WO2017194770A1 - Systèmes et procédés d'avertissement de nausées induites par l'exercice - Google Patents

Systèmes et procédés d'avertissement de nausées induites par l'exercice Download PDF

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WO2017194770A1
WO2017194770A1 PCT/EP2017/061527 EP2017061527W WO2017194770A1 WO 2017194770 A1 WO2017194770 A1 WO 2017194770A1 EP 2017061527 W EP2017061527 W EP 2017061527W WO 2017194770 A1 WO2017194770 A1 WO 2017194770A1
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user
historical data
sensors
physical activity
signals
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PCT/EP2017/061527
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English (en)
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Marc Andre De Samber
Ronaldus Maria Aarts
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Koninklijke Philips N.V.
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1112—Global tracking of patients, e.g. by using GPS
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4884—Other medical applications inducing physiological or psychological stress, e.g. applications for stress testing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/10—Athletes

Definitions

  • Various embodiments described herein are directed generally to health care. More particularly, but not exclusively, various methods and apparatus disclosed herein relate to warning systems and methods for exercise-induced nausea.
  • Exercise-induced nausea is a feeling of sickness that may occur during or after strenuous exercise. It may include a variety of symptoms, such as cold sweats, excessive salivation, headache, dizziness, flushed skin, loss of appetite, retching/gagging, vomiting, muscle tremors, diarrhea, frequent swallowing, licking lips, etc.
  • Exercise-induced nausea may be caused by a variety of factors, such as dehydration, over-hydration (hyponatremia), and/or over exertion. In some cases, blood flow taken away from the stomach can cause nausea. As populations adopt so-called "high performance living" lifestyles in response to recent trends in obesity due to sedentary lifestyles, exercise-induced nausea has become more common.
  • one or more mobile computing devices carried or worn by a user may include one or more sensors to detect various physical and other attributes of a user while the user exercises or otherwise engages in strenuous physical activity that could potentially lead to exercise-induced nausea.
  • Data indicative of the signals from the one or more sensors may be compared to historical data associated with, for instance, past instances of exercise-induced nausea experienced by the user or by a population of users.
  • a determination may be made, e.g., at the mobile device, that the user is likely to experience exercise-induced nausea unless the user somehow changes her behavior, e.g., by taking a remedial action such as resting, slowing down, etc.
  • the user may be notified of the determination so that the user can take appropriate responsive action (or ignore the notification).
  • a computer-implemented method may include: receiving, by one or more processors, one or more signals generated by one or more sensors of one or more mobile computing devices carried by a user engaging in a physical activity, wherein the one or more signals are indicative of one or more health parameters of the user; comparing, by the one or more processors, the one or more signals with historical data associated with engagement by one or more users in one or more physical activities; determining, by the one or more processors based on the comparing, that the user likely will experience nausea if the user continues engaging in the physical activity without taking one or more remedial actions; and raising, by the one or more processors, an alert to be provided to the user to take one or more remedial actions in response to the determining.
  • the one or more sensors may include an accelerometer, a heart rate monitor, a position coordinate sensor, and/ or a sweat sensor.
  • the comparing may include providing, as inputs to a machine learning model, data indicative of the one or more signals from the one or more sensors.
  • the determining may be based on a classification provided by the machine learning model.
  • the determining may include: calculating, based on the comparing, a likelihood that the user will experience nausea if the user continues engaging in the physical activity without taking one or more remedial actions; and determining that the likelihood satisfies a predetermined threshold.
  • the historical data may include personal historical data associated with prior performance of the physical activity by the user. In various embodiments, the historical data may include aggregate historical data associated with prior performance of the physical activity by a plurality of users. In various embodiments, the historical data may include personal historical data associated with prior performance by the user of another physical activity different than the physical activity. In various embodiments, the historical data may include aggregate historical data associated with prior performance by a plurality of users of one or more other physical activities different than the physical activity.
