WO2022174871A1 - System for monitoring vital data of an occupant of a vehicle - Google Patents
System for monitoring vital data of an occupant of a vehicle Download PDFInfo
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- WO2022174871A1 WO2022174871A1 PCT/DE2022/200004 DE2022200004W WO2022174871A1 WO 2022174871 A1 WO2022174871 A1 WO 2022174871A1 DE 2022200004 W DE2022200004 W DE 2022200004W WO 2022174871 A1 WO2022174871 A1 WO 2022174871A1
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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
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
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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 for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
- A61B5/024—Measuring pulse rate or heart rate
- A61B5/0245—Measuring pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/08—Sensors provided with means for identification, e.g. barcodes or memory chips
Definitions
- the invention relates to a system for monitoring vital data of an occupant of a vehicle.
- Blood pressure, heart rate or eye movement, blinking or body temperature can be considered as vital data, but also any moisture on the surface of the calm and the like.
- a large number of different types of sensors are available for the acquisition of this vital data, including health or stress or alertness data per se that can be derived accordingly.
- all vital data can also measure long-term changes in the health of the occupants and corresponding information can be read out and evaluated.
- a plurality of sensors for recording the vital data and a central controller for evaluating this vital data are increasingly being provided in vehicles.
- various types of sensors with different measuring principles and therefore sometimes deviating signal properties are already available for the evaluation of such vital data.
- Neural networks or machine learning algorithms for evaluating the vital data are increasingly being provided for the evaluation and control in order to be able to analyze the complexity of the relevant factors in these vital data more easily and to be able to derive corresponding situations from them.
- a system can be found in US 2019097362 A1, in which various sensor modules can be connected to a control unit and their signals can be analyzed by so-called artificial intelligence, i.e. neural networks or other machine learning algorithms, by corresponding functions for the sensors connected in each case getting charged.
- artificial intelligence i.e. neural networks or other machine learning algorithms
- the correspondingly loaded neural networks or machine learning algorithms are used to analyze the specific vital data with regard to the parameters sought.
- a sensor identifier that is to say an ID, is used as a means for detecting the presence of sensors and for adapting the evaluation, which identifies the sensor and uses this sensor ID to load the appropriate AI algorithm.
- the object of the invention is to further improve the potential of such systems. This is achieved by the features of claim 1. Advantageous developments can be found in the dependent claims.
- the neural networks or other machine learning algorithms already available for the analysis of the vital data are basically also able to recognize the type of connected sensors and their sensor signal properties themselves, so that a newly connected sensor does not have to be recognized via an identifier, but correspondingly prepared rough structures of the neural network first derive only the corresponding sensor type or the type of vital signal received from the sensor signal and only then the actual but known refined analysis of the vital data with regard to the vital parameters and health data to be derived.
- the various sensor signals can be linked via fusion techniques that are known per se, such as the various Kalman types. This increases the flexibility with regard to the sensors used and their use via the present controller. New types of sensors with new functions can thus be recognized and integrated.
- the proposed method already uses classifications of the neural network to recognize the sensor type and applies them to the sensor signals themselves.
- Each type of vital data sensors such as EEG, EKG, etc., has at least rough and yet specific characteristics that are independent of people and health, which are used to initially identify the sensor type.
- Machine or so-called deep learning algorithms can be applied to train the models to detect a variety of sensor types, regardless of the type of electrical connection or the individual occupant or their state of health.
- Interfering signals are preferably filtered out in order to improve detection.
- the model or network is thus trained to recognize types of vital data signals and thus their sensors.
- an ECG signal can be determined using different sensor types, e.g. resistive or capacitive.
- the classic parameters, the so-called QRS complex are still fundamentally present and are at least fundamentally present even in different people or health or stress conditions and recognizable from the signal curve.
- This pattern can be learned, for example, and can be used to distinguish between the signal curves of an EKG and other vital data sensors, such as EEG, etc.
- This also applies analogously, for example, to blood pressure signals or temperature profiles.
