US20170372026A1 - Non-Invasive continuous and adaptive health monitoring eco-system - Google Patents

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  • H04M1/72424—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality for supporting emergency services with manual activation of emergency-service functions
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
  • Y02D30/00—Reducing energy consumption in communication networks
  • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

• a non-invasive multi-sensor eco-system tracks and monitors critical human physiological parameters, including those covered by the term “vital signs,” to detect and predict health conditions.
• the system may be operated in an adaptive mode.
• the physiological parameters are extracted from a plurality of sensors using novel algorithms.
• the parameters measured by one embodiment may include blood pressure, heart rate, oxygen saturation (SpO2), respiratory rate, blood glucose level, body temperature and physical activity measured as step count.
• the eco-system consists of multiple components wirelessly communicating with each other: (1) wearable sensors, which may include signal processing functionality as well as wireless inter-sensor communication and short-term data storage; (2) a portable computing device hosting a mobile application which enables reception of the processed sensed data, transmission of that data to a cloud platform for analysis, display of push notifications determined by the processed sensed data, reception of analysis results fed back from the cloud platform, and visualization of the processed sensed data and of the cloud analytics data; and (3) the cloud platform itself, allowing long-term data storage as well as analysis of the measurement data to obtain short and long-term health trends and future health predictions.
• the eco-system also includes a linked healthcare provider, for professional review and action as and when necessary or appropriate.
• the eco-system operates to (a) analyze the physiological parameters derived from data provided by two or more sensors, positioned at different locations over the subject's body; (b) compare them against their respective normal, critical and life-threatening bounds as defined by the clinical community; and (c) provide feedback, alerts, push notifications and/or 911 calls depending on the criticality of the results of the comparison.
• Machine learning algorithms may be employed to carry out various aspects of the analysis, at the cloud platform level.
• the eco-system would ideally be closed-loop and comprehensive, covering a spectrum of actions, from automatically collecting physiological parameters from each of a plurality of users, getting a full understanding of the parameter profile for each individual user, and recording their long-term health trends and conditions, to providing guidance toward attaining a healthier lifestyle for individual users, groups and the community as a whole.
• FIG. 1 illustrates a high level view of an eco-system according to one embodiment of the invention.
• FIG. 2 schematically illustrates the functioning of an eco-system according to one embodiment of the invention.
• FIG. 3 illustrates examples of sensors that may be worn in various embodiments of the invention.
• FIG. 4 illustrates two examples of subjects wearing sensors according to embodiments of the invention.
• FIG. 5 illustrates the computational flow of data through some embodiments of the invention.
• FIG. 6 illustrates low power connectivity between hardware elements in some embodiments of the invention.
• FIG. 1 is a high level view of an eco-system 100 of the present invention, illustrating relationships between four major elements—sensors 110 (central sensor 110 A and just one remote sensor 110 B are shown in this example for simplicity, but in other embodiments, there may be additional remote sensors), a cloud platform 130 hosting AI-based analytics, and a mobile or portable device 105 .
• Device 105 has a user interface enabling communication with the sensors, the platform, and with an entity 120 , typically comprising a healthcare entity, which may, for example, be a physician, a clinic, or an emergency care unit.
• Entity 120 may also include a user chosen sub-community of people such as family members.
• entity 120 may be considered to lie outside the eco-system, but to be in communication with it.
• a single healthcare entity 120 A is communicatively coupled to mobile device 105 and directly or indirectly to cloud platform 130 .
• FIG. 1 indicates how a system of continuous and adaptive vital data monitoring with clinical accuracy may result in a healthier lifestyle and peace of mind.
• FIG. 2 illustrates the functioning of elements of an eco-system 200 according to the present invention, showing a finer granularity level than FIG. 1 , and illustrating some of the steps performed by components of the closed-loop ecosystem.
• One element or category is a plurality of wearable sensors ( 110 in FIG. 1 ), including one central sensor and one or more auxiliary or remote sensors worn by a user. Each sensor is configured to monitor one of the user's physiological parameters. Examples of typical parameters of interest are listed in Table 1.
• Heart Rate 2. Pulse Rate 3. Heart Rate Variability 4. Cardiac Index 5. Blood Pressure 6. Blood Glucose 7. Respiratory Rate 8. Oxygen Saturation (SpO2) 9. Desaturation Index 10. Apnea Hypopnea Index 11. Body Temperature 12. Electrocardiograph Activity 13. Electro Dermal Activity
• measurements of one or more of these parameters may be initiated at step 240 , as and when desired by the user, using an interface of an application on a convenient portable device easily accessed by the subject, such as a smart-phone ( 105 in FIG. 1 ). Alternately, the parameters may be automatically measured as programmed in such an application.