  • the method may include: receiving, by the one or more processors, feedback from the user, the feedback indicative of whether the user accepted or rejected the alert; and updating, by the one or more processors, the historical data based on the feedback and the one or more signals.
  • the method may include: detecting, by the one or more processors based on one or more subsequent signals from the one or more sensors, that the user took remedial action; and updating, by the one or more processors, the historical data based on the detecting.
  • the method may include: receiving, by the one or more processors, unsolicited input from the user, the unsolicited input indicative of exercise nausea the user is experiencing or expects to experience; in response to the unsolicited input, obtaining, by the one or more processors, from the one or more sensors, one or more additional signals indicative of one or more health parameters of the user; and updating, by the one or more processors, the historical data based on the unsolicited input and the one or more additional signals.
  • FIG. 1 may depict a non-transitory computer readable storage medium storing instructions executable by a processor to perform a method such as one or more of the methods described above.
  • FIG. 1 may depict a system such as a wearable computing device that includes memory and one or more processors operable to execute instructions, stored in the memory, to implement a method such as one or more of the methods described above.
  • a wearable computing device may include: one or more sensors configured to measure one or more health parameters of a user wearing the wearable computing device; one or more input components; and logic operably coupled with the one or more sensors and the one or more input components.
  • the logic may be configured to: receive, via the one or more input components, unsolicited input from the user while the user engages in a physical activity, the unsolicited input indicative of exercise nausea the user is experiencing or expects to experience; in response to the unsolicited input, obtain one or more signals generated by the one or more sensors, wherein the one or more signals are indicative of the one or more health parameters of the user when the user provided the unsolicited input; and update historical data associated with engagement by one or more users in one or more physical activities based on the unsolicited input and the one or more signals generated by the one or more sensors.
  • FIG. 1 schematically illustrates components of a mobile device configured with selected aspects of the present disclosure, in accordance with various embodiments.
  • Fig. 2 depicts the Keller index of Nausea.
  • FIG. 3 depicts an example method of detecting the onset of exercise-induced nausea and providing a warning thereof, in accordance with various embodiments.
  • Fig 4 depicts an example computer system architecture.
  • Exercise-induced nausea is a feeling of sickness that may occur during or after strenuous exercise. It may include a variety of symptoms, such as cold sweats, excessive salivation, headache, dizziness, flushed skin, loss of appetite, retching/gagging, vomiting, muscle tremors, diarrhea, frequent swallowing, licking lips, etc.
  • Exercise-induced nausea may be caused by a variety of factors, such as dehydration, over-hydration, and/ or over exertion. As populations become more active, exercise-induced nausea has become more common. Accordingly, Applicants have recognized and appreciated that it would be beneficial to proactively warn users when they are likely to experience exercise-induced nausea should they not alter their immediate behavior.
  • various embodiments and implementations of the present disclosure are directed to determining, based on a variety of signals, that a user is likely to experience exercise-induced nausea if he or she does not change her immediate behavior, and providing the user with a warning.
  • a mobile device 100 configured with selected aspects of the present disclosure is shown in the form of a smart watch. However, this is not meant to be limiting. Mobile device 100 may come in other form factors, such as a smart phone, an activity tracker meant to be carried in a pocket to secured to the skin, a smart hat or headband, smart glasses, smart apparel (e.g., clothing with sewn-in circuitry that performs selected aspects of the present disclosure), and so forth. Mobile device 100 may include a variety of standard computing components, such as logic 102, memory 104, one or more input components 106, one or more output components 108, one or more communications modules 110 j _ M , a power supply (not depicted), and so forth. In various embodiments, one or more of these components may be operably coupled via one or more buses 112. Only selected buses 112 are labeled in Fig. 1 for the sakes of clarity and brevity.
  • Logic 102 may come in various forms.
  • logic 102 may take the form of one or more processors, and memory 104 may store instructions that may be executed by the one or more processors to perform various techniques described herein.