- the possibilities and techniques of machine learning offer good opportunities for this and so-called Long Short Term Memory Networks have been identified as a particularly efficient solution.
- Interval-based dynamic decision trees so-called time warping decision trees, RNN, CNN can be used for the analysis of time courses.
- the detection and classification of connected sensors is based on the sensor signals of the sensors themselves. This also makes it possible to change the connected sensors, even to use new types of sensors, e.g. if certain previous illnesses of the occupant are known and special parameters are therefore to be recorded. In particular, they can also be carried by the occupants and not permanently present in the vehicle
- Sensors such as modern heart rate monitors or other mobile sensors to be worn on the body for recording vital data can be integrated into the system as long as they are in the vehicle. This solution is not only suitable for the driver of a classic
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- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Heart & Thoracic Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Veterinary Medicine (AREA)
- General Health & Medical Sciences (AREA)
- Surgery (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Physiology (AREA)
- Artificial Intelligence (AREA)
- Signal Processing (AREA)
- Psychiatry (AREA)
- Cardiology (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Child & Adolescent Psychology (AREA)
- Developmental Disabilities (AREA)
- Educational Technology (AREA)
- Hospice & Palliative Care (AREA)
- Psychology (AREA)
- Social Psychology (AREA)
- Fuzzy Systems (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
Description
Beschreibung description
System zur Überwachung von Vitaldaten eines Insassen eines Fahrzeugs System for monitoring vital signs of an occupant of a vehicle
Die Erfindung betrifft ein System zur Überwachung von Vitaldaten eines Insassen eines Fahrzeugs. The invention relates to a system for monitoring vital data of an occupant of a vehicle.
Derartige Systeme sind beispielsweise aus der US2007004969A1 , Such systems are known, for example, from US2007004969A1,
WO 2015175435 A1 oder US2019038204A1 bekannt. Als Vitaldaten kommen dabei der Blutdruck, die Flerzfrequenz oder auch die Augenbewegung, das Zwinkern oder auch die Körpertemperatur in Betracht, aber auch evtl. Feuchtigkeit auf der Flautoberfläche und dergleichen. Eine Vielzahl unterschiedlicher Sensortypen stehen dabei für die Erfassung dieser Vitaldaten einschließlich eben entsprechend daraus ableitbarer Gesundheits- oder eben auch Stress- oder Aufmerksamkeits-Zustandsdaten an sich bereit. WO 2015175435 A1 or US2019038204A1. Blood pressure, heart rate or eye movement, blinking or body temperature can be considered as vital data, but also any moisture on the surface of the calm and the like. A large number of different types of sensors are available for the acquisition of this vital data, including health or stress or alertness data per se that can be derived accordingly.
Die Überwachung von Vitaldaten eines Insassen eines Fahrzeugs, insbesondere aber des Fahrers gewinnt zunehmende Bedeutung für die Sicherheit im Straßenverkehr, da immer häufiger Verkehrsunfälle aufgrund gesundheitlicher Beeinträchtigungen des Fahrers festzustellen sind. The monitoring of vital data of an occupant of a vehicle, but in particular of the driver, is becoming increasingly important for road safety, since traffic accidents due to health impairments of the driver are becoming more frequent.
Andererseits führt die erhöhte Verkehrsdichte zu zunehmenden gesundheitlichen Belastungen des Fahrers und soll durch frühzeitige Erkennung eine Verschlechterung des Gesundheitszustands erkennbar gemacht und mitgeteilt werden, um Langzeitfolgen möglichst auszuschließen oder zu minimieren. On the other hand, the increased traffic density leads to increasing health problems for the driver and a deterioration in the state of health should be made recognizable and reported through early detection in order to rule out or minimize long-term consequences as far as possible.
Zudem können gesamte Vitaldaten neben der primären Verkehrssicherung auch langfristige gesundheitliche Veränderungen der Insassen messen und entsprechende Informationen ausgelesen und ausgewertet werden. In addition, in addition to primary road safety, all vital data can also measure long-term changes in the health of the occupants and corresponding information can be read out and evaluated.