• Components of device 105 include receiver 106 , transmitter 107 , processor 108 , and display screen 109 .
• the central sensor is typically worn on the wrist; typical locations for other sensors include the forehead, chest, fingertip, earlobe and leg.
• Examples of measurement technologies used include photoplethysmography (PPG), electrocardiography (ECG), 3-axis accelerometry, temperature measurement using thermistors, and electrodermal activity monitoring.
• PPG photoplethysmography
• ECG electrocardiography
• 3-axis accelerometry temperature measurement using thermistors
• temperature measurement using thermistors and electrodermal activity monitoring.
• Some of the sensors may be used primarily or solely to provide calibration signals for other sensors.
• FIG. 3 shows close up views of examples of sensors at their envisaged body locations.
• Sensor 310 A is a wrist-mounted sensor, typically the central sensor of the system.
• Sensors 310 B, 310 C, 310 D, 310 E, and 310 F are examples of sensors designed to be worn on “remote” locations such as a finger tip, earlobe, around the chest, head, or ankle respectively.
• FIG. 4 illustrates how such sensors may be worn by two subjects at different stages of life.
• the wireless communication of data between the central sensor and each remote sensor may be carried out using Bluetooth or other well-known and established wireless technologies.
• the placement of sensors 310 A-F is shown on the youthful figure on the left, while the corresponding physiological parameters that may be measured using those sensors are shown on the elderly figure on the right.
• a subset of the sensors shown may be used, with as few as one remote sensor present in addition to one central sensor.
• a single sensor may provide data indicative of more than one physiological parameter of interest.
• a photoplethysmographic (PPG) sensor which essentially monitors blood volume, but from which data indicative of SpO2, glucose, heart rate, blood pressure, and respiratory rate may be derived. Sensors may be operated to monitor the wearer's vital parameters continuously and automatically over long periods of time.
• central sensor selects at step 244 which sensor or sensors are required to perform the desired measurement or measurements. If necessary, the request is transmitted to a remote sensor at step 246 . Each designated sensor (central or remote) performs the measurements of the corresponding parameter or parameters at steps 248 and/or 250 respectively.
• any measurements performed by remote sensors are wirelessly sent to the central sensor (typically the sensor worn on the subject's wrist), with that central sensor taking responsibility for aggregating the other sensors' data as and when necessary, processing them at step 252 , as will be described in greater detail below, and transmitting the results to the user interface on the mobile device, typically a smart-phone.
• the central sensor typically the sensor worn on the subject's wrist
• the second method, dynamic calibration clearly provides some significant advantages in terms of specificity for the individual, and long term reliability.
• both static calibration—the current standard practice—and dynamic calibration may be used, to provide a desirable combination of accuracy, convenience, specificity and reliability.
• the central sensor processes (filters, calibrates, scales, etc) the raw data received to generate physiological parameter data with accuracies sufficient to render the parameter data clinically meaningful.
• Specially developed hardware-embedded algorithms may be used to achieve real-time signal processing.
• FIG. 5 schematically illustrates the computational flow of data gathered by various sensors, and processed by hardware-embedded algorithms according to some embodiments of the present invention, to yield data of clinical significance.
• the central sensor compares those processed data values to values defining ranges of interest (normal, critical and life-threatening) for each corresponding parameter, at step 254 .
• the measured and processed data may be stored for the short term in the memory of the central sensor, at step 256 .
• the central sensor may wirelessly send push notifications to the smart-phone (or similar portable device).
• notifications may be normal text, or in some embodiments, simple symbols or easily appreciated codes.
• a blue code, or a predetermined symbol such as a smiley face may be sent to indicate to the subject that a parameter is within normal bounds
• an orange code may be sent at stip 260 to indicate that the parameter is outside normal bounds but within critical bounds
• a red code may be sent at step 262 to indicate life-threatening bounds have been exceeded.
• audible alerts may be issued as well as or instead of visible ones.
• the smart-phone may automatically initiate a 911 call.
• alerts may be sent to medical professionals such as the user's personal physician, or to health centers or emergency services. In less serious cases, alerts may be sent just to the user, accompanied by recommendations on relevant corrective actions.
• One advantage of the present invention is that the data processing and transmission burdens of the entire group of sensors is carried by just the central sensor, easing the power consumption and size, weight, complexity and cost demands on the remote sensors.
• FIG. 6 schematically illustrates one embodiment in which ultra short range (0.5 m to 1 m), ultra low power (1 to 10 microwatt range) Bluetooth wireless connections are provided between central sensor 610 A and five remote sensors 610 B-F, and a slightly longer range (1.5 m) low power (100 microwatt to 300 microwatt) Bluetooth connection is provided between central sensor 610 A and mobile (in this case hand-held) device 605 .
• ultra short range 0.5 m to 1 m
• ultra low power (1 to 10 microwatt range) Bluetooth wireless connections are provided between central sensor 610 A and five remote sensors 610 B-F
• a slightly longer range (1.5 m) low power (100 microwatt to 300 microwatt) Bluetooth connection is provided between central sensor 610 A and mobile (in this case hand-held) device 605 .
• other similar low and ultra low power protocols may be used. Reduced power consumption results in longer battery lifetime and reduced device heating, so better reliability.
• the smart-phone uses the standard internet service (e.g. 4G, LTE, WiFi etc) to securely send the processed data to the cloud for long-term storage and analytics as will be described further below.
• the standard internet service e.g. 4G, LTE, WiFi etc
• the remote sensors only have to transmit data over very short distances to reach the central sensor, which then sends processed data to the smart-phone, which in turn transmits them to the cloud, and receives other data (such as trend data discussed below) back.
• the display screen on the smart-phone (or PDA or tablet) allows the subject to receive push alerts and easily visualized displays of the results of the cloud-based analytics.
• the cloud provides long term storage of the data received from the smart-phone, and carries out analysis using conventional and/or machine learning algorithms.
• the machine learning algorithms may be especially useful when applied to the stored physiological parameter data to provide information on long-term trends, and to yield personalized measurement data that are wirelessly sent back to the smart-phone.
• the machine learning algorithms may also use the received and stored data regarding one or a combination of the parameters measured to determine health conditions or clinical insights (examples of which are listed below in Table 2) relevant to the individual subject. Predictions regarding future health may be made.
• COPD Choronic Obstructive Pulmonary Disease
• CHF Congestive Heart Failure
• Cardiovascular diseases Cardiac Arrhythmia
• Atrial Fibrillation from ECG
• Ventricular Tachycardia leading to Ventricular Fibrillation
• Stress Level 6. Sleep Apnea and Hypopnea 7.
• Personalized Meal Recommendation 8.
• Bodyweight Regulation 9. Pre-diabetic/Diabetic Stages 10. Hypothermia and Fever 11. Involuntary Fall and Seizure 12. Cholesterol Level 13. Hypertension 14. Dehydration
• Specialized, in some cases unique, algorithms may be used to provide the determinations, insights, and predictions.
• Table 3 lists examples of some of the types of specialized algorithms envisaged.
• the “normal” parameter ranges relative to which the wearer's parameters are compared may be customized according to sex, race, weight, height, and/or other characteristics. Data may be analyzed over time and presented in a way that a user can monitor the progress of his/her health status for a given set of parameters.
• the processed parameter data, trend data and clinical insights data may be sent from the cloud directly or indirectly to a physician at a medical facility authorized by the subject to receive them.
• the physician may provide advice, guidance, education, and/or prescriptions to the patient (user).
• Prescriptions from the patient's doctor may then be wirelessly and securely sent via the cloud to a pharmacy pre-selected by the subject as part of his or her personal profile, the profile having been previously created by the subject at a secure website, accessed via the smart-phone or other computing device. Users can also update their profiles directly from a smart-phone.
• some or all of the processed data may be sent from the cloud directly or indirectly to family members of the user, pre-authorized to receive such data.
• Analytics performed in the cloud can also provide long-term trends for vital parameters and clinical insights to a subject. These long-term trends consist of vital parameters measured over the course of many months or event years that is displayed in a receiver like a smart-phone, a tablet, or a computer.
• the analytics carried out at the cloud may result in suggestions, transmitted back to the user via the smart-phone, for adjusting the sequence and/or frequency of measurements of particular parameters.
• the system may even request additional measurements of the same or other parameters if the previous measurements deviate from the predefined user specific range. For instance, an elevated temperature can trigger the automatic measurement of blood pressure, ECG, oxygen saturation, etc.
• the eco-system can be adaptive, responding to current measurement data in the light of past data from the same subject and/or other comparable subjects, whether to appropriately instigate future measurements, inform the subject of trends, or to add to the physician's knowledge base enabling more effective guidance and treatment.
• the alert system has different severity level visualized by different colors
• Color green means a specific vital parameter is within the normal range
• Color yellow means that a specific vital parameter has exceeded the normal bounds but within a critical range
• Color orange means that a specific vital parameter has exceeded the critical bounds but still below the life-threatening range
• Color red means a specific vital parameter has exceeded the life-threatening bounds and an emergency call (such as, the 911 call in U.S.) is automatically initiated.
• a red alert is automatically issued for any life-threatening situation.
• a central monitoring facility first tries to establish a contact with the user, and upon no response, an emergency call (such as the 911 in U.S.) is issued with a message about the location of the patient and the specific body parameter(s) in question. This will ensure the correct paramedic team with the correct equipment arrive at the scene on time and well prepared to save the life of the patient.
• the red alert is handled in an automatic way to address cases where a patient is unconscious and cannot make an emergency call (such as, 911) or even express him/herself.
• the user can also issue a red alert if a vital parameter is in a life-threatening range and the system has not yet issued a 911 call.
• user profile User can enter personal information called user profile
• the cloud-based analytics platform allows for the secured collection and long-term hosting of all personalized vital parameter data. It allows for
• Personalized subject vital sign monitoring Other embedded algorithms will study a subject body to adjust the sequence or frequency of measurements of vital parameters. This specific data is personalized to a subject's health status.
• AST Aspartate Aminotransferase
• ALT Alanine Aminotransferase
• alkaline phosphatase bilirubin
• albumin total protein
• BUN Blood Urea Nitrogen
• Amylase Amylase
• Lipase calcium
• Some specific blood parameters include alkaline phosphatase, Lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA).
• LDH Lactate dehydrogenase
• CEA carcinoembryonic antigen
• PSA prostate-specific antigen
• An individual is many ways different physically compared to every other person and also the lifestyle choices of that person over a period of time and the changes thereof reflect on all vital health parameters measured instantaneously and/or over a time period.
• the apparatus described in this patent when used by an individual, “learns” about the user's unique physique and lifestyle choices over a period of time and adjusts its pattern of measurement of vital parameters. These changes in measurement patterns over time affect the overall operating efficiency of the apparatus (such as battery power consumption and heating). These changes help the apparatus become more adaptive to an individual with the continuous use of it and “blends” into the unique patterns of life of that individual.
• the enhancement of the quality of life of any given individual is a key outcome of the individual adaptive nature of this system.
• the apparatus is capable of being set into different operating modes such as an adaptive mode (as described above), a traditional mode where every measurement occurs at certain frequency, and a continuous mode where the apparatus keeps taking measurements on a continuous basis.
• Food and nutrition are keys to status of health of an individual.
• An individually adaptive personalized health monitoring system is at the center of personalization and individualization of nutrition.
• the proposed apparatus starts to “learn” about the effects of various food items on the measured vital health parameters of an individual as soon as the individual starts to use it. Over a period of time the apparatus acquires adequate “knowledge” of impact of various food items on the individual's overall wellbeing and estimates trend of health based on the eating habit and recommendations on how to improve it.
• CAHMaaS Continuous and Adaptive Health Monitoring as a Service
• CAHMaaS® offer the possibility to continuously monitor one's vital data through the use of a wearable device that measures vital data and saves them in a secured cloud server. The collected data are then analyzed through advanced analytics to offer the users real time clinical insights, adapted to the specific conditions of each user through the use of machine learning algorithms.
• the use of CAHMaaS® requires monthly and/or yearly subscription as well as the ownership of a device providing the required measurements.
• CAHMaaS® is in the context of Internet-of-life or an embodiment of Internet-of-life.
• the service based health monitoring system is at the heart of individual adaptiveness of the device to every user's unique physique and lifestyle over a period of time.
• the individualized adaptive system would help create a tailored ecosystem for individual consumer, the ecosystem might comprise of many things like personal wellness program, customized nutrition etc, the “tailored ecosystem” as one key application as a direct result of CAHMaas.
• PPG Central Device at Wrist
• MicroProcessing Unit finite state machine, integer/FP units, data path
• HCI Host Controller Interface
• the chest apparatus collects ECG signals that will be wirelessly sent to the central apparatus for further processing and storage.
• the earlobe apparatus is used to collect data that gets wirelessly sent to the central apparatus to calibrate other vital signs for better accuracy.
• the finger tip apparatus is used to collect data that gets wirelessly sent to the central apparatus to calibrate other vital signs for better accuracy.
• the three-axis accelerometer is strapped at the bottom leg part to monitor the leg movements to process a 3-dimentional position of the user that is wirelessly communicated to the central apparatus
• the forehead apparatus is used to collect data that is wirelessly sent to the central apparatus to calibrate other vital signs for better accuracy.

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