  • logic 102 may take the form of an application-specific integrated circuit ("ASIC") or a field- programmable gate array (“FPGA").
  • ASIC application-specific integrated circuit
  • FPGA field- programmable gate array
  • Memory 104 may come in various forms as well, including but not limited to volatile memory such as high-speed random access memory or non-volatile memory such as magnetic disk storage devices, optical storage devices, and flash memory.
  • One or more input components 106 may take various forms to receive input using a variety of modalities.
  • mobile device 100 may include a touch screen display that can receive input. Additionally or alternatively, mobile device 100 may include various "soft" keys, buttons, knobs, and so forth.
  • one or more sensors 114 may additionally or alternatively be used as input components 106. For example, an accelerometer may be used to detect a gesture made by a user while holding mobile device 100.
  • Input components 106 may also include a microphone to receive voice input, a camera to receive visual input, and so forth.
  • One or more output components 108 may take various forms as well. These may include one or more speakers, displays (e.g., touch screen or otherwise), haptic feedback devices (e.g., to provide vibration), and so forth.
  • Communications modules 110 j _ M may be standalone devices or components of devices that facilitate communication, e.g., sending and receiving of commands, triggers, notifications, prompts, acknowledgments, information, messages, forms, and various types of data such as video, text, and audio between, for example, mobile device 100 and one or more other computing devices 116 j _3 operated or otherwise associated with a user 118 over one or more networks 111.
  • communications modules 110 j _ M may include any transmitter or receiver used for Wi-Fi, Bluetooth, infrared, NFC, radio frequency, cellular communication, visible light communication, Li-Fi, WiMAX, ZigBee, fiber optic and other forms of wireless communication devices.
  • the communications modules 110 j _ M may be physical channels such as USB cables. Ethernet cables, or other wired forms of communication.
  • One or more networks 111 may include one or more wireless or wired personal area networks ("PANs”), local area networks (“LANs), and/or wide area networks (“WANs”) such as the Internet.
  • PANs personal area networks
  • LANs local area networks
  • WANs wide area networks
  • mobile device 100 may have two or more different types of communications modules, such as one for cellular communication, and another for Bluetooth, and perhaps even another for Wi-Fi.
  • Mobile device 100 may include various types of sensors 114. Each sensor may be configured to sense, detect, and/ or measure some physical characteristic of the user 118 and/ or of a context of the user (e.g., the user's location). Each sensor 114 may then provide, e.g., to logic 102, a signal indicative of the sensed/detected/measured physical characteristic. Sensors 114 may include but are not limited to accelerometers, thermometers, pedometers, position coordinate sensors (e.g., GPS), sweat sensors, barometers, barometric altimeters, heart rate monitors, cameras (e.g., to detect pulse and/ or respiratory rate), microphones, and so forth.
  • sensors 114 may include but are not limited to accelerometers, thermometers, pedometers, position coordinate sensors (e.g., GPS), sweat sensors, barometers, barometric altimeters, heart rate monitors, cameras (e.g., to detect pulse and/ or respiratory rate), microphones, and so forth.
  • index 120 may include, for instance, historical data pertaining to user 118 and/or a population of users of which user 118 is a member, such as friends on a social network, contacts, fellow gym members, teammates, co-workers, and so forth. Historical data may be stored elsewhere as well, such as in a historical data index 122 maintained on one or more computing systems (e.g., a server farm) located remotely from mobile device 100 and/or other computing devices 116.
  • computing systems e.g., a server farm
  • historical data personal to user 118 may begin as a baseline established by user 118 in various ways.
  • user 118 may provide mobile device 100 with some sort of empirically and/ or user-defined maximum exertion threshold. Additionally or alternatively, user 118 may calibrate mobile device 100 by exercising. For example, when the user begins to feel exercise-induced nausea set in while exercising, user 118 may, with or without solicitation of input from mobile device 100, operate one or more input components 106 of mobile device 100 to indicate that user 118 is beginning to experience and/or is experiencing exercised- induced nausea.
  • one or more objective symptoms of exercise-induced nausea may be detected from user 118 by one or more sensors 114. Either way, mobile device 100 may poll the various sensors 114 and/or other "contextual" data points (e.g., time of day, user's age, user's sleep diary, etc.) to generate a "snapshot" of a state of user 118 that may be used as an exercise-induced nausea baseline moving forward.
  • the user may be provided an opportunity to accept or reject the baseline snapshot. For example, a user may swipe away a baseline notification to reject it. If the user accepts the baseline notification (or in some cases takes no responsive action), then the snapshot may be established as baseline historical data for the user.
  • mobile device 100 may in the future, alone or in combination with other components depicted in Fig. 1, use the baseline and any subsequendy- added historical data to detect and warn user 118 when user 118 likely will experience exercise- induced nausea if user 118 does not alter his or her behavior.
  • logic 102 of mobile device 100 or logic of other computing devices such as 116 j _ 3 , may receive one or more signals generated by one or more sensors 114 while user 118 engages in a physical activity.
  • the one or more signals may be indicative of one or more health parameters of the user, including but not limited to heart rate, respiratory rate, steps taken, location (including change in location), one or more sweat characteristics, temperature, or any other physical characteristic of user 118 and/or of a context of user 118 that can be detected by sensors 114 ⁇ .
  • These received sensor signals may be compared with historical data pertaining to user 118 personally (e.g., the baseline described above plus any subsequendy collected data) and/ or to a plurality of users generally.
  • the historical data may be further associated with the same activity that user 118 is engaging in (e.g., running, walking, dancing, playing sports, etc.), and/ or with activities that are deemed to be similarly strenuous as the activity that user 118 is engaging in.
  • the comparison may involve providing, as inputs to a trained machine learning model (trained, for example, based on the historical data), data indicative of the one or more signals from the one or more sensors 114.
  • a snapshot of all the sensor readings may be packed into a feature vector instance that is then classified by a machine learning model as suggestive of imminent exercise-induced nausea.
  • Various machine learning classification models may be employed, including but not limited to linear/logistic regression models, neural network models, etc.
  • Various learning algorithms for training such models may be used such as, for example, batch or stochastic gradient descent or application of the normal equations..
  • a likelihood that the user will experience nausea if the user continues engaging in the physical activity without taking one or more remedial actions may be calculated, e.g., by logic 102, and then a determination may be made that the likelihood satisfies a predetermined threshold. However the determination is made, an alert may be raised in response, e.g., by mobile device 100 using output components 108 and/ or by one or more other computing devices 116, that user 1 18 should take one or more remedial actions to avoid the onset of exercise- induced nausea. In response to the alert, the user may take various remedial actions, such as slowing down, stopping, etc.
  • the system may continue to "learn" about the proclivity of user 118 to experience exercise-induced nausea by continuing to update the historical data based on user response to alerts.
  • the historical data may be updated in response to various events. For example, in some embodiments, when user 118 takes remedial action in response to alert, that may be detected (e.g., an accelerometer may detect that the user slowed down or stopped) by one or more sensors 114 of mobile device 100. The fact that user 118 took such remedial action may be used in conjunction with the sensor measurements that triggered the alert as, for instance, a positive training example provided to a machine learning model.
  • the positive training example may indicate that a classification by the machine learning model that the user was imminently going to experience exercise-induced nausea was likely accurate.
  • user 118 takes no remedial action in response to an alert, that may be used as a negative training example.
  • the device may continue to monitor the user for signs of nausea and, if they continue to increase, label the example as a positive training example (because, even though the user ignored the alert, they went on to experience nausea increase).
  • user 118 may respond to an alert by taking some action using mobile device 100 (or another computing device 116) to explicitly accept or reject the alert, such as swiping away a notification.
  • Such a response by user 118 may be used in conjunction with the sensor measurements that triggered the alert as an additional positive or negative training example.
  • these techniques may also facilitate evolution of the user's historical data over time to track the user's physical fitness.
  • one or more thresholds associated with imminent exercise-induced nausea may be raised.
  • the same or similar thresholds may be lowered.
  • the user may manually adjust thresholds as the user sees fit, e.g., to account for circumstances that may not be easily detectable by sensors 114.
  • mobile device 100 may take the form of a sports watch, with at least one sensor 114 coming in the form of a heart rate meter.
  • Other components depicted as being integral to mobile device 100 may be integral to such a sports watch, and/ or may be distributed elsewhere (e.g., on other computing devices 116) .
  • Fig. 2 depicts the Keller Index of Nausea.
  • This index includes various subjective symptoms and changes in behavior that may commonly be observed among many individuals who are experiencing exercise-induced nausea.
  • One or more of these symptoms and/ or changes in behavior may serve as an indicator that may be used to calculate in an objective manner whether a particular individual is experiencing or will likely soon experience exercise-induced nausea.
  • Some of these symptoms may be measureable or detectable using various sensors 114 of mobile device 100.
  • sensors 114 of mobile device 100 For example, several of “Alterations in affect and behaviors," such as the individual putting her hand over her mouth or stomach, may be detected by an accelerometer contained in a smart watch worn around a user's wrist.
  • One or more of the "Distress” symptoms, such as nausea facial expression could be detected by facial recognition processes using image data captured by a camera.
  • Other types of sensors could be employed to capture one or more of the "Physiological
  • image data captured by a camera, or a signal from a heart monitor may be used to detect a respiratory rate.
  • Audio data from a microphone e.g., on a wrist-worn device or disposed in the ear of the patient, for example, as part of a headphone
  • Cold sweat may be detected by various moisture, sweat, and/or temperature sensors.
  • Cold skin may be detected, for instance, using a digital temperature sensor (e.g., disposed in a wrist-worn device or otherwise in contact with the user).
  • movements associated with physiological alterations such as vomiting or retching may be detected by comparing a signal from an accelerometer to movement profiles known to be associated with vomiting or retching or generally with nausea (e.g. holding a hand over the mouth or stomach).
  • as separate model may be trained to recognize these gestures from accelerometer, positional, and other available sensor data and/ or to recognize which such gestures correspond to nausea for that particular user.
  • another trained model may be used to extract features from available sensor data that will be used in conjunction with the nausea prediction model (e.g., for construction of the training set and for application of the model to a feature vector).
  • sensors may be deployed elsewhere on the user's body to detect other symptoms of exercise-induced nausea.
  • these sensors may communicate with a mobile device 100 over one or more personal area networks (e.g., Bluetooth).
  • one or more sensors may be deployed on or inside of a user's mouth to detect excessive salivation, frequent swallowing, tongue movements, and so forth.
  • Such sensors could include, for example, microphones, cameras, moisture sensors, etc.
  • such a sensor may be included on a mouth guard worn, for instance, by athletes playing sports.
  • alerts raised by mobile device 100 may convey various types of information.
  • the alert may prompt user 118 to take one or more remedial actions, such as “slow down,” “take x minute break,” “stretch,” “stop immediately,” and so forth.
  • the suggested remedial actions may be to each food and/or rehydrate.
  • user 118 may be prompted to hydrate and/ or eat food well ahead of imminent exercise-induced nausea.
  • logic 102 may determine that user 118 previously experienced exercise-induced nausea while engaging in a particular physical activity on an empty stomach (which may have been determined, for instance, from manual input from user 118 or from an electronic food log maintained by user 118). When user 118 begins performing the same exercise, an alert may be raised suggesting that the user eat something first to prevent the previously-experienced exercise-induced nausea.
  • an example method 300 for predicting and warning a user of imminent exercise-induced nausea is provided. While the operations of method 300 are depicted in a particular order, this is not meant to be limiting. Various operations may be reordered, added, and/ or omitted.
  • one or more sensor signals may be received, e.g., by logic 102, from one or more sensors (e.g., 114) of a mobile device (e.g., 100) carried or worn by a user (e.g., 118) engaging in a physical activity such as running, walking, swimming, playing a sport, lifting weights, etc. These sensor signals may be indicative of a variety of physical characteristics of the user that are described above.
  • one or more contextual signals may be obtained, e.g., by logic 102, from a variety of sources.
  • contextual signals may refer to data points associated with a user that are not physically sensed by a sensor, but instead are obtained from one or more information sources associated with a user. For example, a user's calendar or social network status update could reveal that the user is scheduled to go (or is) running. Or a user's GPS sensor may detect that the user is located at the gym, perhaps even on a particular exercise machine. Or, a pregnancy-tracking application on the user's phone may reveal that the user is pregnant (such a fact may increase the likelihood of exercise-induced nausea).
  • the one or more sensor and/ or contextual signals may be compared to historical data.
  • historical data may include subjective data that is personal to the user (e.g., baseline data and subsequendy learned data) as well as objective data that is observed across a plurality of users.
  • objective historical data associated with users sharing one or more attributes with a user-of-interest may be considered. For example, if the user is a thirty-two year old woman, then objective historical data associated with women in their thirties may be used.
  • sensor/ contextual signals may be compared to historical data in various ways.
  • each sensor/ contextual signal may provide one or more data points to be used as part of an input vector for a trained machine learning model.
  • one or more alerts may be raised, e.g., by mobile device 100 and/ or other computing devices, such as 116.
  • a user's smart watch or a smart phone strapped to the user's arm may vibrate to catch the user's attention.
  • a prompt may be rendered suggesting that the user take remedial action to avoid exercise-induced nausea.
  • the user's watch or phone may beep at the user to catch her attention, and may visually or audibly notify the user that exercise-induced nausea is imminent without some change in behavior.
  • a user's proclivity for exercise-induced nausea may be learned over time and/or updated to reflect changes in the user's physical fitness. This is demonstrated by blocks 312 and 314, of which neither, one, or both may be performed. These blocks are associated with determining whether the determination made at block 308 to raise the alert at block 310 was proper, and to act appropriately in the future.
  • the user may provide implicit feedback.
  • remedial action taken by the user such as slowing down, stopping, eating something, drinking water, etc.
  • an accelerometer may reveal that the user slowed down or stopped moving.
  • a heart rate monitor may reveal that the user's heart rate has lowered to acceptable levels. And so forth.
  • the user may provide explicit feedback.
  • feedback may be received from the user as to the veracity of the determination at block 308.
  • a user may dismiss (e.g., by swiping away) a touch screen notification suggesting imminent onset of exercise-induced nausea.
  • a user may simply ignore haptic feedback.
  • a user may explicitly acknowledge the alert raised at block 310 may selecting an "APPROVE" or "CORRECT DIAGNOSIS" button.
  • the historical data may be updated. For example, if user actions at block 312 and/ or 314 suggest that the decision made at block 308 was incorrect, then a feature vector containing various data points collected at blocks 302 and 304 may be provided as a negative training example to a machine learning model.
  • Fig. 4 is a block diagram of an example computer system 410.
  • Computer system 410 typically includes at least one processor 414 which communicates with a number of peripheral devices via bus subsystem 412.
  • processor will be understood to encompass various devices capable of performing the various functionalities attributed to the CDS system described herein such as, for example, microprocessors, FPGAs, ASICs, other similar devices, and combinations thereof.
  • peripheral devices may include a data retention subsystem 424, including, for example, a memory subsystem 425 and a file storage subsystem 426, user interface output devices 420, user interface input devices 422, and a network interface subsystem 416.
  • the input and output devices allow user interaction with computer system 410.
  • Network interface subsystem 416 provides an interface to outside networks and is coupled to corresponding interface devices in other computer systems.
  • User interface input devices 422 may include a keyboard, pointing devices such as a mouse, trackball, touchpad, or graphics tablet, a scanner, a touchscreen incorporated into the display, audio input devices such as voice recognition systems, microphones, and/ or other types of input devices.
  • pointing devices such as a mouse, trackball, touchpad, or graphics tablet
  • audio input devices such as voice recognition systems, microphones, and/ or other types of input devices.
  • use of the term "input device” is intended to include all possible types of devices and ways to input information into computer system 410 or onto a communication network.
  • User interface output devices 420 may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices.
  • the display subsystem may include a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or some other mechanism for creating a visible image.
  • the display subsystem may also provide non-visual display such as via audio output devices.
  • output device is intended to include all possible types of devices and ways to output information from computer system 410 to the user or to another machine or computer system.
  • Data retention system 424 stores programming and data constructs that provide the functionality of some or all of the modules described herein.
  • the data retention system 424 may include the logic to perform selected aspects of method 300, and/ or to implement one or more aspects of mobile device 100 and/or computing devices 116.
  • Memory 425 used in the storage subsystem can include a number of memories including a main random access memory (RAM) 430 for storage of instructions and data during program execution, a read only memory (ROM) 432 in which fixed instructions are stored, and other types of memories such as instruction/data caches (which may additionally or alternatively be integral with at least one processor 414).
  • a file storage subsystem 426 can provide persistent storage for program and data files, and may include a hard disk drive, a floppy disk drive along with associated removable media, a CD-ROM drive, an optical drive, or removable media cartridges.
  • non-transitory computer- readable medium will be understood to encompass both volatile memory (e.g. DRAM and SRAM) and non-volatile memory (e.g. flash memory, magnetic storage, and optical storage) but to exclude transitory signals.
  • Bus subsystem 412 provides a mechanism for letting the various components and subsystems of computer system 410 communicate with each other as intended. Although bus subsystem 412 is shown schematically as a single bus, alternative implementations of the bus subsystem may use multiple busses.
  • Computer system 410 can be of varying types including a workstation, server, computing cluster, blade server, server farm, or any other data processing system or computing device.
  • computer system 410 may be implemented within a cloud computing environment.
  • logic 102 and/or index 120 (which stores historical data) may be implemented in whole or in part on one or more virtual machines running on hardware within (or distributed among) one or more data centers. Due to the ever-changing nature of computers and networks, the description of computer system 410 depicted in Fig. 4 is intended only as a specific example for purposes of illustrating some implementations. Many other configurations of computer system 410 are possible having more or fewer components than the computer system depicted in Fig. 4.
  • inventive embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed.
  • inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/ or method described herein.
  • a reference to "A and/ or B", when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
  • the phrase "at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements.
  • This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.
  • “at least one of A and B" can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

Abstract

Dans divers modes de réalisation, un ou plusieurs capteurs d'un ou de plusieurs dispositifs informatiques mobiles (100) portés par un utilisateur (118) pratiquant une activité physique peuvent générer un ou plusieurs signaux indiquant un ou plusieurs paramètres de santé de l'utilisateur. Dans certains modes de réalisation, le ou les signaux peuvent être comparés (306) à des données d'historique associées à la pratique par un ou plusieurs utilisateurs d'une ou de plusieurs activités physiques. Dans certains modes de réalisation, une détermination peut être effectuée (308) sur la base de la comparaison selon laquelle l'utilisateur pourrait ressentir de nausées s'il continue à pratiquer l'activité physique sans prendre une ou plusieurs actions correctives. En réponse, une alerte peut être émise (310) auprès de l'utilisateur pour prendre une ou plusieurs actions correctives. Dans certains modes de réalisation, les données historiques peuvent être mises à jour (316) sur la base d'une réaction de l'utilisateur à l'alerte.
PCT/EP2017/061527 2016-05-13 2017-05-12 Systèmes et procédés d'avertissement de nausées induites par l'exercice WO2017194770A1 (fr)

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