Entsprechend werden in Fahrzeugen zunehmend eine Mehrzahl von Sensoren zur Erfassung der Vitaldaten sowie eine zentrale Steuerung zur Auswertung dieser Vitaldaten vorgesehen. Für die Auswertung solcher Vitaldaten stehen, wie eingangs erwähnt, bereits verschiedene Sensortypen mit unterschiedlichen Messprinzipien und daher teils auch abweichenden Signaleigenschaften zur Verfügung. Accordingly, a plurality of sensors for recording the vital data and a central controller for evaluating this vital data are increasingly being provided in vehicles. As mentioned at the beginning, various types of sensors with different measuring principles and therefore sometimes deviating signal properties are already available for the evaluation of such vital data.
Für die Auswertung und Steuerung werden zunehmend neuronale Netzwerke oder maschinelle Lernalgorithmen zur Auswertung der Vitaldaten vorgesehen, um die Komplexität der relevanten Faktoren in diesen Vitaldaten einfacher analysieren und entsprechende Situationen daraus ableiten zu können. Neural networks or machine learning algorithms for evaluating the vital data are increasingly being provided for the evaluation and control in order to be able to analyze the complexity of the relevant factors in these vital data more easily and to be able to derive corresponding situations from them.
So ist beispielsweise aus der US 2019097362 A1 ein System zu entnehmen, bei welchem verschiedene Sensormodule an einer Steuereinheit anschließbar sind und deren Signale durch sogenannte Artifical Intelligence, also neuronale Netzwerke bzw. andere maschinelle Lernalgorithmen analysiert werden können, indem entsprechende Funktionen für die jeweils angeschlossenen Sensoren geladen werden. Die entsprechend geladenen neuronalen Netzwerke oder maschinelle Lernalgorithmen dienen der Analyse der spezifischen Vitaldaten bzgl. der gesuchten Parameter. For example, a system can be found in US 2019097362 A1, in which various sensor modules can be connected to a control unit and their signals can be analyzed by so-called artificial intelligence, i.e. neural networks or other machine learning algorithms, by corresponding functions for the sensors connected in each case getting charged. The correspondingly loaded neural networks or machine learning algorithms are used to analyze the specific vital data with regard to the parameters sought.
Als Mittel zum Erkennen des Vorhandenseins von Sensoren und zur Adaption der Auswertung sind dient eine Sensorkennung, also ID, welche den Sensor identifiziert und über diese Sensor-ID der geeignete Al-Algorithmus geladen wird. A sensor identifier, that is to say an ID, is used as a means for detecting the presence of sensors and for adapting the evaluation, which identifies the sensor and uses this sensor ID to load the appropriate AI algorithm.
Aufgabe der Erfindung ist es, dass Potential solcher Systeme weiter zu verbessern. Dies wird durch die Merkmale des Anspruchs 1 erreicht. Vorteilhafte Weiterbildungen sind den Unteransprüchen zu entnehmen. The object of the invention is to further improve the potential of such systems. This is achieved by the features of claim 1. Advantageous developments can be found in the dependent claims.
Grundgedanke ist dabei, dass die für die Analyse der Vitaldaten ja bereits an sich vorhandenen neuronalen Netzwerke bzw. anderen maschinellen Lernalgorithmen grundsätzlich auch in der Lage sind, die Art der angeschlossenen Sensoren und deren Sensorsignaleigenschaften schon grundsätzlich selbst zu erkennen, so dass ein neu angeschlossener Sensor nicht etwa über eine Kennung erkannt werden muss, sondern entsprechend vorbereitete grobe Strukturen des neuronalen Netzwerks zunächst aus dem Sensorsignal nur den entsprechenden Sensortyp bzw. die Art des empfangenen Vitalsignals ableiten und erst daraufhin die an sich aber bekannte verfeinerte Analyse der Vitaldaten hinsichtlich der abzuleitenden Vitalparameter und Gesundheitsdaten erfolgt. The basic idea is that the neural networks or other machine learning algorithms already available for the analysis of the vital data are basically also able to recognize the type of connected sensors and their sensor signal properties themselves, so that a newly connected sensor does not have to be recognized via an identifier, but correspondingly prepared rough structures of the neural network first derive only the corresponding sensor type or the type of vital signal received from the sensor signal and only then the actual but known refined analysis of the vital data with regard to the vital parameters and health data to be derived.
Dadurch wird es aber nicht nur möglich, Sensoren ohne entsprechende Kennung zu verwenden, sondern eben auch neuartige Sensoren, deren Kennung zum Zeitpunkt der Auslieferung des Fahrzeugs mit der Steuerung noch gar nicht spezifiziert waren. This not only makes it possible to use sensors without a corresponding identifier, but also to use new types of sensors whose identifier was not even specified at the time the vehicle was delivered with the controller.
So kann ein größerer Kreis von Bio-Vitalsensorsignalen leichter berücksichtigt werden. In this way, a larger circle of bio-vital sensor signals can be taken into account more easily.
Über an sich bekannte Fusionstechniken, wie bspw. die diversen Kalmantypen können die verschiedenen Sensorsignale verknüpft werden. Dies erhöht die Flexibilität hinsichtlich der verwendeten Sensoren als auch deren Nutzung über die vorliegende Steuerung. Neuartige Sensoren mit neuen Funktionen können so erkannt und integriert werden. The various sensor signals can be linked via fusion techniques that are known per se, such as the various Kalman types. This increases the flexibility with regard to the sensors used and their use via the present controller. New types of sensors with new functions can thus be recognized and integrated.
Die vorgeschlagene Methode nutzt also bereits zur Erkennung des Sensortyps Klassifikationen des neuronalen Netzwerks und wendet diese auf die Sensorsignale selbst an. Jeder Typ von Vitaldaten-Sensoren, wie EEG, EKG usw. hat personen- und gesundheitsunabhängig zumindest grobe und dennoch spezifische Merkmale, welche zur Erkennung zunächst des Sensortyps genutzt werden. Maschinelle oder sogenannte Deep Learning Algorithmen können angewendet werden, um die Modelle zur Erkennung einer Vielzahl von Sensortypen zu trainieren, und zwar unabhängig von der Art der elektrischen Verbindung oder dem jeweiligen Insassen oder dessen Gesundheitszustand. The proposed method already uses classifications of the neural network to recognize the sensor type and applies them to the sensor signals themselves. Each type of vital data sensors, such as EEG, EKG, etc., has at least rough and yet specific characteristics that are independent of people and health, which are used to initially identify the sensor type. Machine or so-called deep learning algorithms can be applied to train the models to detect a variety of sensor types, regardless of the type of electrical connection or the individual occupant or their state of health.
Störsignale werden vorzugsweise ausgefiltert, um die Erkennung zu verbessern. Das Modell oder Netzwerk wird also trainiert, um Typen von Vitaldatensignalen und damit deren Sensoren zu erkennen. So kann bspw. ein EKG Signal über unterschiedliche Sensortypen, bspw. resistiv oder kapazitiv, ermittelt werden. Jedoch sind die klassischen Parameter, der sogenannte QRS-Komplex dennoch grundsätzlich vorhanden und selbst bei unterschiedlichen Personen oder Gesundheits- oder Belastungszuständen zumindest dem Grunde nach vorhanden und aus dem Signalverlauf erkennbar. Dieses Muster kann bspw. erlernt werden und dazu dienen, Signalverläufe eines EKG von anderen Vitaldatensensoren, wie bspw. EEG usw. zu unterscheiden. Analog gilt dies bspw. auch für Blutdrucksignale oder Temperaturverläufe. Die Möglichkeiten und Techniken maschinellen Lernens bieten hierfür gute Möglichkeiten und sogenannte Long Short Term Memory Networks als besonders effiziente Lösung ermittelt worden. Intervallbasierte dynamische Entscheidungsbäume, sogenannte time warping decison trees, RNN, CNN können für die Analyse zeitlicher Verläufe verwendet werden. Die Erkennung und Klassifizierung angeschlossener Sensoren erfolgt als anhand der Sensorsignale der Sensoren selbst. Dadurch ist es auch möglich, die angeschlossenen Sensoren zu wechseln, sogar neuartige Sensoren zu verwenden, bspw. wenn bestimmte