CN116763267A - Drunk detection method and device, wearable equipment and storage medium - Google Patents
Drunk detection method and device, wearable equipment and storage medium Download PDFInfo
- Publication number
- CN116763267A CN116763267A CN202210238843.XA CN202210238843A CN116763267A CN 116763267 A CN116763267 A CN 116763267A CN 202210238843 A CN202210238843 A CN 202210238843A CN 116763267 A CN116763267 A CN 116763267A
- Authority
- CN
- China
- Prior art keywords
- user
- data
- oxygen saturation
- blood oxygen
- gait
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 60
- 230000005021 gait Effects 0.000 claims abstract description 128
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 101
- 239000008280 blood Substances 0.000 claims abstract description 101
- 210000004369 blood Anatomy 0.000 claims abstract description 101
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 101
- 239000001301 oxygen Substances 0.000 claims abstract description 101
- 230000002159 abnormal effect Effects 0.000 claims description 73
- 238000000034 method Methods 0.000 claims description 39
- 238000004590 computer program Methods 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 10
- 230000035622 drinking Effects 0.000 claims description 8
- 239000004973 liquid crystal related substance Substances 0.000 claims 2
- 230000001360 synchronised effect Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 6
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 5
- 230000005291 magnetic effect Effects 0.000 description 5
- 238000013186 photoplethysmography Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000036541 health Effects 0.000 description 4
- 230000003068 static effect Effects 0.000 description 4
- 230000005856 abnormality Effects 0.000 description 3
- 230000035987 intoxication Effects 0.000 description 3
- 231100000566 intoxication Toxicity 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000003442 weekly effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000005294 ferromagnetic effect Effects 0.000 description 1
- 206010036067 polydipsia Diseases 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- 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
- A61B5/112—Gait analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
- A61B5/681—Wristwatch-type devices
-
- 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
- A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
-
- 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
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
-
- 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
- A61B5/7465—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network
- A61B5/747—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network in case of emergency, i.e. alerting emergency services
Abstract
The application discloses a drunk detection method, a drunk detection device, wearable equipment and a storage medium, wherein the drunk detection method comprises the following steps: determining whether a user of the wearable device is in a drunk state based on first data and second data acquired by the wearable device; wherein the first data comprises a first heart rate, a first blood oxygen saturation and/or a first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and apparatus for detecting drunk, a wearable device, and a storage medium.
Background
The drunk people can bring inconvenience and even endanger life to the drunk people caused by excessive drinking in parties or in consideration of life and work, and the accuracy of the drunk detection method in the related technology is lower.
Disclosure of Invention
In view of the above, the embodiments of the present application provide a method, a device, a wearable device and a storage medium for detecting drunk, so as to solve the technical problem of low accuracy of the method for detecting drunk in the related art.
In order to achieve the above purpose, the technical scheme of the application is realized as follows:
the embodiment of the application provides a drunk detection method, which comprises the following steps:
determining whether a user of the wearable device is in a drunk state based on first data and second data acquired by the wearable device; wherein the first data comprises a first heart rate, a first blood oxygen saturation and/or a first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period.
In the above scheme, the determining whether the user of the wearable device is in the drunk state based on the first data and the second data collected by the wearable device includes:
determining whether the heart rate of the user is abnormal based on the first heart rate and the second heart rate;
determining whether the blood oxygen saturation of the user is abnormal based on the first blood oxygen saturation and the second blood oxygen saturation;
determining whether the gait of the user is abnormal based on the first gait data and the second gait data;
and determining that the user is in a drunk state under the condition that the heart rate, the blood oxygen saturation and the gait data of the user are abnormal.
In the above scheme, the gait data at least comprises a step frequency, a step period and/or a peak-to-valley value of the step period; the method further comprises the steps of:
determining a step period based on a time interval between every two adjacent peaks in the waveform signal output by the inertia measurement unit; and/or the number of the groups of groups,
the peak-valley value of the step period is determined based on the difference between the peak value and the valley value in the waveform signal output by the inertia measurement unit in the step period.
In the above aspect, when determining whether the gait of the user is abnormal based on the first gait data and the second gait data, the method includes at least one of:
determining whether the step frequency of the user is abnormal based on a first step frequency in the first gait data and a second step frequency in the second gait data;
determining whether the user's gait cycle is abnormal based on the variance value of the second gait cycle in the second gait data and the first threshold; wherein the first threshold is determined based on a variance value of a first gait cycle in the first gait data;
determining whether gait symmetry of the user is abnormal based on a peak-to-valley value of a second step period and a second threshold value in second gait data; wherein the second threshold is determined based on a peak-to-valley value of the first gait cycle in the first gait data.
