CN112386250B - Intelligent sleep monitoring operation method - Google Patents
Intelligent sleep monitoring operation method Download PDFInfo
- Publication number
- CN112386250B CN112386250B CN202011281474.XA CN202011281474A CN112386250B CN 112386250 B CN112386250 B CN 112386250B CN 202011281474 A CN202011281474 A CN 202011281474A CN 112386250 B CN112386250 B CN 112386250B
- Authority
- CN
- China
- Prior art keywords
- sleep
- acceleration
- critical value
- time
- state
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000012544 monitoring process Methods 0.000 title claims abstract description 23
- 230000001133 acceleration Effects 0.000 claims abstract description 58
- 230000011218 segmentation Effects 0.000 claims abstract description 5
- 230000000694 effects Effects 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 4
- 230000004622 sleep time Effects 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 2
- 230000033001 locomotion Effects 0.000 abstract description 7
- 230000000276 sedentary effect Effects 0.000 abstract description 6
- 230000004617 sleep duration Effects 0.000 abstract 1
- 238000001914 filtration Methods 0.000 description 16
- 238000012935 Averaging Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 238000003326 Quality management system Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000003860 sleep quality Effects 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
- A61B5/103—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
- A61B5/1123—Discriminating type of movement, e.g. walking or running
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4812—Detecting sleep stages or cycles
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Physics & Mathematics (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Physiology (AREA)
- Dentistry (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Anesthesiology (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses an intelligent sleep monitoring operation method, which comprises the following steps: step a: collecting a user triaxial acceleration a x、ay、az; step b: when judging that the user is not in a motion state according to the triaxial acceleration information, selecting to execute a sleep monitoring step; the sleep monitoring step is based on the regularity of sleep, adopts triaxial acceleration to judge the sleep or sedentary state, has simply and effectively realized the control of sleep state, specifically can monitor shallow sleep, deep sleep duration and make the time slice segmentation, can also effectively avoid misjudgement as sleep with states such as sedentary, reading on bed, watching film etc..
Description
Technical Field
The invention relates to the field of intelligent health management, in particular to an intelligent sleep monitoring operation method.
Background
Along with the rapid development of wearable equipment technology, the pedometer, the sleep quality management system and other applications are widely applied, but most of the existing pedometers realize the effect of noise filtering by using an adaptive algorithm, a forward prediction method, an adaboost KNN algorithm, a Kalman filtering algorithm and the like, and the pedometer is complex in operation, large in operation amount, and large in occupied operation space when being applied to the wearable equipment.
In addition, most of the existing step counting algorithms use a fixed threshold value as a reference to judge whether to count steps, and cannot respond to the change of the motion state of a user in time, so that the step counting result is seriously distorted.
In addition, the existing wearable device has only one function of step counting or sleep monitoring, or two programs of step counting and sleep monitoring are simultaneously executed in one device, but a user can be in one state of movement or no movement, so that the utilization rate of running space is greatly reduced, and the invention is developed based on the fact that the running space utilization rate is greatly reduced.
Disclosure of Invention
Aiming at the technical problems existing in the prior art, the invention provides an intelligent step counting operation method, which comprises the following steps:
step a: sequentially collecting the triaxial acceleration a x、ay、az of the user;
step b: judging whether the user is in an active state or not according to the triaxial acceleration information, and if so, executing a step counting step; if not, abandoning or executing the sleep monitoring step;
The step counting step comprises the following steps:
Step 101: data axis combination processing; according to the formula Calculating the combined acceleration and forming a change curve of the combined acceleration on a time axis, wherein a s is the combined acceleration;
step 102: sequentially carrying out moving average filtering treatment on the combined acceleration a s;
Step 103: acquiring an effective peak value Top and a valley value Btm of the acceleration a s;
step 104: respectively carrying out effectiveness weighting on the peak value Top and the valley value Btm;
Step 105: performing fuzzy step counting; the specific fuzzy step counting mode is as follows: when a peak value Top and a valley value Btm are obtained, checking whether the weight reaches the step counting requirement or not, and if not, counting steps; if the requirement is met, checking whether the previous state is a step counting state, if yes, counting the step +1, if not, counting from the step N, and setting the step N as the step counting state, wherein N is a natural number larger than 1;
step 106: and outputting a step counting result.
Preferably, in the step b, the method for judging whether the user is in a motion state according to the triaxial acceleration information comprises the following steps: an acceleration critical value is preset, the acceleration critical value is determined to be a fluctuation state when the acceleration critical value is exceeded, the single fluctuation state is determined to be an active state when the single fluctuation state is more than 20s, and otherwise, the acceleration critical value is determined to be an inactive state.
Preferably, the threshold is 0.125g, where g is the gravitational acceleration.
Preferably, n=10.
Preferably, the specific scheme of the moving average filtering processing in step 102 is: and averaging every five adjacent numbers to obtain a first order filtering number, and averaging the five adjacent numbers based on the first order filtering number to obtain second order data.
Preferably, step 103 is specifically to obtain peak value Top and valley value Btm by comparing acceleration change trend, and filter out invalid peak value Top and valley value Btm by:
Taking the space between two adjacent peaks as an interval, if the current interval time length is longer than 0.6S, resetting the weight value, ending the step counting state, and outputting a step counting result, and if the current interval time length is shorter than 0.2S, judging as an invalid peak value and ignoring the peak value point; the same applies to the filtering of invalid valleys.
Preferably, the method of validity weighting the peak Top and the valley BTM in step 104 is as follows: taking four intervals as one period, namely taking two steps as one week, namely taking one step of each of left and right feet as one period; comparing each interval duration t N of the current period with the corresponding interval duration t N-1 of the previous period, wherein the corresponding interval duration t N-1 of the previous period satisfies 70% t N-1≤tN≤130％tN-1, and if the corresponding interval duration t N of the current period is equal to the corresponding interval duration t N-1 of the previous period, the weight value is equal to one, otherwise the weight value is equal to one; wherein the weight value represents the confidence level of the peak value Top or valley value Btm, when the weight value is greater than 12 and less than 18, the corresponding peak value Top or valley value BTM is judged to be valid, otherwise, the corresponding peak value Top or valley value BTM is judged to be invalid.
Preferably, the method for effectively weighting the peak Top and the valley BTM in step 104 further includes: comparing each peak value of the current period with the corresponding peak value of the previous period, wherein the current peak value is greater than 80% of the corresponding peak value of the previous period and less than 120% of the corresponding peak value of the previous period, adding one to the weight value, otherwise, subtracting one from the weight value, and judging that the corresponding peak value Top is valid when the weight value is greater than 12 and less than 18, otherwise, judging that the corresponding peak value Top is invalid; the validity weighting method for the valley Btm is the same as the peak Top.
The invention also provides an intelligent sleep monitoring operation method, which specifically comprises the following steps:
step a: collecting triaxial acceleration of a user, wherein the triaxial acceleration is a x、ay、az respectively;
step b: judging whether the user is in an active state according to the triaxial acceleration information, if so, executing the step of giving up or counting steps; if not, executing a sleep monitoring step;
the sleep monitoring step comprises the following steps:
Step 201: performing stability detection and judgment on the collected triaxial acceleration;
Step 202: accumulated settling time t s and unstable time t m;
Step 203: performing sleep determination, if the sleep is determined, executing step 204, and if the non-sleep is determined, ending the sleep monitoring step;
Step 204: performing sleep time slice segmentation according to the recording results of the step 202 and the step 203;
step 205: sleep state data is saved and output.
Preferably, in the step b, the method for judging whether the user is in a motion state according to the triaxial acceleration information comprises the following steps: an acceleration critical value is preset, the acceleration critical value is determined to be a fluctuation state when the acceleration critical value is exceeded, the single fluctuation state is determined to be an active state when the single fluctuation state is more than 20s, and otherwise, the acceleration critical value is determined to be an inactive state.
Preferably, in the step 201, the specific stability determining method includes: and determining that the fluctuation state exceeds the critical value, and determining that the single fluctuation state is unstable if the single fluctuation state is more than 20s, otherwise, determining that the single fluctuation state is stable.
Preferably, the sleep determination method in step 203 is as follows: taking limb rest over 20min, namely a x、ay、az, as a judgment standard for entering deep sleep, wherein a rest period over 20min comprises transition time from shallow sleep to deep sleep to shallow sleep, the first 8min and the last 7min in the rest period over 20min in the first time of single continuous sleep are judged to be shallow sleep, and the first 4min and the last 3min of the second to last period are judged to be shallow sleep; if a few periods of time with extremely little activity appear in front of the deep sleep period, the periods of time are recorded as undetermined, and the undetermined periods of time are not converted into the shallow sleep periods until the limbs are still for more than 20 minutes, namely, the states of a x、ay、az value is smaller than a critical value, so that the states of low activity states such as sedentary, reading books on a bed, watching films and the like are prevented from being misjudged as shallow sleep.
Preferably, the threshold is 0.125g, where g is the gravitational acceleration.
The invention has the beneficial effects that:
1. The intelligent step counting operation method provided by the invention adopts a sliding filtering and fuzzy step counting mode, has small operation amount and simple operation, is applied to wearable equipment, has small required code amount, occupies small equipment operation space, and can ensure step counting precision; in addition, the reliability of the peak value and the valley value is further evaluated by adopting an effectiveness weighting mode, and the step is counted by adopting a fuzzy principle, so that the step counting precision is further improved.
2. The intelligent sleep monitoring operation method provided by the invention is based on the regularity of sleep, adopts the triaxial acceleration stability to determine the dynamic or static time slices, establishes the undetermined time slices before falling asleep, adopts the delay judging method to judge the sleep or sedentary state, simply and effectively realizes the sleep state monitoring, particularly can monitor the time length of shallow sleep and deep sleep and make time slice segmentation, and simultaneously can effectively avoid misjudging the states such as sedentary, reading books on a bed, watching movies and the like as sleep.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and the specific examples.
Referring to fig. 1, a flowchart of an embodiment of an intelligent step counting and sleep monitoring operation method of the present invention is shown, where the method includes:
Step a: and sequentially collecting the triaxial acceleration of the user, wherein the triaxial acceleration is a x、ay、az respectively.
Step b: judging whether the user is in an active state according to the triaxial acceleration information, and if so, executing a step counting step; if not, a sleep monitoring step is performed.
The method for judging whether the user is in a motion state according to the triaxial acceleration information comprises the following steps: and if the acceleration peak exceeds 0.125g as a fluctuation recording standard, filtering acceleration changes lower than 0.125g, and judging that the single continuous fluctuation state is an active state if the single continuous fluctuation state is more than 20s, otherwise, judging that the single continuous fluctuation state is an inactive state.
The step counting step comprises the following steps:
Step 101: data axis combination processing; according to the formula Calculating the combined acceleration and forming a change curve of the combined acceleration on a time axis, wherein a s is the combined acceleration;
step 102: sequentially carrying out moving average filtering treatment on the combined acceleration a s;
The specific scheme of the moving average filtering treatment is as follows: averaging every five adjacent numbers to obtain a first-order filtering number, and averaging the five adjacent numbers based on the first-order filtering number to obtain second-order data; sequentially scrolling to achieve the effect of second-order smooth filtering;
Step 103: acquiring an effective peak value Top and a valley value Btm of the acceleration a s; specifically, peak value Top and valley Btm are obtained by comparing acceleration change trend, and invalid peak value Top and valley Btm are filtered out by the following method:
taking the space between two adjacent peaks as an interval, if the current interval time length is longer than 0.6S, resetting the weight value, ending the step counting state, and outputting a step counting result, and if the current interval time length is shorter than 0.2S, judging as an invalid peak value and ignoring the peak value point; the mode of filtering invalid valley values is the same;
step 104: respectively carrying out effectiveness weighting on the peak value Top and the valley value Btm;
The method for effectively weighting the peak Top comprises the following steps: taking four intervals as one period, namely taking two steps as one week, namely taking one step of each of left and right feet as one period; comparing each interval duration t N of the current period with the corresponding interval duration t N-1 of the previous period, wherein the corresponding interval duration t N-1 of the previous period satisfies 70% t N-1≤tN≤130％tN-1, and if the corresponding interval duration t N of the current period is equal to the corresponding interval duration t N-1 of the previous period, the weight value is equal to one, otherwise the weight value is equal to one; comparing each peak value of the current period with the corresponding peak value of the previous period, wherein the current peak value is greater than 80% of the corresponding peak value of the previous period and less than 120% of the corresponding peak value of the previous period, and the weight value is increased by one, otherwise, the weight value is decreased by one; the effectiveness weighting method is carried out on the valley Btm and the peak value Top; when the weight value is greater than 12 and less than 18, the corresponding peak Top or valley BTM is valid, otherwise, it is determined to be invalid.
Step 105: performing fuzzy step counting;
the specific fuzzy step counting mode is as follows: when a peak value Top and a valley value Btm are obtained, checking whether the weight reaches the step counting requirement or not, and if not, counting steps; if the requirement is met, checking whether the previous state is a step counting state, if yes, counting the step +1, and if not, counting the step from the step 10, and setting the step counting state.
Step 106: and outputting a step counting result.
The sleep monitoring step comprises the following steps:
Step 201: performing stability detection and judgment on the collected triaxial acceleration;
the specific stability judging method comprises the following steps: and (3) taking the acceleration peak value exceeding 0.125g as a fluctuation recording standard, filtering acceleration changes lower than 0.125g, judging that the acceleration is unstable if the single continuous fluctuation state is more than 20s, and otherwise, judging that the acceleration is stable.
Step 202: accumulated settling time t s and unstable time t m;
step 203: performing sleep determination, if the sleep is determined, executing step 204, and if the non-sleep is determined, ending the sleep determination and detection steps;
The sleep judging method comprises the following steps: taking limb rest over 20min, namely a x、ay、az value less than 0.125g, as a judgment standard for entering deep sleep, wherein a rest period over 20min comprises transition time from shallow sleep to deep sleep to shallow sleep, the first 8min and the last 7min in the rest period over 20min in the first time of single continuous sleep are judged to be shallow sleep, and the first 4min and the last 3min of the second to last period are judged to be shallow sleep; if a few periods of time with extremely few activities appear in front of the deep sleep period, recording the periods of time as a pending time slice, and not converting the pending periods of time into shallow sleep periods until the limbs are stationary for more than 20min, namely, the states of a x、ay、az values are smaller than 0.125g, so that the states of low activities such as sedentary sitting, reading books on a bed, watching movies and the like are prevented from being misjudged as shallow sleep;
Step 204: performing sleep time slice segmentation according to the recording results of the step 202 and the step 203;
step 205: sleep state data is saved and output.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (2)
1. An intelligent sleep monitoring operation method is characterized by comprising the following steps:
step a: sequentially collecting the triaxial acceleration of the user, namely a x、ay、az;
Step b: judging whether the user is in an active state or not according to the triaxial acceleration information, and if not, executing a sleep monitoring step;
wherein the sleep monitoring step comprises:
Step 201: performing stability detection and judgment on the collected triaxial acceleration;
Step 202: accumulated settling time t s and unstable time t m;
Step 203: performing sleep determination, if the sleep is determined, executing step 204, and if the non-sleep is determined, ending the sleep monitoring step;
Step 204: performing sleep time slice segmentation according to the recording results of the step 202 and the step 203;
Step 205: storing and outputting sleep state data;
the specific stability determination method in step 201 is as follows: the acceleration critical value is preset, the acceleration critical value is determined to be in a fluctuation state when the acceleration critical value exceeds the acceleration critical value, the acceleration critical value is determined to be in a static state when the acceleration critical value is smaller than the acceleration critical value, the acceleration critical value is determined to be unstable when the single fluctuation state reaches more than 20s, and otherwise, the acceleration critical value is determined to be stable;
The sleep determination method in step 203 is as follows: taking the limb rest of more than 20min, namely that the a x、ay、az value is smaller than the critical value as a judgment standard for entering deep sleep, wherein the rest period of more than 20min comprises the transition time from shallow sleep to deep sleep to shallow sleep, the first 8min and the last 7min in the rest period of more than 20min of the first time in single continuous sleep are judged to be shallow sleep, and the first 4min and the last 3min of the second to last period are judged to be shallow sleep; if a few periods of time with extremely little activity appear in front of the deep sleep period, the periods of time are recorded as undetermined time slices, and the undetermined periods of time are converted into shallow sleep periods until the limbs are stationary for more than 20 minutes, namely, the states with the a x、ay、az values smaller than the critical value appear, so that the situation that the low activity state is misjudged as shallow sleep is avoided.
2. The intelligent sleep monitoring operation method according to claim 1, wherein: the critical value is 0.125g, where g is the gravitational acceleration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011281474.XA CN112386250B (en) | 2018-05-28 | 2018-05-28 | Intelligent sleep monitoring operation method |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011281474.XA CN112386250B (en) | 2018-05-28 | 2018-05-28 | Intelligent sleep monitoring operation method |
| CN201810523458.3A CN108937852B (en) | 2018-05-28 | 2018-05-28 | Intelligent step counting operation method |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810523458.3A Division CN108937852B (en) | 2018-05-28 | 2018-05-28 | Intelligent step counting operation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112386250A CN112386250A (en) | 2021-02-23 |
| CN112386250B true CN112386250B (en) | 2024-07-09 |
Family
ID=64491898
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810523458.3A Active CN108937852B (en) | 2018-05-28 | 2018-05-28 | Intelligent step counting operation method |
| CN202011281474.XA Active CN112386250B (en) | 2018-05-28 | 2018-05-28 | Intelligent sleep monitoring operation method |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810523458.3A Active CN108937852B (en) | 2018-05-28 | 2018-05-28 | Intelligent step counting operation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN108937852B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109620158B (en) * | 2018-12-28 | 2021-10-15 | 惠州Tcl移动通信有限公司 | Sleep monitoring method, intelligent terminal and storage device |
| CN113138007B (en) * | 2020-01-19 | 2022-12-27 | 四川省应他利科技有限公司 | Body weight and step counting detection method and subminiature body weight data intelligent processing board |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105030199A (en) * | 2015-06-24 | 2015-11-11 | 深圳市元征软件开发有限公司 | Sleep monitoring method and device |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8096960B2 (en) * | 2004-01-09 | 2012-01-17 | Loree Iv Leonor F | Easy wake device |
| CN102551735B (en) * | 2011-12-31 | 2014-12-03 | 北京超思电子技术股份有限公司 | Blood oxygen measuring instrument |
| WO2014191803A1 (en) * | 2013-05-27 | 2014-12-04 | Tata Consultancy Services Limited | Acceleration-based step activity detection and classification on mobile devices |
| JP2016067811A (en) * | 2014-10-01 | 2016-05-09 | セイコーエプソン株式会社 | Biological information detection system, biological information detection device and control method of biological information detection system |
| CN104990562B (en) * | 2015-06-29 | 2018-02-23 | 合肥工业大学 | Step-recording method based on auto-correlation computation |
| CN105004349B (en) * | 2015-06-30 | 2018-08-31 | 深圳还是威健康科技有限公司 | A kind of step-recording method |
| EP3112811B1 (en) * | 2015-07-02 | 2019-02-27 | Embedded Sensor Solutions B.V. | System and method for processing a foot acceleration signal |
| CN105640508B (en) * | 2016-03-30 | 2018-09-18 | 安徽华米信息科技有限公司 | Real-time sleep monitor method and device, intelligent wearable device |
| CN106983515A (en) * | 2017-04-17 | 2017-07-28 | 安徽咏鹅家纺股份有限公司 | A kind of intelligent sleep monitoring system |
| CN107091650A (en) * | 2017-04-27 | 2017-08-25 | 重庆邮电大学 | A kind of software step-recording method based on mobile phone acceleration and range sensor |
| CN107811641A (en) * | 2017-11-17 | 2018-03-20 | 上海斐讯数据通信技术有限公司 | A kind of method and system of monitoring sleep |
| CN108186024A (en) * | 2017-12-27 | 2018-06-22 | 宁波安尚川智能科技有限公司 | A kind of movement gait monitor and multi-parameter sensor data processing method |
| US11185281B2 (en) * | 2018-02-20 | 2021-11-30 | Koninklijke Philips N.V. | System and method for delivering sensory stimulation to a user based on a sleep architecture model |
-
2018
- 2018-05-28 CN CN201810523458.3A patent/CN108937852B/en active Active
- 2018-05-28 CN CN202011281474.XA patent/CN112386250B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105030199A (en) * | 2015-06-24 | 2015-11-11 | 深圳市元征软件开发有限公司 | Sleep monitoring method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108937852A (en) | 2018-12-07 |
| CN112386250A (en) | 2021-02-23 |
| CN108937852B (en) | 2021-06-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Paluš et al. | Direction of coupling from phases of interacting oscillators: an information-theoretic approach | |
| CN106289309A (en) | Step-recording method based on 3-axis acceleration sensor and device | |
| CN112386250B (en) | Intelligent sleep monitoring operation method | |
| US9474472B2 (en) | Apparatus, method, and system for accurate estimation of total energy expenditure in daily activities | |
| CN106681619B (en) | Method and device for controlling energy consumption of electronic equipment | |
| CN107314777B (en) | Method for dynamically setting step counting threshold value and mobile terminal | |
| Rhudy et al. | A comprehensive comparison of simple step counting techniques using wrist-and ankle-mounted accelerometer and gyroscope signals | |
| CN106175696B (en) | Sleep state monitoring method and system | |
| CN108095694A (en) | State monitoring method, device and the wearable device of wearable device | |
| CN112735563A (en) | Recommendation information generation method and device and processor | |
| CN107348943B (en) | Wearable, portable and alarm human sleep quality monitoring system and method | |
| CN108195395A (en) | Mobile terminal and its step-recording method, storage device | |
| CN118230953A (en) | Health monitoring management method, system and medium based on intelligent equipment platform | |
| CN105662394B (en) | A kind of method and apparatus prompting the user with information | |
| CN114440884A (en) | Intelligent analysis method for human body posture for intelligent posture correction equipment | |
| Nguyen-Huu et al. | Smartwatch/Smartphone Cooperative Indoor Lifelogging System. | |
| CN106994238B (en) | Data processing method and electronic device for executing the same | |
| CN107067649A (en) | A kind of typical behaviour real-time identification method based on wireless wearable aware platform | |
| CN112535464A (en) | PAI-based state evaluation method, PAI-based state evaluation device, PAI-based state evaluation equipment and storage medium | |
| CN109374014B (en) | Step counting method, system and mobile device | |
| CN113499064A (en) | Wi-Fi perception human body tumbling detection method and system in bathroom scene | |
| Aicha et al. | How busy is my supervisor? Detecting the visits in the office of my supervisor using a sensor network | |
| CN112835442A (en) | Power saving method and electronic equipment | |
| CN118121190B (en) | Daily activity level calculating method and related device | |
| CN111248857A (en) | Sleep quality monitoring method and device, computer equipment 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |