and the time a person spends at a high / vigorous level of activity. In this way, instead of providing separate indications of how many steps a person walks or runs, how long they spend exercising, and how long they spend sitting, an activity score can account for each of these activities. For example, higher-intensity activities can be weighted higher than lower-intensity activities. Sitting time can be subtracted from activities at low, moderate, and vigorous activity intensity levels. Sleep time can be ignored and not subtracted from activities at low, moderate, and vigorous activity intensity levels.

[0020] In one or more embodiments, a threshold can be used to determine the activity intensity level. For example, a person's heart rate (HR) can be compared to a threshold HR. A person's amount of movement (e.g., multiple steps) can be compared to a movement threshold. A person's HR variation (e.g., over a period of time) can be compared to an HR variation threshold. Based on the amount of HR variation over a period of time, the device can determine whether the person is sitting or active at a light, moderate, or high intensity level. To determine a person's HR variation, the system can determine data from a previous period of time (e.g., three hours prior to the period being evaluated or another time measure) and can filter out any non-static time. In this way, the system can determine a person's resting HR as a baseline for HR variation measurement.

[0021] In one or more embodiments, the threshold used to determine the activity intensity level may depend on other data. The movement threshold used for a person at a first HR may be the same as or different from the movement threshold used for a person at a second HR. The HR change threshold used for a person at a first activity level may differ from the HR change threshold used for a person at a second activity level. For example, when a person's HR is below the first HR threshold for a period of time, the person's movement data for that period of time may be compared with one or more movement thresholds selected based on the HR being below the first HR threshold. When a person's HR is above the first HR threshold for a period of time, the person's movement data for that period of time may be compared with one or more movement thresholds selected based on the HR being above the first HR threshold. For example, when a person's HR is high, the movement threshold may be higher (e.g., 0-150 steps / min, >150 steps / min) than when a person's HR is low (e.g., the movement threshold may be 0-110 steps / min and >110 steps / min). In this way, to achieve high activity intensity, a person does not need to walk / run the same number of steps when their HR is low as when their HR is high. The HR change threshold may depend on the movement threshold. For example, a higher activity threshold (e.g., 150 steps / minute) would require a smaller change in heart rate (HR) than a lower activity threshold (e.g., 100 steps / minute) to achieve high intensity. In this way, the intensity level can depend on a combination of HR and activity data, and the activity score, based on the amount of time spent engaging in activity at different intensities, can also depend on a combination of HR and activity data. Therefore, the activity score can reflect the amount of activity at different intensities over a period of time (e.g., a week), and the determination of activity intensity within that period can be dynamic.

[0022] In one or more embodiments, the threshold used to determine the activity intensity level can be dynamic based on information about the person. With the user's consent and in compliance with applicable laws, the user may choose to access a system that determines and adjusts the threshold based on demographic data such as the person's age, past activity intensity levels, health, fitness level, etc. In this way, the amount and level of activity required for one person to reach moderate or high intensity may differ from that required for another person. The activity score can be customized for the user, rather than a "one-size-fits-all" approach.

[0023] In one or more embodiments, one or more devices may provide data for determining a person's activity score. For example, the devices may provide accelerometer or other motion data and may provide biometric data. For example, one or more devices may detect a person's HR data, and the same device or one or more other devices may detect motion data. HR and motion data may be collected by one of the multiple devices for analysis, or may be sent to a remote network (e.g., a cloud-based computing network) for analysis. The device or system may collect HR and motion data, may determine activity points for the activity score based on a person's HR selection model (e.g., a threshold) over a period of time, and may add and / or subtract activity points within a time period to determine the person's total activity score for that time period. The device or system may compare the person's total activity score with an activity target (e.g., a score threshold) to determine whether the person achieved the activity target within that time period, or how much additional activity (and at what intensity and duration) is needed to achieve the activity target.

[0024] In one or more embodiments, a device or system that collects HR and motion data and determines a person's activity score can present the person's real-time activity score against an activity goal, and / or can send such data to another device for presentation. In this way, the person's activity score, whether the activity score has reached the activity goal, how many activity points the person needs to reach the activity goal, and / or a suggested activity duration and intensity for the person to reach the activity goal can be presented on the device.

[0025] The above description is for illustrative purposes and is not intended to be limiting. Many other instances, configurations, processes, etc., may exist, some of which will be described in more detail below. Exemplary embodiments will now be described with reference to the accompanying drawings.

[0026] Exemplary processes and use cases

[0027] Figure 1 An exemplary system 100 for quantity and intensity-based activity evaluation using a device is shown according to one or more exemplary embodiments of this disclosure.

[0028] Reference Figure 1System 100 may include a user 102 with multiple devices (e.g., device 104, device 106, device 108). For example, user 102 may wear device 104 (e.g., a wristwatch) and device 106 (e.g., a ring device), and may hold or carry device 108 (e.g., a smartphone). In step 116 (e.g., a full-time period), user 102 may be stationary (e.g., sitting). In step 118 (e.g., a period of time), user 102 may be walking (e.g., light or moderate exercise). In step 120, user 102 may be walking or running on treadmill 122 (e.g., moderate or high-intensity exercise). Steps 116, 118, and 120 may represent different times of day or multiple days (e.g., a week, a month, etc.). User 102 may wear or hold any one or more of devices 104, 106, and / or 108 in any of steps 116, 118, and 120, or any one or more of devices 104, 106, and / or 108 may otherwise monitor user 102’s activities in a manner that is in accordance with the user’s consent and applicable laws, as further explained herein.

[0029] Still referencing Figure 1System 100 may include one or more servers 140 (e.g., cloud-based servers located remotely from devices 104, 106, and / or 108) that can receive data from any one or more of devices 104, 106, and / or 108 (e.g., corresponding to steps 116, 118, and / or 120). The data received by one or more servers 140 from any one or more of devices 104, 106, and / or 108 may include biometric data and / or device data (e.g., accelerometer data or other motion data captured by any one or more of devices 104, 106, and / or 108). One or more servers 140 may analyze the biometric and / or device data to determine the amount of activity performed by user 102 over a period of time (e.g., a week, a month, etc.). For example, one or more servers 140 may determine the amount of vigorous activity, moderate activity, light activity, resting activity, and / or any other quality or category of activity intensity level based on user 102's biometric and / or device data. One or more servers 140 can determine the amount of time user 102 spends exercising at vigorous / vigorous, moderate, or mild intensity levels of activity, and the amount of time user 102 spends sitting. One or more servers 140 can determine the total number and average number of steps performed by user 102 over a period of time (e.g., daily or weekly totals or averages). One or more servers 140 can use biometric data to determine resting heart rate (HR) and maximum HR (e.g., daily or weekly averages). One or more servers 140 may include one or more machine learning (ML) modules 142 that can determine activity levels and biometric levels, and can adjust the methods used for this determination as one or more ML modules 142 learns about user 102 (e.g., by adjusting thresholds for activity and biometrics). Alternatively, any of devices 104, 106, and / or 108 can collect device and / or biometric data and can perform assessments of activity intensity levels and biometric levels. One or more servers 140 and / or any one of devices 104, 106 and / or 108 can determine an activity score and provide activity information (e.g., including the activity score) to any one of devices 104, 106 and / or 108 for presentation.

[0030] In one or more embodiments, activity scores can be calculated for different amounts and types of activity. For example, an activity score can measure the level of activity of user 102 over a period of time, including a period of time comprising multiple days (e.g., weeks). Activity scores can calculate the time user 102 spends at rest / sitting (e.g., step 116), the time spent active at a low intensity level (e.g., step 118), the time spent active at a moderate intensity level (e.g., steps 118 and / or 120), and the time spent active at a high / vigorous intensity level (e.g., step 120). In this way, instead of providing separate indications of how many steps user 102 walked or ran, how long user 102 spent exercising, and how long user 102 spent sitting, a single activity score can be calculated for each of these activities. For example, higher intensity activities can be weighted more heavily than lower intensity activities. Sitting time can be subtracted from activities at low, moderate, and high intensity activity levels. Sleep time can be ignored so as not to be subtracted from activities at low, moderate, and vigorous intensity activity levels.

[0031] In one or more embodiments, a threshold can be used to determine the activity intensity level. For example, a user's heart rate (HR) can be compared to a threshold HR. The amount of movement a user makes (e.g., multiple steps) can be compared to a movement threshold. The change in a user's HR (e.g., over a time period) can be compared to an HR change threshold. Based on the amount of change in HR over a time period, a device (e.g., one or more servers 140 and / or any one of devices 104, 106, and / or 108) can determine whether user 102 is sitting or active at a light, moderate, or high intensity level.

[0032] In one or more embodiments, the threshold used to determine the activity intensity level may depend on other data. The movement threshold for user 102 in a first HR may be the same as or different from the movement threshold for user 102 in a second HR. The HR change threshold for user 102 at a first activity level may differ from the HR change threshold for user 102 at a second activity level. For example, when the user's HR is below the first HR threshold for a period of time, the user's movement data for the same period of time can be compared with one or more movement thresholds selected based on the HR being below the first HR threshold. When the user's HR is above the first HR threshold for a period of time, the user's movement data for the same period of time can be compared with one or more movement thresholds selected based on the HR being above the first HR threshold. For example, when the user's HR is high, the movement threshold may be higher (e.g., 0-150 steps / minute, >150 steps / minute) than when the user's HR is low (e.g., the movement threshold may be 0-110 steps / minute, and >110 steps / minute). In this way, to achieve high activity intensity, when the user's HR is low, user 102 does not need to walk / run the same number of steps as when the HR is high. The HR change threshold may depend on the movement threshold. For example, a higher exercise threshold (e.g., 150 steps / min) would require a smaller HR variation than a lower exercise threshold (e.g., 100 steps / min) to achieve high intensity. In this way, the intensity level can depend on a combination of HR and exercise data, and the activity score based on the amount of time spent active at different intensities can also depend on a combination of HR and exercise data. Therefore, the activity score can reflect the amount of activity at different intensities over a period of time (e.g., a week), and the determination of activity intensity within that period can be dynamic.

[0033] In one or more embodiments, the threshold for determining the activity intensity level based on information about user 102 can be dynamic. With the user's consent and in compliance with applicable laws, user 102 may choose to access a system that determines and adjusts the threshold based on demographic data such as user age, past activity intensity levels, and health.

[0034] In one or more embodiments, any of device 104, device 106, and / or device 108 may provide (e.g., to any of device 104, device 106, and / or device 108 and / or one or more servers 140) data for determining a user's activity score. For example, any of device 104, device 106, and / or device 108 may provide accelerometer or other motion data to each other and / or to one or more servers 140, and may provide biometric data. For example, any of device 104, device 106, and / or device 108 may detect HR data of user 102, and the same device or another of device 104, device 106, and / or device 108 may detect motion data. HR and motion data may be collected by one of these devices and / or one or more servers 140 for analysis. Any one or more servers 140 of devices 104, 106, and / or 108 can collect HR and motion data, select a model (e.g., a threshold) based on the user's HR over a time period, determine multiple activity points for the activity score based on the model, and add and / or subtract activity points over a time period to determine the user's total activity score for that time period. Any one or more servers 140 of devices 104, 106, and / or 108 can compare the user's total activity score with an activity target (e.g., a score threshold) to determine whether the user 102 achieved the activity target within the time period, or how much additional activity (and at what intensity and duration) is needed to achieve the activity target.

[0035] In one or more embodiments, any one or more servers 140 of devices 104, 106, and / or 108 that collect HR and motion data and determine a user's activity score can present the user's real-time activity score compared to an activity goal, and / or can send this data to any of devices 104, 106, and / or 108 for presentation. In this way, user 102 can be presented on the device with his / her activity score, whether the activity goal has been achieved, how many activity points the person might need to achieve the activity goal, and / or a suggested activity duration and intensity for user 102 to achieve the activity goal. Figure 1As shown, as an example, a user's vigorous activity time could be 5 minutes; moderate activity time could be 4 hours and 35 minutes; light activity time could be 1 hour and 39 minutes; seated time could be 15 hours and 6 minutes; average daily steps could be 9,847; resting activity level (HR) could be 83 (as a daily average); and maximum HR could be 124 (as a daily average). As further explained herein, this data could result in an activity score of 250 for that time period (e.g., one week). Since the target used for that time period (e.g., selected by user 102 or predetermined by any one or more servers 140, such as device 104, device 106, and / or device 108) can give user 102 a score of 300 activity points for that time period, the remaining activity points could be 50. The activity score, target score, and remaining points required to reach the target score can be presented by any of devices 104, 106, and / or 108, along with quantities assigned to different intensity levels based on the amount of time spent sitting at each activity intensity level. For example, 5 minutes of vigorous activity can generate 10 points; 4 hours and 35 minutes of moderate activity can generate 275 points; 1 hour and 39 minutes of light activity can generate 1 point; and 15 hours and 6 minutes of sitting time can generate -36 points. An activity score of 250 can be the sum of 10, 275, 1, and -36 points. In this way, activity scores can be calculated for multiple quantities of various activity levels over time, with the multiple quantities and levels of activity determined using a combination of device data and biometric data.

[0036] In one or more embodiments, device 104, device 106, device 108, and / or one or more servers 140 may include a personal computer (PC), a smart home device, a wearable wireless device (e.g., a bracelet, watch, glasses, ring, etc.), a desktop computer, a mobile computer, a laptop computer, or an Ultrabook. TM computers, laptop computers, tablet computers, server computers, handheld computers, handheld devices, Internet of Things (IoT) devices, sensor devices, PDA devices, handheld PDA devices, in-vehicle devices, non-in-vehicle devices, hybrid devices (e.g., combining cellular phone functionality with PDA device functionality), user equipment, vehicle devices, non-vehicle devices, mobile or portable devices, non-mobile or non-portable devices, mobile phones, cellular phones, PCS devices, PDA devices including wireless communication devices, mobile or portable GPS devices, DVB devices, relatively small computing devices, non-desktop computers, "Small Portable Reality Large" (CSLL) devices, ultra-mobile devices (UMD), ultra-mobile PCs (UMPC), mobile internet devices (MID), "origami" devices or computing devices, devices supporting dynamic synthetic computing (DCC), context-aware devices, video devices, audio devices, A / V devices, set-top boxes (STBs), Blu-ray disc (BD) players, BD recorders, digital video disc (DVD) players, high-definition (HD) DVD players, DVD recorders, HD DVD recorders, personal video recorders (PVRs), broadcast HD receivers, video sources, audio sources, video converters, audio converters, stereo tuners, broadcast radio receivers, flat panel displays, personal media players (PMPs), digital video cameras (DVCs), digital audio players, speakers, audio receivers, audio amplifiers, gaming devices, data sources, data converters, digital still cameras (DSCs), media players, smartphones, televisions, music players, etc. Other devices, including smart devices such as lights, climate controllers, automotive parts, home appliances, etc., can also be included in this list.

[0037] Devices 104, 106, 108, and / or one or more servers 140 can be configured to communicate wirelessly or wiredly via communication network 130 (e.g., the same or different wireless communication networks). Communication network 130 can be any combination of suitable communication networks of different types, such as broadcast networks, wired networks, public networks (e.g., the Internet), private networks, wireless networks, cellular networks, or any other suitable private and / or public networks. Furthermore, communication network 130 can have any suitable communication range associated with it and can include, for example, global networks (e.g., the Internet), metropolitan area networks (MANs), wide area networks (WANs), local area networks (LANs), or personal area networks (PANs). Additionally, communication network 130 can include any type of medium capable of carrying network transmissions, including but not limited to coaxial cable, twisted pair, optical fiber, hybrid fiber-coaxial (HFC) media, microwave terrestrial transceivers, radio frequency communication media, white space communication media, ultra-high frequency communication media, satellite communication media, or any combination thereof.

[0038] Figure 2 An exemplary flowchart 200 is shown for performing an activity evaluation based on quantity and intensity, according to one or more exemplary embodiments of the present disclosure.

[0039] Reference Figure 2 Flowchart 200 illustrates activities (e.g., by) based on biometric data and device (e.g., motion) data using various thresholds. Figure 1 The user 102 performs the level determination. In box 202, the device (e.g., collection) Figure 1 HR and motion data for user 102 Figure 1 Any one or more servers 140 of the illustrated devices 104, 106, and / or 108 can compare a user's HR with a first threshold HR. When biometric data indicates that the person's HR is less than or equal to the first threshold HR, the process can... Figure 3 The process continues. When biometric data indicates that the person's HR is greater than a first threshold HR, the process can continue in boxes 204 and / or 206. In box 204, when device data indicates that the user's movement reaches (e.g., is less than or equal to) a first movement threshold (the first movement threshold is based on HR being greater than the first threshold HR), the process can use one or more HR thresholds based on movement being less than or equal to the first movement threshold. In box 206, when device data indicates that the user's movement is greater than the first movement threshold, the process can proceed to box 208, where it can be determined that the HR and movement data indicate that the HR data is greater than the first threshold HR and the time when the movement data is greater than the first movement threshold indicates a level of intensity (e.g., vigorous activity).

[0040] Still referencing Figure 2In box 210, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the change in the user's HR (e.g., the difference between the user's HR at the first and second times) is less than the change in a first threshold HR (e.g., based on exercise reaching a first exercise threshold and / or HR being greater than the first threshold HR), the process may determine in box 212 that the HR and exercise data indicate that the user was at rest during the time period when the HR was greater than the first threshold HR and the exercise reached the first exercise threshold. In box 214, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the change in the user's HR is greater than or equal to the change in the first threshold HR and less than the change in the second threshold HR (e.g., based on exercise reaching the first exercise threshold and / or the change in the second threshold HR being greater than the first threshold HR), the process may determine in box 216 that the HR and exercise data indicate that the user was exercising at a light intensity during the time period when the HR was greater than the first threshold HR and the exercise reached the first exercise threshold. In box 218, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in a second threshold HR and less than the change in a third threshold HR (e.g., based on the movement reaching a first movement threshold and / or the HR being greater than the first threshold HR), the process may determine in box 220 that the HR and movement data indicate that the user is exercising at a moderate intensity during the time period when the HR is greater than the first threshold HR and the movement reaches the first movement threshold. In box 222, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in a third threshold HR, the process may determine in box 224 that the HR and movement data indicate that the user is exercising at a vigorous intensity during the time period when the HR is greater than the first threshold HR and the movement reaches the first movement threshold.

[0041] Figure 3 An exemplary flowchart 300 for conducting quantity and intensity-based activity evaluation is shown according to one or more exemplary embodiments of the present disclosure.

[0042] Reference Figure 3 Flowchart 300 illustrates the use of various thresholds to measure activity levels (e.g., by biometric data and device (e.g., motion) data) based on biometric data and device (e.g., motion) data. Figure 1 The determination was made by user 102. For example, regarding... Figure 2 As indicated, in box 202, the device (e.g., for collecting...) Figure 1 User 102's HR and exercise data Figure 1 Any one or more servers 140 of devices 104, 106, and / or 108 can compare a user's HR with a first threshold HR. When biometric data indicates that the person's HR is less than or equal to the first threshold HR, the process can... Figure 3 Continue in section 302. In box 302, the device (e.g., for collecting...) Figure 1 HR and motion data of user 102 Figure 1 Any one or more servers 140 of devices 104, 106, and / or 108 can compare a user's HR with a first threshold HR and a second threshold HR. When biometric data indicates that the person's HR is less than or equal to the first threshold HR and greater than or equal to the second threshold HR (e.g., within the range from the second threshold HR to the first threshold HR), the process can proceed to box 304. When biometric data indicates that the person's HR is less than the second threshold HR, the process can... Figure 4A Continuing. When biometric data indicates that the person's HR is within the range of a second threshold HR to a first threshold HR, the process can continue in boxes 304, 306, and / or 308. In box 304, when device data indicates that the user's movement reaches (e.g., is less than or equal to) a second movement threshold (the second movement threshold is based on HR within the range of a second threshold HR to a first threshold HR), the process can use one or more HR thresholds based on movement less than or equal to the second movement threshold. In box 306, when device data indicates that the user's movement is greater than the second movement threshold and reaches a third movement threshold (the third movement threshold is based on HR within the range of a second threshold HR to a first threshold HR), the process can use one or more HR thresholds based on movement greater than the second movement threshold and reaching the third movement threshold. For example, the process can continue in box 310, where the HR data and device data indicate that the user's activity is of moderate intensity. In box 308, when device data indicates that the user's movement is greater than the third movement threshold, the process can proceed to box 312, where it can be determined that the HR and movement data are at a high intensity level (e.g., vigorous activity).

[0043] Still referencing Figure 3In box 314, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change (e.g., the difference between the user's HR at the first and second times) is less than the change in a fourth threshold HR (e.g., the change in the fourth threshold HR is based on movement reaching a second movement threshold and / or HR being within the range from the second threshold HR to the first threshold HR), the process may determine in box 322 that the HR and movement data indicate that the user was stationary during the time period when the HR was within the range from the second threshold HR to the first threshold HR and the movement reached the second movement threshold. In box 316, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in the fourth threshold HR and less than the change in the fifth threshold HR (e.g., the change in the fifth threshold HR is based on movement reaching a second movement threshold and / or HR being within the range from the second threshold HR to the first threshold HR), the process may determine in box 324 that the user exercised at a mild intensity during the time period when the HR was within the range from the second threshold HR to the first threshold HR and when the movement reached the second movement threshold. In box 318, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in the fifth threshold HR and less than the change in the sixth threshold HR (e.g., the change in the sixth threshold HR is based on exercise reaching a second exercise threshold and / or HR being within the range from the second threshold HR to the first threshold HR), the process may determine in box 326 that the HR and exercise data indicate the user has exercised at a moderate intensity. In box 320, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in the sixth threshold HR, the process may determine in box 328 that the HR and exercise data indicate the user has exercised at a high intensity.

[0044] Figure 4A An exemplary flowchart 400 is shown for conducting quantity and intensity-based activity evaluation according to one or more exemplary embodiments of this disclosure.

[0045] Reference Figure 4A Flowchart 400 illustrates the use of various thresholds to measure activity levels (e.g., by biometric data and device (e.g., motion) data) based on biometric data and device (e.g., motion) data. Figure 1 The determination was made by user 102. For example, regarding... Figure 3 As indicated, in box 302, the device (e.g., for collecting...) Figure 1 User 102's HR and exercise data Figure 1Any one or more servers 140 of devices 104, 106, and / or 108 can compare a user's HR with a first threshold HR and a second threshold. When biometric data indicates that the person's HR is within the range from the first threshold HR to the second threshold HR, the process can... Figure 4A Continue. In box 402, the device (e.g., for collecting) Figure 1 HR and motion data of user 102 Figure 1 Any one or more servers 140 of devices 104, 106, and / or 108 can compare a user's HR with a second threshold HR. When biometric data indicates that the person's HR is less than the second threshold HR, the process can continue to boxes 406 and / or 408. In box 406, when device data indicates that the user's movement reaches (e.g., is less than or equal to) a fourth movement threshold (the fourth movement threshold is based on HR being less than the second threshold HR), the process can use one or more HR thresholds based on movement being less than or equal to the fourth movement threshold. At box 408, when device data indicates that the user's movement is greater than the fourth movement threshold, the process can use one or more HR thresholds based on movement being greater than the fourth movement threshold. For example, the process can continue to... Figure 4B .

[0046] Still referencing Figure 4AIn box 410, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change (e.g., the difference between the user's HR at the first and second times) is less than the change in the seventh threshold HR (e.g., the change in the seventh threshold HR is based on movement reaching the fourth movement threshold and / or HR being less than the second threshold HR), the process may determine in box 414 that the HR and movement data indicate the user is at rest. In box 416, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in the seventh threshold HR and less than the change in the eighth threshold HR (e.g., the change in the eighth threshold HR is based on movement reaching the fourth movement threshold and / or HR being less than the second threshold HR), the process may determine in box 418 that the HR and movement data indicate the user has exercised at a mild intensity. In box 420, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in the eighth threshold HR and less than the change in the ninth threshold HR (e.g., the change in the ninth threshold HR is based on exercise reaching the fourth exercise threshold and / or HR being less than the second threshold HR), the process may determine in box 422 that the HR and exercise data indicate the user exercised at a moderate intensity. In box 424, when HR data indicates (e.g., by comparing the user's HR at a first time with the user's HR at a second time) that the user's HR change is greater than or equal to the change in the ninth threshold HR, the process may determine in box 426 that the HR and exercise data indicate the user exercised at a vigorous intensity.

[0047] Figure 4B An exemplary flowchart 450 is shown for conducting an activity evaluation based on quantity and intensity, according to one or more exemplary embodiments of this disclosure.

[0048] Reference Figure 4B Flowchart 450 illustrates the use of various thresholds to measure activity levels (e.g., by biometric data and device (e.g., motion) data) based on biometric data and device (e.g., motion) data. Figure 1 The determination was made by user 102. For example, regarding... Figure 4A As indicated, in box 408, the device (e.g., for collecting...) Figure 1 HR and motion data of user 102 Figure 1 Any one or more servers 140 of devices 104, 106, and / or 108 can determine that motion data indicates the user's motion exceeds a fourth motion threshold. Based on the user's HR being less than a second threshold HR and the motion exceeding the fourth motion threshold, changes in one or more threshold HRs can be used to determine the activity intensity level.

[0049] Still referencing Figure 4BIn box 452, when HR data indicates an HR change less than the eleventh threshold HR change (e.g., an eleventh threshold HR change based on HR being less than the second threshold HR and / or motion being greater than the fourth motion threshold), the process may continue to box 454, where the evaluation device (e.g., collecting data for...) Figure 1 HR and motion data of user 102 Figure 1 Any one or more of devices 104, 106, and / or devices 108, and servers 140, can determine that biometric and device data indicate that the user is at rest. In box 456, when HR data indicates an HR change greater than or equal to the eleventh threshold and less than the twelfth threshold HR change (e.g., the twelfth threshold HR change is based on HR less than the second threshold HR and / or motion greater than the fourth motion threshold), the process can continue to box 458, where the evaluation device (e.g., collecting data for...) Figure 1 HR and motion data of user 102 Figure 1 The device 104, device 106, and / or device 108, or one or more of the server 140, can determine that biometric and device data indicate that the user has exercised at a mild intensity level. In box 464, when HR data indicates a change in HR greater than or equal to a change in the thirteenth threshold HR, the process can continue to box 466, where the assessment device (e.g., collecting data for...) Figure 1 HR and motion data of user 102 Figure 1 Any one or more of devices 104, 106 and / or 108 (or one or more servers 140) can determine that biometric and device data indicate that the user has exercised at a vigorous intensity level.

[0050] refer to Figure 2-4B Equipment (e.g., for collecting) Figure 1 HR and motion data of user 102 Figure 1 The server 140 (any one or more of devices 104, 106, and / or 108) can be based on the user's sitting position and / or at different activity intensity levels (e.g., for...). Figure 1(As explained above). At any time within a time period (e.g., over a week), a user's biometric and device data can be compared to various thresholds to determine whether the user was at rest or exercising at any intensity level at a particular time. The amount of activity score assigned to the user during periods of sitting and periods of exercising at any intensity level can vary. For example, the time increment for sitting can be multiplied by zero or a negative number; the time increment for exercising at a light intensity can be multiplied by a first positive number; the time increment for exercising at a moderate intensity can be multiplied by a second positive number (e.g., greater than the first positive number used for light intensity); and the time increment for exercising at a vigorous intensity can be multiplied by a third positive number (e.g., greater than the second positive number used for moderate intensity). For example, the time increment can be seconds, minutes, multiple seconds or multiple minutes, hours, etc. Each time increment for sitting or at any exercise intensity level can be multiplied by a negative, zero, and / or positive number and summed together to produce an activity score. In this way, the user's activity score can be updated over time (e.g., over a week) and can be compared to a goal over a time period. When the activity score is less than the target, the device can determine from the current activity score the number of activity points needed to reach the target (e.g., the difference between the activity score and the target), the average number of activity points needed to reach the target, and / or the average time increment required to generate enough activity points to reach the target at one or more intensity levels. The device can present the score information or send the score information to another device for presentation.

[0051] Still referencing Figure 2-4B The thresholds can be dynamic not only relative to each other, but can also vary based on time of day, day of week, and / or user data (e.g., user age, health, previous activity level, etc.). Any of the motion thresholds can be the same as or different from the motion threshold used for the HR range (e.g., Figure 3 , Figure 4A and / or Figure 4B The motion threshold in can be compared with Figure 2 (The movement thresholds may be the same or different, etc.). In this way, for different HR ranges, the threshold-based movement range can be the same and / or can vary, and can overlap with the threshold used for any HR threshold. For example, when a user's HR meets... Figure 3 When the range of frame 302 is within the range, the range of motion of frame 306 can be the same as that of frame 306. Figure 2 The range of motion from the first motion threshold in box 204 to the second motion threshold in box 206 overlaps, and so on. Any change in threshold HR can be the same as or different from the change in threshold HR used for the HR range (e.g., Figure 3 , Figure 4A and / or Figure 4B Changes in the threshold HR can be related to Figure 2(The changes in the threshold HR may be the same or different, etc.). In this way, for different HR ranges, the range of HR variation based on the threshold can be the same and / or can vary, and can overlap with the threshold used for any HR threshold. For example, when the user's movement satisfies Figure 3 When the range of box 306 is defined, the range of HR variation in box 316 can be compared with... Figure 2 The range of HR changes from the first threshold HR change in box 210 to the second threshold HR change in box 216 overlaps, and so on.

[0052] Referring again to Figure 2-4B, any number of thresholds can be used. For example, a person's HR can be compared to multiple HR ranges established by multiple HR thresholds. Any HR range can have one or more multiple motion thresholds. Although Figure 3 Three motion thresholds are shown; for example, more than three motion thresholds may correspond to HR or HR range. Figure 2-4B The number and combination of thresholds shown are examples and are not intended to be restrictive.

[0053] Still referencing Figure 2-4B To determine an individual's HR changes, the system can identify data from prior time periods (e.g., three hours prior to the time period being evaluated, or another time measure) and can filter out any non-static time periods. In this way, the system can determine an individual's static HR as a baseline for measuring HR changes.

[0054] Figure 5A A flowchart is shown of a process 500 for conducting an activity assessment based on quantity and intensity, according to one or more exemplary embodiments of the present disclosure.

[0055] In box 502, the device (e.g., Figure 1 Equipment 104, Figure 1 Equipment 106, Figure 1 Equipment 108, Figure 1 One or more servers 140) can identify a person (e.g., Figure 1 The device can receive the user's current heart rate (HR) and baseline HR. The device can also receive biometric sensor data, detected by the device's sensors or received from another device, indicating the person's HR at a given time or over a period of time (e.g., from one time to another). For example, HR could be a value measuring beats per minute (bpm). The device can compare the HR with one or more threshold HR values ​​(e.g.,...). Figure 2 Box 202, Figure 3 Box 302, Figure 4AThe threshold HR can be compared to box 404. One or more threshold HRs can be based on one or more criteria, such as a person's age, health status, time of day or day of week, past exercise data, etc. For example, the threshold HR could be maximum age-predicted heart rate (MPHR). In this way, in Figure 2 Frame 202 Figure 3 Box 302 and / or Figure 4A The multiple threshold HRs and their corresponding multiple HR ranges in box 404 can be multiple threshold MPHR values. The baseline HR can be based on data from a prior time period (e.g., three hours prior to the time period being evaluated, or another time period). The device can filter out any non-stationary time from the prior HR data. In this way, the device can determine the person's stationary HR as a baseline.

[0056] In box 504, the device can determine the motion threshold based on the current HR in box 502. For example... Figures 2-4B As shown, the motion threshold can depend on the threshold HR. For example, when the HR of box 502 is greater than 75% of MPHR, one or more motion thresholds used to determine whether HR indicates activity intensity level or whether the person is sitting can be different from the motion thresholds used to determine whether HR indicates activity intensity level or whether the person is sitting when the HR of box 502 is less than 75% of MPHR. In this way, the amount of exercise a person performs at high intensity when HR is higher than R (e.g., indicated by device data) can differ from the amount of exercise a person performs when HR is lower than R.

[0057] In box 506, the device can determine motion data. Motion data may include device data detected by the device (e.g., using an accelerometer, magnetometer, etc.) and / or device data received by another device. Motion data can indicate a person's level of activity at a specific time or within a time period. For example, when the device is wearable and / or receives motion data from a wearable device, the motion data can be an indication of a person's movement, such as the number of steps a person takes during a time period (e.g., minutes, hours, days, etc.).

[0058] In box 508, the device can determine that the motion data satisfies the motion thresholds in box 506. Satisfying a motion threshold can mean determining which motion threshold or motion thresholds the motion data satisfies. Figure 2 Box 204, Figure 2 Box 206, Figure 3 Box 304, Figure 3 Box 306, Figure 3 Box 308, Figure 4A Box 406 and Figure 4ABox 408 illustrates an example of comparing motion data to various motion thresholds to determine the range of motion that the motion data satisfies. For example, when only one motion threshold exists, satisfying that threshold may mean that the motion data is above or below (or within) that threshold. The threshold HR change used when the motion data is below the motion threshold may differ from the threshold HR change used when the motion data is above the motion threshold. When multiple motion thresholds exist, satisfying a motion threshold may mean being within a range of motion (e.g., zero to a first motion threshold, between a first and a second motion threshold, greater than a second motion threshold, etc.).

[0059] In box 510, the device can determine the threshold HR change based on the motion threshold. As described above in box 508, the threshold HR change used when motion data is below the motion threshold can differ from the threshold HR change used when motion data is above the motion threshold. The threshold HR change can also differ based on the HR in box 502. In this way, for example, Figure 2 The threshold HR changes in boxes 210, 214, 218, and 222 may differ from those in other boxes. Figure 3 Threshold HR changes in boxes 314, 316, 318, and 320. At higher HRs, the threshold indicating HR change for vigorous exercise (indicated by the change in HR from one time point to a later time point in box 502) may differ from the HR change threshold used for lower HRs. In this way, exercise and HR thresholds can vary based on each other and / or on user data, environmental data, etc. Over a multi-day period, for example, the thresholds can vary at different times, thus allowing the determination of the activity level for a person to be based on criteria that change during the period being evaluated.

[0060] In box 512, the device can determine the HR change associated with the baseline HR in box 502. For example, the device can use the HR data in box 502 to determine the change in a person's HR from HR1 at time t1 to HR2 at time t2. The HR change can be represented by the difference between HR2 and HR1. The HR change can be a measurement in bpm or it can be a percentage of MPRH (e.g., HR2-HR1 could indicate the percentage of MPRH of HR2 - the percentage of MPRH of HR1).

[0061] In box 514, the device can determine whether an HR change reaches the threshold HR change specified in box 510. Reaching the threshold HR change can refer to determining which HR change or which HR change thresholds the HR data meets. Figure 2 Box 210, Figure 2 Box 214, Figure 2 Box 218, Figure 2 Box 222, Figure 3 Box 314, Figure 3 Box 316, Figure 3 Box 318, Figure 3 Frame 320, Figure 4A Frame 410, Figure 4A Box 416, Figure 4A Frame 420, Figure 4A Box 424, Figure 4B Box 452, Figure 4B Box 456, Figure 4B The frame 460 and Figure 4B Box 464 illustrates an example of comparing HR change data to various HR change thresholds to determine the range of HR change data reached. For example, when only one threshold HR change exists, reaching the threshold HR change could mean that the HR change data is above or below the threshold (or at that threshold). When multiple HR change thresholds exist, reaching the threshold HR change could mean that it is within a range of HR change thresholds (e.g., zero to a first HR change threshold, between a first HR change threshold and a second HR change threshold, greater than a second HR change threshold, etc.).

[0062] In box 516, the device can determine the activity intensity level based on HR variation and / or motion data. For example, the HR variation and HR variation threshold in boxes 510, 512, and 514 can be optional, because motion data that meets the motion threshold can indicate the activity intensity level without taking HR variation into account. Figure 2 Box 208, Figure 3 Frame 310 and Figure 3 Box 312 is an example of when the satisfaction of the motion threshold corresponds to the activity intensity level regardless of changes in a person's HR. Figure 2 Box 212, Figure 2 Box 216, Figure 2 Frame 220, Figure 2 Box 224, Figure 3 Box 314, Figure 3 Box 316, Figure 3 Box 318, Figure 3 Frame 320, Figure 4A Box 414, Figure 4A Box 418, Figure 4A Box 422, Figure 4A Box 426, Figure 4B Box 454, Figure 4B Box 458, Figure 4B box 462 and Figure 4B Box 466 illustrates an example of an activity intensity level indicated by reaching one or more motion thresholds and one or more HR change thresholds. Activity intensity levels can include seated / resting intensity, mild intensity activity, moderate intensity activity, vigorous / high intensity activity, etc.

[0063] In box 518, the device can determine an activity score based on the activity intensity level. The device can determine the activity score based on the amount of time the user spends sitting and / or exercising at different activity intensity levels (e.g., as described above regarding...). Figure 1 (As explained). When a user exercises at any intensity level, the activity score assigned to the period of sitting and the period of exercising at any intensity level can vary. For example, the time increment for sitting can be multiplied by zero or a negative number; the time increment for exercising at a light intensity can be multiplied by a first positive number; the time increment for exercising at a moderate intensity can be multiplied by a second positive number (e.g., greater than the first positive number for light intensity); and the time increment for exercising at a vigorous intensity can be multiplied by a third positive number (e.g., greater than the second positive number for moderate intensity). For example, the time increment can be seconds, minutes, multiple seconds or minutes, hours, etc. Each time increment for sitting or at any exercise intensity level can be multiplied by a negative, zero, and / or positive number and added together to produce an activity score. In this way, the user's activity score can be updated over time (e.g., over a week) and can be compared to a target for a period of time. Exercise and HR measurements can be taken within the time increment (e.g., every 30 seconds). In this way, the activity score can mean including multiple periods of time for increasing exercise and HR measurements. For example, five minutes of activity at a certain intensity level may include ten heart rate (HR) and motion measurements, which indicate to the user that they are active at a certain intensity level for a duration of five minutes.

[0064] In box 520, the device can present data indicating the activity score, or it can send data indicating the activity score to another device for presentation. For example, the presented data can be as follows: Figure 1 As shown, when the activity score is less than the target (e.g., a target activity score for a time period), the device can determine from the current activity score (e.g., the difference between the activity score and the target) the value of the activity score needed to reach the target, the average of the activity scores needed to reach the target, and / or the average of the time increments required to generate sufficient activity scores at one or more intensity levels to reach the target. The device can present the score information or send the score information to another device for presentation. The activity score can be used for the entire duration (e.g., for activities over a week) or can indicate whether a person is synchronizing within that duration (e.g., whether a person is accumulating enough activity scores each day to progressively reach a weekly goal). In this way, activity score data can provide users with real-time updates to provide incremental goals and feedback that enable users to achieve their activity goals.

[0065] Figure 5B A flowchart is shown of a method 550 for performing quantity and intensity-based activity evaluation according to one or more exemplary embodiments of the present disclosure.

[0066] In box 552, the device (e.g., Figure 1 Equipment 104, Figure 1 Equipment 106, Figure 1 Equipment 108, Figure 1 One or more servers 140 can determine a first amount of activity at a first activity intensity level. In box 554, the device can determine a second amount of activity at a second activity intensity level. In box 556, the device can determine a third amount of activity at a third activity intensity level. In box 558, the device can determine a fourth amount of activity at a fourth activity intensity level. For example, the first, second, and third activity intensity levels can be light intensity, medium intensity, or strong / high intensity (e.g., as...). Figure 2 , Figure 3 , Figure 4A and / or Figure 4B The fourth activity intensity level can indicate a person's (e.g., Figure 1 User 102) is sitting / still (e.g., as used by Figure 2 , Figure 3 , Figure 4A and / or Figure 4B (As determined). The motion and HR measurements used to determine the activity intensity level can be performed in time increments (e.g., every 30 seconds). In this way, multiple activity intensity levels can mean including multiple increments of time periods for motion and HR measurements. For example, five minutes of activity at one activity intensity level could include ten HR and motion measurements, indicating that the user was active at one intensity level over a five-minute duration.

[0067] The activity intensity levels in boxes 552, 554, 556, and 558 can correspond to biometric and device data (e.g., accelerometer data indicated by the device and / or another device) at multiple time points. In this way, the device can determine an activity score based on biometric and device data at different time points over a period of time (e.g., a week). For example, a first quantity of activity at a first activity intensity level can correspond to the amount of time the person is exercising at that first activity intensity level, as indicated by the person's biometric and device data. A second quantity of activity at a second activity intensity level can correspond to the amount of time the person is exercising at that second activity intensity level, as indicated by the person's biometric and device data. A third quantity of activity at a third activity intensity level can correspond to the amount of time the person is exercising at that third activity intensity level, as indicated by the person's biometric and device data. A fourth quantity of activity at a fourth activity intensity level can correspond to the amount of time the person is sitting, as indicated by the person's biometric and device data. The quantities used for activity intensity levels (e.g., the amount of time) can vary. For example, the first quantity could indicate that a person is walking for sixty minutes. The second quantity could indicate that a person is jogging for thirty minutes. The third quantity can indicate that a person has been running for fifteen minutes. Any of the activity intensity levels can be the same (for example, multiples of the first, second, and third activity intensity levels can represent moderate activity, and the corresponding quantities can represent different times when a person is active at a moderate intensity level).

[0068] In box 560, the device can determine the sum of quantities of non-sitting activity levels. For example, when the first, second, and third activity intensity levels in boxes 552, 554, and 556 indicate non-sitting activity levels (e.g., light activity, moderate activity, and / or vigorous / high activity), the time quantities or activity scores corresponding to the time quantities can be added together over a duration. For example, all non-sitting activities over a multi-day or weekly period can be added together. The amount of time a person does not sleep and / or sit during a time period can be added together and then converted into activity scores, or activity scores corresponding to any amount of time a person does not sleep and / or sit during a time period can be added together.

[0069] At box 562, the device can subtract a fourth quantity of activity (e.g., sitting activity time or corresponding minutes) from the sum of non-sitting time or minutes at box 560. Because an increment in time (e.g., the fourth quantity) can correspond to a negative activity score while the person is sitting during that time, the sitting time or minutes can be subtracted (or negative scores can be included in the sum of all activity quantities). The device can include multiple sitting activity quantities in the sum, either by adding all sitting quantities and subtracting the added sitting quantities from the added non-sitting quantities, or by subtracting a separate sitting quantity from a separate non-sitting quantity.

[0070] In box 564, the device can determine activity scores for a time period based on summations and subtractions (e.g., the sum of positive activity scores for non-seated activity and negative activity scores for seated activity). For example, 5 minutes of vigorous activity might generate 10 points; 4 hours and 35 minutes of moderate activity might generate 275 points; 1 hour and 39 minutes of light activity might generate 1 point; and 15 hours and 6 minutes of sedentary time might generate -36 points. An activity score of 250 points could be the sum of 10, 275, 1, and -36. In this way, activity scores can be calculated as multiple quantities of multiple activity levels varying over time, with the activity amount and level determined using a combination of device data and biometric data.

[0071] In box 566, the device can determine whether the activity score meets a score threshold (e.g., a target score). The target score can be set by the person for whom the activity score is calculated, determined by the device based on that person's past activity data / scores, or selected from a template. For example, the template can set HR and activity thresholds and their correlation with different activity intensity levels, and the device can select a template randomly or based on information about the person (e.g., the person's age and / or health) and / or based on environmental information, such as the time of year (e.g., month or quarter), weather, etc. Meeting the score threshold can refer to whether the activity score is higher or lower than the target score. For example, when the activity score is 250 and the target score is 300, the device can determine that the person needs an additional 50 points to achieve the target score, and proceed to box 568. When the target score is 250 or less, the device can determine that the 250 activity score has been met, and proceed to box 570. Activity scores can be for the entire duration (e.g., activities over a week), or they can indicate whether a person is making progress within that duration (e.g., whether a person has earned enough activity scores each day to progressively reach their weekly goal). In this way, activity score data can be provided to users in real time with incremental goals and feedback that enable them to achieve their activity objectives.

[0072] In box 568, when the activity score has not yet reached the target score (e.g., a person needs more activity points to achieve the target score), the device may present or send an indication to another device used for presentation that the activity score has not met the score threshold (e.g., the target score). Figure 1An example of this scenario is shown, where the target score is 300 points and the activity score is 250 points. The device can present or send to another device used for presentation the activity score, the target score, and the remaining activity points (and / or corresponding amounts of activity at different activity intensity levels) that the person needs to achieve the target score and meet the score threshold. The device can present or send to another device used for presentation the activity score, the first, second, third, and fourth amounts of the activity, and / or the activity points corresponding to the first, second, third, and fourth amounts of the activity. Box 568 can provide real-time updates indicating that the person has not yet acquired multiple activity points (e.g., for a day) to progressively achieve the target score (e.g., for a week).

[0073] In box 570, when the activity score has reached the target score (e.g., the person has exercised sufficiently to meet or exceed the score threshold), the device can present or send to another device for presentation the activity score, the target score, an indication that the activity score has met or exceeded the target score, the time when the activity score met or exceeded the target score, the first, second, third, and fourth quantities of the activity, and / or the activity scores corresponding to the first, second, third, and fourth quantities of the activity. Box 570 can provide real-time updates indicating that the person has acquired multiple activity scores (e.g., for a day) to progressively achieve the target score (e.g., for a week).

[0074] In one or more embodiments, based on the activity score and whether the activity score exceeds a target score, the device can adjust the threshold of the template, generate new templates with different thresholds, and / or select different templates with different thresholds for the next time period during which the person's activity score is determined. For example, when the person reaches the target score, the device can modify, generate, or select another template with a higher threshold for the next activity assessment time period (e.g., more activity is needed to achieve a high / vigorous intensity activity level). When the person fails to reach the target score, the device can modify, generate, or select another template with a lower threshold for the next activity assessment time period (e.g., less activity is needed to achieve a high / vigorous intensity activity level). Alternatively or additionally, the device can use a different target score for the next activity assessment time period (e.g., as determined by the template or other means). For example, when the person reaches the target score, the device can modify, generate, or select another template with a higher target score for the next activity assessment time period. When the person fails to reach the target score, the device can modify, generate, or select another template with a lower target score for the next activity assessment time period.

[0075] Still referencing Figure 5BThe amount of activity, measured by its intensity level, can be based on user input. For example, not all activities can be identified by the device; for instance, is a person lifting weights or running? A person may not have a device to measure their activity level while performing an activity, or the device may not be able to distinguish all types of activities. In this way, the device can receive user input, which allows the person to indicate the activity being performed and the duration of that activity. For example, when a person provides user input to the device indicating the type of activity and its duration (e.g., lifting weights for one hour), the device can determine an activity score corresponding to one hour of weightlifting activity and can calculate the weightlifting activity score while determining the activity score.

[0076] The descriptions in this article are not intended to be restrictive.

[0077] Figure 6 Machine 600 is shown (e.g., Figure 1 Equipment 104 Figure 1 Equipment 106 Figure 1 Equipment 108 Figure 1 A block diagram illustrating examples of one or more servers 140) or systems performing any one or more techniques (e.g., methods) discussed herein. In other embodiments, machine 600 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, machine 600 may operate as a server, client, or both in a server-client network environment. In one example, machine 600 may act as a peer machine in a Wi-Fi Direct, peer-to-peer (P2P), cellular, (or other distributed) network environment. Machine 600 may be a server, personal computer (PC), smart home device, tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, wearable computing device, network device, network router, switch, or bridge, or any machine capable of executing instructions (sequences or others) specifying the actions to be performed by that machine (e.g., a base station). Furthermore, although only a single machine is shown, the term "machine" should also be considered to include any collection of machines that individually or jointly execute a set (or more sets) of instructions to perform any one or more methods discussed herein, such as cloud computing, software as a service (SaaS), or other computer group configurations.

[0078] As described herein, instances may include or logically operate multiple components, modules, or mechanisms. A module is a tangible entity (e.g., hardware) capable of performing a specified operation at operational time. Modules include hardware. In one example, the hardware may be specifically configured to perform a particular operation (e.g., hardwiring). In another instance, the hardware may include configurable execution units (e.g., transistors, circuitry, etc.) and a computer-readable medium containing instructions that configure the execution units to perform a specific operation at operational time. This configuration may be performed under the guidance of the execution units or loading mechanisms. Thus, when the device operates, the execution units are communicatively coupled to the computer-readable medium. In this example, the execution unit may be a member of more than one module. For example, in operation, the execution unit may be configured by a first set of instructions to execute a first module at a point in time, and may be reconfigured by a second set of instructions to execute a second module at a second point in time.

[0079] Machine (e.g., computer system) 600 may include a hardware processor 602 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), main memory 604, and static memory 606, some or all of which may communicate with each other via an interconnect (e.g., bus) 608. Machine 600 may also include a power management device 632, a graphics display device 610, an alphanumeric input device 612 (e.g., a keyboard), and a user interface (UI) navigation device 614 (e.g., a mouse). In one example, the graphics display device 610, the alphanumeric input device 612, and the UI navigation device 614 may be a touchscreen display. Machine 600 may also include a storage device (i.e., a drive) 616, a signal generation device 618, and one or more activity evaluation modules 619 (e.g., capable of evaluating based on...). Figure 2 -5 (the execution steps in the box), a network interface device / transceiver 620 coupled to antenna 630, and one or more sensors 628 (e.g., HR sensor), Global Positioning System (GPS) sensor, compass, accelerometer, or other biometric and / or motion sensor. Machine 600 may include an output controller 634, such as serial (e.g., Universal Serial Bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connections, to communicate with or control one or more peripheral devices (e.g., printer, card reader, etc.).

[0080] Storage device 616 may include machine-readable medium 622 on which one or more sets of data structures or instructions 624 (e.g., software) are stored, which implement or be used by any one or more of the technologies or functions described herein. Instructions 624 may also reside wholly or at least partially within main memory 604, static memory 606, or hardware processor 602 during execution by machine 600. In one example, one or any combination of hardware processor 602, main memory 604, static memory 606, or storage device 616 may constitute a machine-readable medium.

[0081] Although machine-readable medium 622 is shown as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) configured to store one or more instructions 624.

[0082] Various embodiments can be implemented wholly or partially in software and / or firmware. The software and / or firmware may take the form of instructions contained in or on a non-transitory computer-readable storage medium. These instructions can then be read and executed by one or more processors to perform the operations described herein. The instructions can be in any suitable form, such as, but not limited to, source code, compiled code, interpreted code, executable code, static code, dynamic code, etc. Such computer-readable medium may include any tangible non-transitory medium for storing information in one or more computer-readable forms, such as, but not limited to, read-only memory (ROM); random access memory (RAM); disk storage media; optical storage media; flash memory, etc.

[0083] The term "machine-readable medium" can include any medium capable of storing, encoding, or carrying instructions executable by machine 600 and causing machine 600 to perform any one or more of the techniques disclosed herein, or any medium capable of storing, encoding, or carrying data structures used by or associated with such instructions. Examples of non-limiting machine-readable media can include solid-state memory and optical and magnetic media. In one instance, mass machine-readable media includes machine-readable media having a plurality of particles having rest masses. Specific examples of mass machine-readable media can include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM) or electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable hard disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0084] Instruction 624 can also be sent or received via communication network 626 using a transmission medium through network interface device / transceiver 620 using any of a plurality of transport protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Exemplary communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), simple old-fashioned telephone (POTS) networks, wireless data networks (e.g., referred to as…). The Institute of Electrical and Electronics Engineers (IEEE) 602.11 standard series is called... The IEEE 602.16 series of standards, the IEEE 602.15.4 standard system, and peer-to-peer (P2P) networks, etc. In one example, the network interface device / transceiver 620 may include one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas to connect to the communication network 626. In one example, the network interface device / transceiver 620 may include multiple antennas to perform wireless communication using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. The term "transmission medium" should be considered to include any intangible medium capable of storing, encoding, or carrying instructions executed by machine 600 and comprising digital or analog communication signals, or other intangible media facilitating communication of such software.

[0085] The operations and procedures described and illustrated above can be performed or executed in any suitable order desired in various implementations. Furthermore, in some implementations, at least a portion of the operations can be performed in parallel. Additionally, in some implementations, fewer or more operations than described can be performed.

[0086] The word "exemplary" is used herein to mean "as an example, instance, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The terms "computing device," "user equipment," "communication station," "station," "handheld device," "mobile device," "wireless device," and "user equipment" (UE) as used herein refer to wireless communication devices such as cellular phones, smartphones, tablets, web readers, wireless terminals, laptops, home base stations, high data rate (HDR) subscriber stations, access points, printers, point-of-sale equipment, access terminals, or other personal communication system (PCS) devices. Devices can be mobile or fixed.

[0087] As used herein, the term "communication" is intended to include sending, receiving, or both. While this is particularly useful in claims when describing the arrangement of data sent by one device and received by another, only the functionality of one of these devices infringes the claim. Similarly, a bidirectional data exchange between two devices (where both devices send and receive during the exchange) can be described as "communication" when only the functionality of one of those devices is claimed. The term "communication" as used herein with respect to wireless communication signals includes sending and / or receiving wireless communication signals. For example, a wireless communication unit capable of transmitting wireless communication signals may include a wireless transmitter that sends wireless communication signals to at least one other wireless communication unit, and / or a wireless communication receiver that receives wireless communication signals from at least one other wireless communication unit.

[0088] As used herein, unless otherwise stated, the use of ordinal adjectives such as “first,” “second,” “third,” etc., to describe common objects merely indicates that different instances of the same object are being mentioned and is not intended to imply that the objects described in this way must be in a particular order in time, space, sequence, or any other way.

[0089] Some embodiments can be used in conjunction with a variety of devices and systems, such as personal computers (PCs), desktop computers, mobile computers, laptop computers, notebook computers, tablet computers, server computers, handheld computers, handheld devices, personal digital assistant (PDA) devices, handheld PDA devices, in-vehicle devices, non-in-vehicle devices, hybrid devices, vehicle devices, non-vehicle devices, mobile or portable devices, consumer devices, non-mobile or non-portable devices, wireless communication stations, wireless communication devices, wireless access points (APs), wired or wireless routers, wired or wireless modems, video devices, audio devices, audio-video (A / V) devices, wired or wireless networks, wireless local area networks, wireless video local area networks (WVANs), local area networks (LANs), wireless LANs (WLANs), personal area networks (PANs), wireless PANs (WPANs), etc.

[0090] Some embodiments may incorporate one-way and / or two-way wireless communication systems, cellular wireless telephone communication systems, mobile phones, cellular phones, wireless phones, personal communication system (PCS) devices, PDA devices including wireless communication devices, mobile or portable global positioning system (GPS) devices, devices including GPS receivers or transceivers or chips, devices including RFID elements or chips, multiple-input multiple-output (MIMO) transceivers or devices, single-input multiple-output (SIMO) transceivers or devices, multiple-input single-output (MISO) transceivers or devices, devices with one or more internal antennas and / or external antennas, digital video broadcasting (DVB) devices or systems, multi-standard wireless devices or systems, wired or wireless handheld devices, such as smartphones, Wireless Application Protocol (WAP) devices, etc.

[0091] Some embodiments can be used in conjunction with one or more types of wireless communication signals and / or systems following one or more wireless communication protocols, such as radio frequency (RF), infrared (IR), frequency division multiplexing (FDM), orthogonal FDM (OFDM), time division multiplexing (TDM), time division multiple access (TDMA), extended TDMA (E-TDMA), General Packet Radio Service (GPRS), extended GPRS, code division multiple access (CDMA), wideband CDMA (WCDMA), CDMA 2000, single-carrier CDMA, multi-carrier CDMA, multi-carrier modulation (MDM), discrete multi-tone (DMT). Global Positioning System (GPS), Wi-Fi, Wi-Max, ZigBee, Ultra Wideband (UWB), Global System for Mobile Communications (GSM), 2G, 2.5G, 3G, 3.5G, 4G, 5G mobile networks, 3GPP, Long Term Evolution (LTE), LTE Advanced, Enhanced Data Rates GSM Evolution (EDGE), etc. Other embodiments can be used in a variety of other devices, systems, and / or networks.

[0092] Example 1 could be a method comprising: determining a heart rate associated with the conduct of an activity by at least one processor of a first device; determining a threshold exercise intensity by the at least one processor and based on the heart rate; determining device accelerometer data by the at least one processor; comparing the device accelerometer data with the threshold exercise intensity by the at least one processor; determining a threshold heart rate change by the at least one processor and based on the comparison of the device accelerometer data with the threshold exercise intensity; determining a heart rate change associated with the heart rate by the at least one processor; comparing the heart rate change with the threshold heart rate change by the at least one processor; determining an activity intensity level associated with the heart rate and the device accelerometer data by the at least one processor and based on the comparison of the heart rate change with the threshold heart rate change by the at least one processor; determining an activity score by the at least one processor and based on the activity intensity level; and causing data indicating the activity score to be presented by the at least one processor.

[0093] Example 2 may include the method of Example 1 and / or some other examples herein, wherein the heart rate is a first heart rate associated with performing a first activity during a first time period, wherein the threshold exercise for the first time period is a first threshold exercise for the first time period, wherein the device accelerometer data is first device accelerometer data, wherein the threshold heart rate change is a first threshold heart rate change, wherein the heart rate change is a first heart rate change, and wherein the activity intensity level is a first activity intensity level, the method further comprising: determining a second heart rate associated with performing a second activity during a second time period; determining a second threshold exercise for the second time period based on the second heart rate; determining second device accelerometer data; comparing the second device accelerometer data with the second threshold exercise; determining a second threshold heart rate change based on the comparison of the second device accelerometer data with the second threshold exercise; determining a second heart rate change associated with the second heart rate and the second time period; and determining a second activity intensity level associated with the second heart rate and the second device accelerometer data based on the comparison of the second heart rate change with the second threshold heart rate change, the second activity intensity level being different from the first activity intensity level, wherein the activity score is further based on the second activity intensity level.

[0094] Example 3 may include the method of Example 1 and / or some other examples herein, further including: determining a heart rate threshold; comparing a heart rate with a heart rate threshold; wherein the determination of the threshold exercise is based on the comparison of heart rate with a heart rate threshold.

[0095] Example 4 may include the method of Example 1 and / or some other examples herein, wherein the activity score is a first activity score associated with the user and activities performed in a first time period, and the method further includes determining a second activity score associated with the user and activities performed in a second time period, the second time period occurring before the first time period, wherein determining the threshold amount of movement for the first time period is also based on the second activity score.

[0096] Example 5 could be a method comprising: determining a heart rate by at least one processor of the device; determining an exercise threshold by the at least one processor and based on the heart rate; determining exercise data by the at least one processor; comparing the exercise data with the exercise threshold by the at least one processor; determining an activity intensity level by the at least one processor and based on the comparison of the exercise data with the exercise threshold; determining an activity score by the at least one processor and based on the activity intensity level; and causing data indicating the activity score to be presented by the at least one processor.

[0097] Example 6 may include the method of Example 5 and / or some other examples herein, further including: determining the change in threshold heart rate based on a comparison of exercise data with an exercise threshold; determining the heart rate change associated with a time period; and comparing the heart rate change with the change in threshold heart rate, wherein determining the activity intensity level is further based on the comparison of the heart rate change with the change in threshold heart rate.

[0098] Example 7 may include the method of Example 5 and / or some other examples herein, further including: comparing an activity score to a score threshold; determining that the activity score exceeds the score threshold, wherein the data further indicates that the activity score exceeds the score threshold.

[0099] Example 8 may include the method of Example 7 and / or some other examples herein, wherein the fractional threshold is a first fractional threshold, and the method further includes determining a second fractional threshold greater than the first fractional threshold.

[0100] Example 9 may include the method of Example 5 and / or some other examples herein, further including: comparing an activity score to a score threshold; determining that the activity score is less than the score threshold; and determining a difference between the activity score and the score threshold, wherein the data further indicates the difference between the activity score and the score threshold.

[0101] Example 10 may include the method of Example 9 and / or some other examples herein, wherein the fractional threshold is a first fractional threshold, and the method further includes determining a second fractional threshold that is smaller than the first fractional threshold by an amount associated with the difference.

[0102] Example 11 may include the method of Example 5 and / or some other examples herein, wherein heart rate and exercise data are associated with a user, the method further comprising: determining a second heart rate associated with the user; determining second exercise data associated with the user; and determining a second activity intensity level associated with the second heart rate and the exercise data, the second activity intensity level being different from the activity intensity level, wherein the determination of an activity score is further based on the second activity intensity level.

[0103] Example 12 may include the method of Example 11 and / or some other examples herein, further including determining a sum of a first score associated with the activity intensity level and a second score associated with the second activity intensity level, wherein the determination of the activity score is further based on the sum.

[0104] Example 13 may include the method of Example 5 and / or some other examples herein, wherein the heart rate is a first heart rate associated with a user, the method further comprising: determining a second heart rate associated with the user; determining second motion data associated with the user; and determining a second activity intensity level associated with the second heart rate and the motion data, wherein the second activity intensity level indicates that the user was sitting during a time period associated with the second heart rate; and determining a negative activity score based on the second activity intensity level, wherein determining the activity score is further based on the negative activity score.

[0105] Example 14 may include the method of Example 5 and / or some other examples herein, further including: determining a heart rate threshold; and comparing a heart rate with a heart rate threshold, wherein determining the exercise threshold is based on the comparison of the heart rate with the heart rate threshold.

[0106] Example 15 may include the method of Example 14 and / or some other examples herein, wherein the activity score is a first activity score associated with a first time period, wherein the heart rate is associated with the user, and wherein the determination of the heart rate threshold is based on at least one of data or environmental data associated with the user, wherein the data associated with the user includes at least one of the user's age or a second activity score associated with a second time period prior to the first time period.

[0107] Example 16 may include the method of Example 5 and / or some other examples herein, further including: receiving user input including an activity type and a duration associated with the activity; and determining a second activity intensity level based on the user input, wherein the activity score is determined further based on the second activity intensity level.

[0108] Example 17 may include the method of Example 5 and / or some other examples herein, wherein the activity score is a first activity score associated with a first time period, and the method further includes: determining a second activity score associated with a second time period prior to the first time period, wherein determining the first activity score is further based on the second activity score.

[0109] Example 18 may include the method of Example 5 and / or some other examples herein, wherein heart rate and motion data are associated with a user, a first time period, and a first heart rate change, and wherein the motion data is first motion data. The method further includes: determining a second heart rate associated with a user and a second time period; determining second motion data associated with the user and the second time period; determining a threshold heart rate change for the second time period based on the second motion data; determining a second heart rate change associated with the second heart rate and the second time period; and determining a second activity intensity level associated with the second heart rate and the second motion data based on a comparison of the second heart rate change with the threshold heart rate change, wherein the second activity intensity level is different from the activity intensity level, wherein: the activity score is determined further based on the second activity intensity level, and at least one of the following: the first heart rate is different from the second heart rate, the first motion data is different from the second motion data, or the first heart rate change is different from the second heart rate change.

[0110] Example 19 may include a device comprising memory coupled to at least one processor configured to: determine a heart rate associated with a user; determine an exercise threshold for a time period based on the heart rate; determine device data associated with a second device and the user; compare the device data with the exercise threshold for the time period; determine a heart rate variability associated with the heart rate and the time period; determine an activity intensity level associated with the heart rate and the device data based on the heart rate variability; determine an activity score based on the activity intensity level; and transmit data indicating the activity score for presentation at the second device.

[0111] Example 20 may include the device of Example 19 and / or some other example herein, wherein the at least one processor is configured to: determine a heart rate threshold; and compare a heart rate with a heart rate threshold, wherein the determination of an exercise threshold for the time period is based on the comparison of the heart rate with the heart rate threshold.

[0112] Example 21 may include a device comprising means for performing the following operations: determining a heart rate associated with a user; determining an exercise threshold for a time period based on the heart rate; determining device data associated with a second device and the user; comparing the device data with the user's exercise threshold for the time period; determining a heart rate variability associated with the heart rate and the time period; determining an activity intensity level associated with the heart rate and the device data based on the heart rate variability; determining an activity score based on the activity intensity level; and transmitting data indicating the activity score for presentation at the second device.

[0113] Example 22 may include one or more non-transitory computer-readable media comprising instructions that cause an electronic device, when executed by one or more processors of the electronic device, to perform one or more elements of a method described or associated with any of Examples 1-21 or any other method or process described or associated with herein.

[0114] Example 23 may include an apparatus comprising logic, modules, and / or circuitry to perform one or more elements of a method described or associated with any of Examples 1-21 or any other method or process described or associated with herein. Example 24 may include a method, technique, or process described or associated with any of or a portion thereof as described in any of Examples 1-21.

[0115] Example 25 may include an apparatus comprising: one or more processors and one or more computer-readable media including instructions that, when executed by the one or more processors, cause the one or more processors to perform the methods, techniques, or processes described or associated with any one or more of Examples 1-21.

[0116] It should be understood that the above description is for illustrative purposes and not for limitation.

[0117] Although specific embodiments of this disclosure have been described, those skilled in the art will recognize that many other modifications and alternative embodiments are also within the scope of this disclosure. For example, any functionality and / or processing capabilities described with respect to a particular device or component can be performed by any other device or component. Furthermore, while various exemplary implementations and architectures have been described with respect to embodiments of this disclosure, those skilled in the art will understand that many other modifications to the exemplary implementations and architectures described herein are also within the scope of this disclosure.

[0118] The program modules, applications, etc., disclosed herein may include one or more software components, such as software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

[0119] Software components can be coded in any of a variety of programming languages. Exemplary programming languages ​​could be low-level languages, such as assembly language, which is associated with a specific hardware architecture and / or operating system platform. Software components that include assembly language instructions may require being translated into executable machine code by an assembler before being executed by the hardware architecture and / or platform.

[0120] Another exemplary programming language could be a high-level programming language that is portable across multiple architectures. Software components that include high-level programming language instructions may need to be converted into an intermediate representation by an interpreter or compiler before execution.

[0121] Other examples of programming languages ​​include, but are not limited to, macro languages, shell or command languages, job control languages, scripting languages, database query or search languages, or report writing languages. In one or more example embodiments, a software component that includes instructions employing one of the above examples of a programming language can be executed directly by an operating system or other software components without first being converted into another form.

[0122] Software components can be stored as files or other data storage structures. Software components of similar type or with related functions can be stored together, for example, in a specific directory, folder, or library. Software components can be static (e.g., pre-built or fixed) or dynamic (e.g., created or modified at runtime).

[0123] Software components can be invoked or called by other software components through any of a variety of mechanisms. The invoked or called software components can include other custom-developed application software, operating system functions (e.g., device drivers, data storage (e.g., file management) routines, other common routines and services, etc.), or third-party software components (e.g., middleware, encryption or other security software, database management software, file transfer or other network communication software, mathematical or statistical software, image processing software, and format conversion software).

[0124] Software components associated with a specific solution or system may reside on and execute on a single platform, or they may be distributed across multiple platforms. Multiple platforms may be associated with more than one hardware vendor, underlying chip technology, or operating system. Furthermore, software components associated with a specific solution or system may initially be written in one or more programming languages, but may invoke software components written in another programming language.

[0125] Computer-executable program instructions may be loaded onto a special-purpose computer or other specific machine, processor, or other programmable data processing apparatus to create a particular machine, such that execution of the instructions on the computer, processor, or other programmable data processing apparatus causes one or more functions or operations specified in any applicable flowchart to be performed. These computer program instructions may also be stored in a computer-readable storage medium (CRSM) that, when executed, directs the computer or other programmable data processing apparatus to function in a particular manner, causing the instructions stored in the computer-readable storage medium to produce an article of writing comprising instruction means including implementing one or more functions or operations specified in any flowchart. Computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus, thereby producing a computer-implemented process.

[0126] Additional types of CRSMs that may appear in any device described herein may include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, optical disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store information and is accessible. Any combination of the foregoing is also included within the scope of CRSMs. Alternatively, computer-readable communication media (CRCM) may include computer-readable instructions, program modules, or other data transmitted within data signals, such as carrier waves or other transmissions. However, as used herein, CRSMs do not include CRCMs.

[0127] Although embodiments have been described using language specific to structural features and / or methodological actions, it should be understood that this disclosure is not necessarily limited to the specific features or actions described. Rather, specific features and actions are disclosed as exemplary forms for implementing embodiments. Conditional language, such as “can,” “may,” “can,” or “will,” unless otherwise specifically stated or understood in the context in which they are used, is generally intended to convey that certain embodiments may include certain features, elements, and / or steps, while other embodiments do not. Therefore, such conditional language is not generally intended to imply that features, elements, and / or steps are necessary in any way for one or more embodiments, or that one or more embodiments necessarily include logic for determining whether such features, elements, and / or steps are included or to be performed in any particular embodiment, with or without user input or prompting.

Claims

1. A method comprising: Heart rate is determined by at least one processor in the device; The exercise threshold is determined by the at least one processor and based on the heart rate; Motion data is determined by the at least one processor; The motion data is compared with the motion threshold by at least one processor; The activity intensity level is determined by the at least one processor and based on a comparison of the motion data with the motion threshold; The activity score is determined by the at least one processor based on the activity intensity level; and The at least one processor causes data indicating the activity score to be presented; The method further includes: Determine the heart rate threshold; and Compare the heart rate with the heart rate threshold; The determination of the exercise threshold is based on a comparison between the heart rate and the heart rate threshold; and The method further includes: The change in threshold heart rate is determined based on a comparison between the exercise data and the exercise threshold; Identify heart rate changes associated with a time period; and Compare the heart rate change with the threshold heart rate change; The determination of the activity intensity level is further based on a comparison of the heart rate change with the threshold heart rate change.

2. The method of claim 1, further comprising: Compare the activity score with the score threshold; and It is determined that the activity score exceeds the score threshold. The data further indicates that the activity score exceeds the score threshold.

3. The method of claim 2, wherein the fractional threshold is a first fractional threshold, and the method further comprises determining a second fractional threshold that is larger than the first fractional threshold.

4. The method of claim 1, further comprising: The activity score is compared with a score threshold; The activity score is determined to be less than the score threshold; and Determine the difference between the activity score and the score threshold. The data further indicates that the activity score is less than the score threshold by a certain difference.

5. The method of claim 4, wherein the fractional threshold is a first fractional threshold, and the method further comprises determining a second fractional threshold that is smaller than the first fractional threshold by an amount associated with the difference.

6. The method of claim 1, wherein the heart rate and the exercise data are associated with a user, the method further comprising: Determine the second heart rate associated with the user; Determine the second motion data associated with the user; and A second activity intensity level is determined, associated with the second heart rate and the exercise data, wherein the second activity intensity level is different from the activity intensity level. The activity score is further determined based on the second activity intensity level.

7. The method of claim 6, further comprising determining the sum of a first score associated with the activity intensity level and a second score associated with the second activity intensity level. The activity score is further determined based on the sum.

8. The method of claim 1, wherein the heart rate is a first heart rate associated with the user, the method further comprising: Determine the second heart rate associated with the user; Determine the second motion data associated with the user; and Determine a second activity intensity level associated with the second heart rate and the exercise data, wherein the second activity intensity level indicates that the user was sitting during the time period associated with the second heart rate; and The negative activity score is determined based on the second activity intensity level. The activity score is further determined based on the negative activity score.

9. The method of claim 1, wherein the activity score is a first activity score associated with a first time period, wherein the heart rate is associated with a user, and wherein determining the heart rate threshold is based on at least one of data associated with the user or environmental data, wherein the data associated with the user includes at least one of the user's age or a second activity score associated with a second time period preceding the first time period.

10. The method of claim 1, further comprising: Receive user input, which includes an activity type and a duration associated with the activity; and The second activity intensity level is determined based on the user input. The activity score is further determined based on the second activity intensity level.

11. The method of claim 1, wherein the activity score is a first activity score associated with a first time period, the method further comprising: Determine the second activity score associated with the second time period preceding the first time period. The determination of the first activity score is further based on the second activity score.

12. The method of claim 1, wherein the heart rate and the motion data are associated with a user, a first time period, and a first heart rate change, and wherein the motion data is first motion data, the method further comprising: Determine the second heart rate associated with the user and the second time period; Determine the second motion data associated with the user and the second time period; The threshold heart rate change for the second time period is determined based on the second exercise data; Determine the second heart rate variation associated with the second heart rate and the second time period; and Based on a comparison of the second heart rate change and the threshold heart rate change, a second activity intensity level is determined that is associated with the second heart rate and the second exercise data. This second activity intensity level differs from the stated activity intensity level, wherein: The activity score is further determined based on the second activity intensity level, and At least one of the following: The first heart rate is different from the second heart rate. The first motion data is different from the second motion data, or The first heart rate change is different from the second heart rate change.

13. An apparatus comprising a memory coupled to at least one processor, the at least one processor being configured to: Determine the user's associated heart rate; Determine the exercise threshold for a time period based on the heart rate; Determine the device data associated with the second device and the user; Compare the device data with the motion threshold used for the time period; Determine the heart rate changes associated with the heart rate and the time period; Based on the heart rate changes, determine the activity intensity level associated with the heart rate and the device data; The activity score is determined based on the activity intensity level. and Data indicating the activity score is sent for presentation on the second device; The at least one processor is further configured to: Determine the heart rate threshold; and Compare the heart rate with the heart rate threshold; The determination of the exercise threshold is based on a comparison between the heart rate and the heart rate threshold; and The at least one processor is further configured to: Threshold heart rate changes are determined by comparing exercise data with the exercise threshold; Identify heart rate changes associated with a time period; and The heart rate change is compared with the threshold heart rate change. The determination of the activity intensity level is further based on a comparison of the heart rate change with the threshold heart rate change.

14. The device of claim 13, wherein the at least one processor is further configured to: Compare the activity score to a score threshold; and It is determined that the activity score exceeds the score threshold. The data also indicates that the activity score exceeds the score threshold.

15. The device of claim 14, wherein the fractional threshold is a first fractional threshold, and wherein the at least one processor is further configured to determine a second fractional threshold greater than the first fractional threshold.

16. The device of claim 13, wherein the at least one processor is further configured to: The activity score is compared with a score threshold; Determine that the activity score is less than the score threshold; and Determine the difference between the activity score and the score threshold. The data further indicates that the activity score is less than the score threshold by a certain difference.

17. The device of claim 16, wherein the fractional threshold is a first fractional threshold, wherein the at least one processor is further configured to determine a second fractional threshold, the second fractional threshold being smaller than the first fractional threshold by an amount associated with the difference.

18. The device of claim 13, wherein the heart rate and the motion data are associated with a user, and wherein the at least one processor is further configured to: Determine the second heart rate associated with the user; Determine the second motion data associated with the user; and A second activity intensity level is determined, associated with the second heart rate and the exercise data, wherein the second activity intensity level is different from the activity intensity level. The activity score is further determined based on the second activity intensity level.

19. The device of claim 18, wherein the at least one processor is further configured to: Determine the sum of the first score associated with the activity intensity level and the second score associated with the second activity intensity level. The activity score is further determined based on the sum.

20. The device of claim 13, wherein the heart rate is a first heart rate associated with a user, and wherein the at least one processor is further configured to: Determine the second heart rate associated with the user; Determine the second motion data associated with the user; and Determine a second activity intensity level associated with the second heart rate and the exercise data, wherein the second activity intensity level indicates that the user was sitting during the time period associated with the second heart rate; and The negative activity score is determined based on the second activity intensity level. The activity score is further determined based on the negative activity score.

21. The device of claim 13, wherein the activity score is a first activity score associated with a first time period, wherein the heart rate is associated with a user, and wherein determining the heart rate threshold is based on at least one of data associated with the user or environmental data, wherein the data associated with the user includes at least one of the user's age or a second activity score associated with a second time period preceding the first time period.

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