In the above aspect, the determining whether the heart rate of the user is abnormal based on the first heart rate and the second heart rate includes:
determining a first mean value and a first variance value based on a first heart rate acquired when the user is in an intoxicated state;
determining a second mean value and a second variance value based on all second heart rates acquired in a set detection period;
and determining that the heart rate of the user is abnormal under the condition that the second average value is larger than the first average value and the second variance value is larger than the first variance value.
In the above aspect, the determining whether the blood oxygen saturation of the user is abnormal based on the first blood oxygen saturation and the second blood oxygen saturation includes:
determining a product of the first blood oxygen saturation and a set percentage as a third threshold;
determining that the blood oxygen saturation of the user is abnormal if the first number of second blood oxygen saturation is less than the third threshold; wherein the first number is greater than half of the total number of second blood oxygen saturation levels acquired in a set detection period;
and determining that the blood oxygen saturation of the user is normal under the condition that the first number of second blood oxygen saturation is greater than or equal to the third threshold.
In the above scheme, the method further comprises one of the following steps:
outputting prompt information representing reduced drinking when the drunk frequency is greater than the set times;
transmitting second data corresponding to the drunk state to a first terminal under the condition that the user is in the drunk state, wherein the first terminal characterizes a terminal which is in pairing connection with the wearable equipment; the second data are used for the first terminal to determine the drunk frequency, and the prompt information is output under the condition that the determined drunk frequency is larger than the set times.
The embodiment of the application also provides a drunk detection device, which comprises:
a determining unit, configured to determine, based on first data and second data acquired by a wearable device, whether a user of the wearable device is in a drunk state; wherein the first data comprises a first heart rate, a first blood oxygen saturation and/or a first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period.
The embodiment of the application also provides wearable equipment, which comprises: a processor and a memory for storing a computer program executable on the processor, wherein the processor is adapted to perform the steps of the above-described drunken detection method when the computer program is executed.
An embodiment of the present application also provides a computer-readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the above-mentioned drunkenness detection method.
According to the embodiment of the application, the wearable device determines whether the user is in the drunk state or not based on the first data of the user in the non-drunk state and the second data acquired in the set detection period. From this, wearable equipment need not rely on alcohol sensor can carry out the drunk and detect to, wearable equipment is when carrying out drunk and detects, has taken into account heart rate, blood oxygen saturation and/or gait data comprehensively, for the scheme of drunk detection through single index, this scheme can improve the degree of accuracy of drunk testing result.
Drawings
Fig. 1 is a schematic diagram of an implementation flow of a method for detecting drunk according to an embodiment of the present application;
fig. 2 is a schematic diagram of a waveform signal output by an IMU according to an embodiment of the application;
fig. 3 is a schematic flow chart of an implementation of a method for detecting drunk according to another embodiment of the present application;
fig. 4 is a schematic implementation flow chart of the drunk detection method provided by the application embodiment of the application;
FIG. 5 is a schematic diagram of a method for detecting drunk according to an embodiment of the present application;
Fig. 6 is a schematic structural diagram of an drunk detecting device according to an embodiment of the present application;
fig. 7 is a schematic diagram of a hardware composition structure of a wearable device according to an embodiment of the present application.
Detailed Description
In the related art, drunk detection is carried out through an alcohol sensor, but the alcohol sensor cannot be installed in terminal equipment with smaller sizes such as a wearable device, and when drunk detection is carried out through the wearable device, the detection result is inaccurate.
Based on the first data and the second data acquired by the wearable equipment, the embodiment of the application provides an alcohol detection method, and whether a user of the wearable equipment is in a drunk state or not is determined; wherein the first data comprises a first heart rate, a first blood oxygen saturation and/or a first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period. Therefore, when the drunk detection is carried out through the wearable equipment, the accuracy of the detection result can be improved.
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Fig. 1 is a schematic diagram of an implementation flow of a method for detecting drunk according to an embodiment of the present application, where an execution subject of the flow is wearable devices such as a smart watch and a smart bracelet. As shown in fig. 1, the drunk detection method includes:
step 101: determining whether a user of the wearable device is in a drunk state based on first data and second data acquired by the wearable device; wherein the first data comprises a first heart rate, a first blood oxygen saturation and/or a first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period.
Here, the user of the wearable device triggers the wearable device to collect the first data in the case of being in an intoxicated state. The wearable device collects first data through a sensor in the wearable device and stores the first data to a database. The first data at least comprises a first heart rate, and further comprises first blood oxygen saturation and/or first gait data. It should be noted that the wearable device may continuously collect the first data and update the first data in the database. The first heart rate, the first blood oxygen saturation, and the number of first gait data are plural. In practical application, the wearable device collects heart rate of a user by adopting a photoplethysmography (PPG) green light sensor; collecting blood oxygen saturation of a user by adopting a PPG red sensor and an infrared sensor; an inertial measurement unit (IMU, inertial Measurement Unit) is used to collect gait data of the user. IMUs, also known as inertial sensors, include acceleration sensors and/or gyroscopic sensors.