Vorerkrankungen des Insassen bekannt sind und daher besondere Parameter erfasst werden sollen. Insbesondere können auch an sich nicht im Fahrzeug permanent vorhandene, sondern vom Insassen mitgeführteInterfering signals are preferably filtered out in order to improve detection. The model or network is thus trained to recognize types of vital data signals and thus their sensors. For example, an ECG signal can be determined using different sensor types, e.g. resistive or capacitive. However, the classic parameters, the so-called QRS complex, are still fundamentally present and are at least fundamentally present even in different people or health or stress conditions and recognizable from the signal curve. This pattern can be learned, for example, and can be used to distinguish between the signal curves of an EKG and other vital data sensors, such as EEG, etc. This also applies analogously, for example, to blood pressure signals or temperature profiles. The possibilities and techniques of machine learning offer good opportunities for this and so-called Long Short Term Memory Networks have been identified as a particularly efficient solution. Interval-based dynamic decision trees, so-called time warping decision trees, RNN, CNN can be used for the analysis of time courses. The detection and classification of connected sensors is based on the sensor signals of the sensors themselves. This also makes it possible to change the connected sensors, even to use new types of sensors, e.g. if certain previous illnesses of the occupant are known and special parameters are therefore to be recorded. In particular, they can also be carried by the occupants and not permanently present in the vehicle
Sensoren, wie die modernen Pulsuhren oder andere mobile am Körper zu tragende Sensoren zur Erfassung von Vitaldaten mit in das System integriert werden, so lange diese sich im Fahrzeug befinden. Diese Lösung eignet sich dabei nicht nur für den Fahrer eines klassischenSensors such as modern heart rate monitors or other mobile sensors to be worn on the body for recording vital data can be integrated into the system as long as they are in the vehicle. This solution is not only suitable for the driver of a classic
Automobils, sondern auch für 2- oder 3-Räder, Lastkraftwagen oder andere Land-, Wasser- oder Luftfahrzeuge als auch für andere Insassen, Mitfahrer bis hin zur Überwachung der Gesundheit von Insassen von fahrerlosen Verkehrssystemen. Automobiles, but also for 2- or 3-wheelers, trucks or other land, water or air vehicles as well as for other occupants, passengers up to monitoring the health of occupants of driverless traffic systems.
Claims
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020237021423A KR20230109748A (en) | 2021-02-16 | 2022-01-18 | A system that monitors the biometric data of vehicle occupants |
| CN202280009359.8A CN116829043A (en) | 2021-02-16 | 2022-01-18 | System for monitoring vehicle occupant vital sign data |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102021201461.5 | 2021-02-16 | ||
| DE102021201461.5A DE102021201461A1 (en) | 2021-02-16 | 2021-02-16 | System for monitoring vital signs of an occupant of a vehicle |
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| Publication Number | Publication Date |
|---|---|
| WO2022174871A1 true WO2022174871A1 (en) | 2022-08-25 |
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| Application Number | Title | Priority Date | Filing Date |
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| PCT/DE2022/200004 Ceased WO2022174871A1 (en) | 2021-02-16 | 2022-01-18 | System for monitoring vital data of an occupant of a vehicle |
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| Country | Link |
|---|---|
| KR (1) | KR20230109748A (en) |
| CN (1) | CN116829043A (en) |
| DE (1) | DE102021201461A1 (en) |
| WO (1) | WO2022174871A1 (en) |
Citations (8)
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| US20070004969A1 (en) | 2005-06-29 | 2007-01-04 | Microsoft Corporation | Health monitor |
| DE102014103520A1 (en) * | 2014-03-14 | 2015-09-17 | Elmeditec GmbH | Medical data acquisition device and adapter device |
| WO2015175435A1 (en) | 2014-05-12 | 2015-11-19 | Automotive Technologiesinternational, Inc. | Driver health and fatigue monitoring system and method |
| US20180251122A1 (en) * | 2017-03-01 | 2018-09-06 | Qualcomm Incorporated | Systems and methods for operating a vehicle based on sensor data |
| US20190038204A1 (en) | 2017-08-01 | 2019-02-07 | Panasonic Intellectual Property Management Co., Lrtd. | Pupillometry and sensor fusion for monitoring and predicting a vehicle operator's condition |
| US20190097362A1 (en) | 2017-09-26 | 2019-03-28 | Xcelsis Corporation | Configurable smart object system with standard connectors for adding artificial intelligence to appliances, vehicles, and devices |
| WO2019212833A1 (en) * | 2018-04-30 | 2019-11-07 | The Board Of Trustees Of The Leland Stanford Junior University | System and method to maintain health using personal digital phenotypes |
| DE102019202523A1 (en) * | 2019-02-25 | 2020-08-27 | Robert Bosch Gmbh | Method and device for operating a control system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE10337235A1 (en) | 2003-08-13 | 2005-03-24 | Trium Analysis Online Gmbh | Patient monitoring system sensor communication procedure transmits sensor identity code before data transmission to allow plug and play central unit interface set up |
| EP2534597B1 (en) | 2010-03-15 | 2018-10-17 | Singapore Health Services Pte Ltd | Method of predicting the survivability of a patient |
| RU2692213C2 (en) * | 2013-10-22 | 2019-06-21 | Конинклейке Филипс Н.В. | Device with a sensitive element and a method of monitoring the vital activity of a subject |
| DE102014003783B4 (en) | 2014-03-15 | 2016-11-10 | Audi Ag | Safety device for a motor vehicle and associated operating method |
| CN111956196A (en) * | 2020-08-21 | 2020-11-20 | 无锡威孚高科技集团股份有限公司 | Vital sign detecting system in car |
-
2021
- 2021-02-16 DE DE102021201461.5A patent/DE102021201461A1/en active Pending
-
2022
- 2022-01-18 WO PCT/DE2022/200004 patent/WO2022174871A1/en not_active Ceased
- 2022-01-18 CN CN202280009359.8A patent/CN116829043A/en active Pending
- 2022-01-18 KR KR1020237021423A patent/KR20230109748A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070004969A1 (en) | 2005-06-29 | 2007-01-04 | Microsoft Corporation | Health monitor |
| DE102014103520A1 (en) * | 2014-03-14 | 2015-09-17 | Elmeditec GmbH | Medical data acquisition device and adapter device |
| WO2015175435A1 (en) | 2014-05-12 | 2015-11-19 | Automotive Technologiesinternational, Inc. | Driver health and fatigue monitoring system and method |
| US20180251122A1 (en) * | 2017-03-01 | 2018-09-06 | Qualcomm Incorporated | Systems and methods for operating a vehicle based on sensor data |
| US20190038204A1 (en) | 2017-08-01 | 2019-02-07 | Panasonic Intellectual Property Management Co., Lrtd. | Pupillometry and sensor fusion for monitoring and predicting a vehicle operator's condition |
| US20190097362A1 (en) | 2017-09-26 | 2019-03-28 | Xcelsis Corporation | Configurable smart object system with standard connectors for adding artificial intelligence to appliances, vehicles, and devices |
| WO2019212833A1 (en) * | 2018-04-30 | 2019-11-07 | The Board Of Trustees Of The Leland Stanford Junior University | System and method to maintain health using personal digital phenotypes |
| DE102019202523A1 (en) * | 2019-02-25 | 2020-08-27 | Robert Bosch Gmbh | Method and device for operating a control system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116829043A (en) | 2023-09-29 |
| KR20230109748A (en) | 2023-07-20 |
| DE102021201461A1 (en) | 2022-08-18 |
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