And the wearable equipment acquires second data according to a set detection period under the condition that the first data are acquired. The second data includes at least a second heart rate, further includes second blood oxygen saturation and/or second gait data. Or the wearable device acquires the second data according to the set detection period under the condition that the wearable device acquires the first data and detects that the drunk detection function is currently activated. The set detection period may be 5 minutes, 10 minutes, 15 minutes or 30 minutes, and of course, the set detection period may be set according to actual situations. The number of second heart rate, second blood oxygen saturation and second gait data is a plurality.
Under the condition that the wearable device collects the first data and the second data, the first data and the second data with the same type are compared to obtain a comparison result, and whether the user of the wearable device is in a drunk state is determined according to the comparison result. For example, the first heart rate is compared to the second heart rate, the first blood oxygen saturation is compared to the second blood oxygen saturation, and the first gait data is compared to the second gait data.
For example, in the case that the deviation value between the first data and the second data of the same type is within a set error range, the data of the type representing the user is normal; and under the condition that the deviation value between the first data and the second data of the same type is not in the set error range, characterizing the abnormal data of the user. Under the condition that each type of data is abnormal, determining that the user is in a drunk state; in the event that any type of data is normal, it is determined that the user is in an intoxicated state. Of course, in some embodiments, the wearable device may also compare the average of the first data of the same type with the average of the second data, or compare the variance value of the first data of the same type with the variance value of the second data, so as to determine whether the user of the wearable device is in a drunk state according to the comparison result.
It should be noted that, in the case that it is determined that the user is in the drunk state, the wearable device may output the prompt information in a text or voice mode, so as to ask the user if assistance is needed; and under the condition that information representing that the user needs assistance is received, the wearable device dials a set number or sends set help seeking information and current position information to the set emergency contact. Thus, emergency assistance may be provided to drunk users. The set number may be a phone number of the emergency contact set in the address book, or may be an emergency phone, for example 120.
In some embodiments, the wearable device may also determine whether the user is in a fall state through data collected by the IMU and barometer. And when the user is in a drunk state and is in a falling state, sending the set help seeking information and the current position information to the set emergency contact.
To more accurately determine whether the user's gait is abnormal, thereby further improving the accuracy of the drunk detection result, in some embodiments, the gait data includes at least a step frequency, a step cycle and/or a peak-to-valley value of the step cycle; the method further comprises the steps of:
Determining a step period based on a time interval between every two adjacent peaks in the waveform signal output by the inertia measurement unit; and/or the number of the groups of groups,
the peak-valley value of the step period is determined based on the difference between the peak value and the valley value in the waveform signal output by the inertia measurement unit in the step period.
Here, the wearable device acquires a waveform signal output by an IMU in the wearable device, and determines a step period based on a time interval between every two adjacent peaks in the waveform signal; the peak-to-valley value of the step period is determined based on the difference between the peak value and the valley value in the waveform signal output by the IMU during the step period. Thus, a plurality of step periods, and peak-to-valley values for each step period, can be obtained. Illustratively, the waveform signals output by the IMU are shown in fig. 2.
The step frequency refers to the number of times of landing per minute of foot step when walking or running. The reciprocal of the step period is the real-time step frequency.
In order to improve accuracy of the drunk detection result and reduce false detection probability, as shown in fig. 3, in some embodiments, the determining whether the user of the wearable device is in a drunk state based on the first data and the second data acquired by the wearable device includes steps 301 to 304:
Step 301: based on the first heart rate and the second heart rate, it is determined whether the heart rate of the user is abnormal.
Here, the wearable device compares the first heart rate with the second heart rate to obtain a first comparison result, and determines whether the heart rate of the user is abnormal according to the first comparison result. For example, calculating a difference value between the first heart rate and the second heart rate, and determining that the heart rate of the user is normal when the absolute value of the difference value is within a set error range corresponding to the heart rate; and if the absolute value of the difference is not in the set error range corresponding to the heart rate, determining heart rate abnormality of the user.
Considering that the heart rate of the user in the drunk state is different from the heart rate of the user in the non-drunk state, whether the heart rate of the user is abnormal or not is determined more accurately. Thereby further improving the accuracy of the drunk detection result, in some embodiments, the determining whether the heart rate of the user is abnormal based on the first heart rate and the second heart rate comprises:
determining a first mean value and a first variance value based on a first heart rate acquired when the user is in an intoxicated state;
determining a second mean value and a second variance value based on all second heart rates acquired in a set detection period;
And determining that the heart rate of the user is abnormal under the condition that the second average value is larger than the first average value and the second variance value is larger than the first variance value.
Here, the wearable device determines a first mean value and a first variance value based on a plurality of first heart rates acquired when the user is in an intoxicated state; determining a second mean value and a second variance value based on a second heart rate acquired in a set detection period; comparing the first mean with the second mean, and comparing the first variance with the second variance; determining that the heart rate of the user is abnormal under the condition that the second average value is larger than the first average value and the second variance value is larger than the first variance value; and determining that the heart rate of the user is normal when the second average value is smaller than or equal to the first average value or the second variance value is smaller than or equal to the first variance value.
Step 302: based on the first blood oxygen saturation and the second blood oxygen saturation, it is determined whether the blood oxygen saturation of the user is abnormal.
Here, the wearable device compares the first blood oxygen saturation with the second blood oxygen saturation to obtain a second comparison result, and determines whether the blood oxygen saturation of the user is abnormal according to the second comparison result. For example, calculating a difference between the first blood oxygen saturation and the second blood oxygen saturation, and determining that the blood oxygen saturation of the user is normal when the absolute value of the difference is within a set error range corresponding to the blood oxygen saturation; and if the absolute value of the difference is not within the set error range corresponding to the blood oxygen saturation, determining that the blood oxygen saturation of the user is abnormal.
In order to more accurately determine whether the blood oxygen saturation of the user is abnormal, the accuracy of the drunk detection result is further improved. In some embodiments, the determining whether the blood oxygen saturation of the user is abnormal based on the first blood oxygen saturation and the second blood oxygen saturation comprises:
determining a product of the first blood oxygen saturation and a set percentage as a third threshold;
determining that the blood oxygen saturation of the user is abnormal if the first number of second blood oxygen saturation is less than the third threshold; wherein the first number is greater than half of the total number of second blood oxygen saturation levels acquired in a set detection period;
and determining that the blood oxygen saturation of the user is normal under the condition that the first number of second blood oxygen saturation is greater than or equal to the third threshold.
Here, the wearable device determines a product of the first blood oxygen saturation and the set percentage to obtain a third threshold; determining a first number based on a total number of all second blood oxygen saturation levels acquired in a set period; comparing each second blood oxygen saturation acquired in the set acquisition period with a third threshold value to obtain a comparison result; determining that the blood oxygen saturation of the user is abnormal under the condition that the first quantity of comparison results represent that the second blood oxygen saturation is smaller than a third threshold value; and determining that the blood oxygen saturation of the user is normal under the condition that the first quantity of comparison results indicate that the second blood oxygen saturation is greater than or equal to a third threshold value.
In practical application, the set percentage can be 95%, and of course, other values can be set according to practical situations.
Step 303: based on the first gait data and the second gait data, it is determined whether the gait of the user is abnormal.
Here, the wearable device compares the first gait data and the second gait data, obtains a third comparison result, and determines whether the gait of the user is abnormal based on the third comparison result.
On the basis that the gait data at least comprises the step frequency, the step period and/or the peak-to-valley value of the step period, whether the gait of the user is abnormal or not is determined more accurately, and therefore the accuracy of the drunk detection result is further improved. In some embodiments, in determining whether the gait of the user is abnormal based on the first gait data and the second gait data, the method includes at least one of:
determining whether the step frequency of the user is abnormal based on a first step frequency in the first gait data and a second step frequency in the second gait data;
determining whether the user's gait cycle is abnormal based on the variance value of the second gait cycle in the second gait data and the first threshold; wherein the first threshold is determined based on a variance value of a first gait cycle in the first gait data;
Determining whether gait symmetry of the user is abnormal based on a peak-to-valley value of a second step period and a second threshold value in second gait data; wherein the second threshold is determined based on a peak-to-valley value of the first gait cycle in the first gait data.
Here, the wearable device may determine whether the step frequency of the user is abnormal based on the mean or variance value of the step frequency. For example, the wearable device may determine, based on a plurality of first step frequencies in the first gait data, a mean value of the first step frequencies and a variance value of the first step frequencies; determining a mean value of the second step frequency and a variance value of the second step frequency based on all the second step frequencies in the second gait data; and determining whether the step frequency of the user is abnormal or not based on the average value of the first step frequency and the average value of the second step frequency and/or based on the variance value of the first step frequency and the variance value of the second step frequency. For example, determining that the step frequency of the user is abnormal in the case that the average value of the second step frequency is greater than the average value of the first step frequency and/or the variance value of the second step frequency is greater than the variance value of the first step frequency; and determining that the step frequency of the user is normal under the condition that the average value of the second step frequency is smaller than or equal to the average value of the first step frequency and the variance value of the second step frequency is smaller than or equal to the variance value of the first step frequency. Of course, in some embodiments, the wearable device may also set the average threshold based on the average of the first step frequency and the variance threshold based on the variance value of the first step frequency within the error tolerance range; and determining that the step frequency of the user is abnormal under the condition that the average value of the second step frequency is larger than the average value threshold value and/or the variance value of the second step frequency is larger than the variance threshold value. The average value threshold is smaller than the average value of the first step frequency, and the variance threshold is larger than the variance value of the first step frequency.
The wearable device determines a variance value of a first step period based on a plurality of first step periods in the first gait data; setting a first threshold based on a variance value of the first step period within an error allowable range; determining variance values of the second step periods in all the second step periods in the second gait data; based on the variance value of the second step period and the first threshold, it is determined whether the step period of the user is abnormal. And determining that the gait cycle of the user is abnormal under the condition that the variance value of the second gait cycle is larger than the first threshold value. And under the condition that the variance value of the second step period is smaller than or equal to the first threshold value, determining that the step period of the user is normal and the gait rule of the user. It should be noted that the first threshold is greater than or equal to the variance value of the first step period.
The wearable device determines a variance value of the peak-to-valley value of the first gait cycle based on the peak-to-valley values of the plurality of first gait cycles in the first gait data; setting a second threshold based on a variance value of a peak-to-valley value of the first step period within an error allowable range; peak-valley values of all second step periods in the second gait data, and determining variance values of the peak-valley values of the second step periods; based on the variance value of the peak-to-valley value of the second step cycle and the second threshold, it is determined whether the gait symmetry of the user is abnormal. And determining that the peak-valley value of the step cycle of the user is abnormal and the gait symmetry of the user is abnormal under the condition that the variance value of the peak-valley value of the second step cycle is larger than a second threshold value. And under the condition that the variance value of the peak-valley value of the second step period is smaller than or equal to a second threshold value, determining that the peak-valley value of the step period of the user is normal and the gait symmetry of the user is normal. The second threshold is greater than or equal to the variance value of the peak-to-valley value of the first step period.
It should be noted that, step 301, step 302 and step 303 are parallel steps and may be performed simultaneously.
Step 304: and determining that the user is in a drunk state under the condition that the heart rate, the blood oxygen saturation and the gait data of the user are abnormal.
Here, in the case that the heart rate, the blood oxygen saturation, and the gait data of the user are abnormal, it is determined that the user is in a drunk state; in the event that at least one of the heart rate, blood oxygen saturation, and gait data of the user is normal, it is determined that the user is in an intoxicated state.
It should be noted that, in some embodiments, the wearable device may determine that the user is in a drunk state when the heart rate, the blood oxygen saturation, and the gait data of the user are all abnormal, and the duration of the abnormality is longer than or equal to the set duration; and under the condition that the heart rate, the blood oxygen saturation and the gait data of the user are abnormal and the duration of the abnormality is smaller than the set duration, determining that the user is in an drunk state.
To facilitate health management for the user, in some embodiments, after step 101, the method further comprises one of:
outputting prompt information representing reduced drinking when the drunk frequency is greater than the set times;
Transmitting second data corresponding to the drunk state to a first terminal under the condition that the user is in the drunk state, wherein the first terminal characterizes a terminal which is in pairing connection with the wearable equipment; the second data are used for the first terminal to determine the drunk frequency, and the prompt information is output under the condition that the determined drunk frequency is larger than the set times.
Here, the wearable device updates a drunk record for recording a drunk time and a drunk frequency in a case that it is determined that the user is in a drunk state; determining a frequency of intoxication weekly or monthly based on the intoxication record; under the condition that the drunk frequency is greater than the set frequency, outputting prompt information representing reduced drinking so as to remind a user of less drinking.
Considering that the hardware resources of the wearable device are limited, the wearable device can also send second data corresponding to the drunk state to the first terminal when determining that the user is in the drunk state and establishing pairing connection with the first terminal, and can also send information such as drunk time, drunk place and the like to the second terminal.
The first terminal stores the received data into a set health database, for example, stores the second data, the information such as the drunk time, the drunk place and the like into the set health database; determining a frequency of intoxication weekly or monthly based on data in the set health database; under the condition that the drunk frequency is greater than the set frequency, outputting prompt information representing reduced drinking so as to remind a user of less drinking. In practical application, the set frequency may be 2 times/month.
In this embodiment, the wearable device determines whether the user is in the drunk state based on the first data in which the user is in the non-drunk state and the second data acquired in the set detection period. From this, wearable equipment need not rely on alcohol sensor can carry out the drunk and detect to, wearable equipment is when carrying out drunk and detects, has taken into account heart rate, blood oxygen saturation and/or gait data comprehensively, for the scheme of drunk detection through single index, this scheme can improve the degree of accuracy of drunk testing result.
Fig. 4 is a schematic diagram of a method for detecting drunk according to an embodiment of the present application, as shown in fig. 4, the method for detecting drunk includes:
step 401: based on the first heart rate and the second heart rate, it is determined whether the heart rate of the user is abnormal.
Here, as shown in fig. 5, the wearable device collects a first heart rate and a second heart rate of a user of the wearable device through the PPG green light sensor.
Step 401 is the same as step 301, and the implementation process of step 401 is described with reference to step 301, which is not repeated here.
Step 402: based on the first blood oxygen saturation and the second blood oxygen saturation, it is determined whether the blood oxygen saturation of the user is abnormal.
Here, the wearable device collects a first blood oxygen saturation and a second blood oxygen saturation of the user through the PPG red light sensor and the infrared sensor.
Step 402 is implemented by referring to the related description in step 302, which is not repeated here.
Step 403: based on the first gait data and the second gait data, it is determined whether the gait of the user is abnormal.
Here, the wearable device acquires first gait data and second gait data through the IMU. Step 403 is implemented by referring to the related description in step 303, which is not repeated here.
Step 404: and under the condition that the heart rate, the blood oxygen saturation and the gait data of the user are abnormal and the set duration is continuous, determining that the user is in a drunk state.
Step 405: outputting first information when the user is in a drunk state; the first information is used to ask the user if assistance is needed.
The wearable device calls the set emergency contact person or sends the set help seeking information to the set emergency contact person under the condition that the user needs help, and can also send positioning information to the set emergency contact person.
Step 406: and under the condition that the user is in a drunk state and the user is in a falling state, sending second information based on the set emergency contact person in the address book.
Here, the wearable device determines whether the user is in a falling state through data collected by the barometer and data collected by the IMU. And under the condition that the user is in a drunk state and is in a falling state, sending a call request message based on the set emergency contact in the address book, or sending set help seeking information and positioning information based on the set emergency contact in the address book.
In order to implement the drunk detection method according to the embodiment of the present application, the embodiment of the present application further provides a drunk detection device, as shown in fig. 6, including:
a determining unit 61, configured to determine, based on the first data and the second data acquired by the wearable device, whether a user of the wearable device is in a drunk state; wherein the first data comprises a first heart rate, a first blood oxygen saturation and/or a first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period.
In some embodiments, the determining unit 61 is specifically configured to:
determining whether the heart rate of the user is abnormal based on the first heart rate and the second heart rate;
Determining whether the blood oxygen saturation of the user is abnormal based on the first blood oxygen saturation and the second blood oxygen saturation;
determining whether the gait of the user is abnormal based on the first gait data and the second gait data;
and determining that the user is in a drunk state under the condition that the heart rate, the blood oxygen saturation and the gait data of the user are abnormal.
In some embodiments, the gait data includes at least a stride frequency, further includes a stride period and/or a peak-to-valley value of the stride period; the determining unit 61 is further configured to:
determining a step period based on a time interval between every two adjacent peaks in the waveform signal output by the inertia measurement unit; and/or the number of the groups of groups,
the peak-valley value of the step period is determined based on the difference between the peak value and the valley value in the waveform signal output by the inertia measurement unit in the step period.
In some embodiments, the determining unit 61 is specifically configured to at least one of:
determining whether the step frequency of the user is abnormal based on a first step frequency in the first gait data and a second step frequency in the second gait data;
determining whether the user's gait cycle is abnormal based on the variance value of the second gait cycle in the second gait data and the first threshold; wherein the first threshold is determined based on a variance value of a first gait cycle in the first gait data;
Determining whether gait symmetry of the user is abnormal based on a peak-to-valley value of a second step period and a second threshold value in second gait data; wherein the second threshold is determined based on a peak-to-valley value of the first gait cycle in the first gait data.
In some embodiments, the determining unit 61 is specifically configured to:
determining a first mean value and a first variance value based on a first heart rate acquired when the user is in an intoxicated state;
determining a second mean value and a second variance value based on all second heart rates acquired in a set detection period;
and determining that the heart rate of the user is abnormal under the condition that the second average value is larger than the first average value and the second variance value is larger than the first variance value.
In some embodiments, the determining unit 61 is specifically configured to:
determining a product of the first blood oxygen saturation and a set percentage as a third threshold;
determining that the blood oxygen saturation of the user is abnormal if the first number of second blood oxygen saturation is less than the third threshold; wherein the first number is greater than half of the total number of second blood oxygen saturation levels acquired in a set detection period;
and determining that the blood oxygen saturation of the user is normal under the condition that the first number of second blood oxygen saturation is greater than or equal to the third threshold.
In some embodiments, the drunkenness detection device further comprises one of:
the prompting unit is used for outputting prompting information representing reduction of drinking when the drunk frequency is greater than the set times;
the transmitting unit is used for transmitting second data corresponding to the drunk state to a first terminal under the condition that the user is in the drunk state, wherein the first terminal characterizes a terminal which is in pairing connection with the wearable equipment; the second data are used for the first terminal to determine the drunk frequency, and the prompt information is output under the condition that the determined drunk frequency is larger than the set times.
In practical applications, the determining unit 61 and the prompting unit may be implemented by a processor in the drunk detecting device, such as a central processing unit (CPU, central Processing Unit), a digital signal processor (DSP, digital Signal Processor), a micro control unit (MCU, microcontroller Unit) or a programmable gate array (FPGA, field-Programmable Gate Array), etc.; the transmitting unit may be implemented jointly by a processor and a communication interface in the drunkenness detection device.
It should be noted that: in the drunkenness detection device provided in the above embodiment, only the division of each program module is used for illustration, and in practical application, the above processing allocation may be performed by different program modules according to needs, i.e. the internal structure of the device is divided into different program modules to complete all or part of the above processing. In addition, the embodiments of the drunkenness detection device and the drunkenness detection method provided in the foregoing embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments, and are not repeated here.
Based on the hardware implementation of the program modules, and in order to implement the method of the embodiment of the present application, the embodiment of the present application further provides a wearable device. Fig. 7 is a schematic diagram of a hardware composition structure of a wearable device provided by an embodiment of the present application, as shown in fig. 7, the wearable device 7 includes:
a communication interface 71 capable of information interaction with other devices such as a network device and the like;
and a processor 72 connected with the communication interface 71 to realize information interaction with other devices, and is used for executing the drunk detection method provided by one or more of the technical schemes when running a computer program. And the computer program is stored on the memory 73.
Of course, in actual use, the various components in the wearable device 7 are coupled together by the bus system 74. It is understood that the bus system 74 is used to enable connected communications between these components. The bus system 74 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus system 74 in fig. 7.
The memory 73 in the embodiment of the present application is used to store various types of data to support the operation of the wearable device 7. Examples of such data include: any computer program for operating on the wearable device 7.
It will be appreciated that the memory 73 may be either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random Access Memory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronous Dynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr SDRAM, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). The memory 73 described in embodiments of the present application is intended to comprise, without being limited to, these and any other suitable types of memory.
The method disclosed in the above embodiment of the present application may be applied to the processor 72 or implemented by the processor 72. The processor 72 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in the processor 72. The processor 72 may be a general purpose processor, DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 72 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the application can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium in the memory 73 and the processor 72 reads the program in the memory 73 and in combination with its hardware performs the steps of the method described above.
Optionally, when the processor 72 executes the program, corresponding processes implemented by the wearable device in the methods according to the embodiments of the present application are implemented, and are not described herein for brevity.
In an exemplary embodiment, the present application also provides a storage medium, i.e. a computer storage medium, in particular a computer readable storage medium, for example comprising a first memory 113 storing a computer program executable by the processor 72 of the wearable device for performing the steps of the aforementioned method. The computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash Memory, magnetic surface Memory, optical disk, or CD-ROM.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
The technical schemes described in the embodiments of the present application may be arbitrarily combined without any collision.
It should be noted that, the term "and/or" in the embodiment of the present application is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for detecting drunk, comprising:
Determining whether a user of the wearable device is in a drunk state based on first data and second data acquired by the wearable device; wherein, the liquid crystal display device comprises a liquid crystal display device,
the first data comprises first heart rate, first blood oxygen saturation and/or first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period.
2. The method of claim 1, wherein the determining whether the user of the wearable device is in an drunk state based on the first data and the second data collected by the wearable device comprises:
determining whether the heart rate of the user is abnormal based on the first heart rate and the second heart rate;
determining whether the blood oxygen saturation of the user is abnormal based on the first blood oxygen saturation and the second blood oxygen saturation;
determining whether the gait of the user is abnormal based on the first gait data and the second gait data;
and determining that the user is in a drunk state under the condition that the heart rate, the blood oxygen saturation and the gait data of the user are abnormal.
3. The method according to claim 2, wherein the gait data comprises at least a step frequency, further comprising a step period and/or a peak-to-valley of the step period; the method further comprises the steps of:
Determining a step period based on a time interval between every two adjacent peaks in the waveform signal output by the inertia measurement unit; and/or the number of the groups of groups,
the peak-valley value of the step period is determined based on the difference between the peak value and the valley value in the waveform signal output by the inertia measurement unit in the step period.
4. The method of claim 3, wherein in determining whether the gait of the user is abnormal based on the first gait data and the second gait data, the method comprises at least one of:
determining whether the step frequency of the user is abnormal based on a first step frequency in the first gait data and a second step frequency in the second gait data;
determining whether the user's gait cycle is abnormal based on the variance value of the second gait cycle in the second gait data and the first threshold; wherein the first threshold is determined based on a variance value of a first gait cycle in the first gait data;
determining whether gait symmetry of the user is abnormal based on a peak-to-valley value of a second step period and a second threshold value in second gait data; wherein the second threshold is determined based on a peak-to-valley value of the first gait cycle in the first gait data.
5. The method of claim 2, wherein the determining whether the heart rate of the user is abnormal based on the first heart rate and the second heart rate comprises:
determining a first mean value and a first variance value based on a first heart rate acquired when the user is in an intoxicated state;
determining a second mean value and a second variance value based on all second heart rates acquired in a set detection period;
and determining that the heart rate of the user is abnormal under the condition that the second average value is larger than the first average value and the second variance value is larger than the first variance value.
6. The method of claim 2, wherein the determining whether the user's blood oxygen saturation is abnormal based on the first blood oxygen saturation and the second blood oxygen saturation comprises:
determining a product of the first blood oxygen saturation and a set percentage as a third threshold;
determining that the blood oxygen saturation of the user is abnormal if the first number of second blood oxygen saturation is less than the third threshold; wherein the first number is greater than half of the total number of second blood oxygen saturation levels acquired in a set detection period;
and determining that the blood oxygen saturation of the user is normal under the condition that the first number of second blood oxygen saturation is greater than or equal to the third threshold.
7. The method according to any one of claims 1 to 6, further comprising one of:
outputting prompt information representing reduced drinking when the drunk frequency is greater than the set times;
transmitting second data corresponding to the drunk state to a first terminal under the condition that the user is in the drunk state, wherein the first terminal characterizes a terminal which is in pairing connection with the wearable equipment; the second data are used for the first terminal to determine the drunk frequency, and the prompt information is output under the condition that the determined drunk frequency is larger than the set times.
8. An drunk detecting device, characterized by comprising:
a determining unit, configured to determine, based on first data and second data acquired by a wearable device, whether a user of the wearable device is in a drunk state; wherein the first data comprises a first heart rate, a first blood oxygen saturation and/or a first gait data acquired when the user is in an intoxicated state; the second data includes a second heart rate, a second blood oxygen saturation, and/or second gait data acquired during a set detection period.
9. A wearable device, comprising: a processor and a memory for storing a computer program executable on the processor, wherein the processor is configured to perform the steps of the drunkenness detection method as claimed in any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the drunkenness detection method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210238843.XA CN116763267A (en) | 2022-03-11 | 2022-03-11 | Drunk detection method and device, wearable equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210238843.XA CN116763267A (en) | 2022-03-11 | 2022-03-11 | Drunk detection method and device, wearable equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116763267A true CN116763267A (en) | 2023-09-19 |
Family
ID=88006877
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210238843.XA Pending CN116763267A (en) | 2022-03-11 | 2022-03-11 | Drunk detection method and device, wearable equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116763267A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117481615A (en) * | 2023-12-27 | 2024-02-02 | 网思科技股份有限公司 | Drunk recognition method and device, storage medium and computer equipment |
-
2022
- 2022-03-11 CN CN202210238843.XA patent/CN116763267A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117481615A (en) * | 2023-12-27 | 2024-02-02 | 网思科技股份有限公司 | Drunk recognition method and device, storage medium and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220159216A1 (en) | Monitoring process | |
| US20170293727A1 (en) | Intelligent blood pressure monitoring | |
| US20040199056A1 (en) | Body monitoring using local area wireless interfaces | |
| Thorpe et al. | Development of a sensor-based behavioral monitoring solution to support dementia care | |
| US20190373114A1 (en) | System and method for controlling notifications in an electronic device according to user status | |
| CN109347653A (en) | A kind of Indexes Abnormality discovery method and apparatus | |
| CN110477884B (en) | Health reminding method, device, equipment and storage medium based on wearable equipment | |
| Moser et al. | Personal health monitoring using a smartphone | |
| CN116763267A (en) | Drunk detection method and device, wearable equipment and storage medium | |
| US20140244294A1 (en) | Apparatus and Method for Network Based Remote Mobile Monitoring of a Medical Event | |
| CN111651041A (en) | Lifting wake-up method and system for mobile equipment | |
| CN110087205B (en) | Method and device for acquiring basic parameters of rescued person | |
| CN113887463A (en) | ICU ward monitoring method and device, electronic equipment and medium | |
| CN109620180A (en) | Control method, apparatus, equipment and the storage medium of wearable device alarm | |
| AU2019200869A1 (en) | Alert System | |
| KR102187389B1 (en) | Device, system and method for managing the person in quarantine | |
| KR101864358B1 (en) | Mobile device for collecting sleeping information, method for collecting sleeping information, and application thereof | |
| CN109700443A (en) | Wearable device, emergency call system and method | |
| CN116417153A (en) | Old person safety warning system of using medicine | |
| US10469659B2 (en) | Method and device for setting up a voice call | |
| CN115547438A (en) | Health management method and system, medical follow-up terminal and health management platform | |
| CN112603279B (en) | Medical information intelligent sharing method and system | |
| CN116823511B (en) | Method and device for identifying social isolation state of user and wearable device | |
| EP4128833A1 (en) | Assistance in controlling the spread of a disease, using networked devices | |
| CN111486990A (en) | Human body state monitoring method, system, device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |