CN115813351B - Tooth grinding action detection method and system - Google Patents
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Abstract
The invention provides a method and a system for detecting a tooth grinding action, wherein the method comprises the steps of obtaining first facial myoelectricity data when a detected object executes a preset action; determining an individual baseline intensity from the first facial myoelectricity data; acquiring second facial myoelectricity data of the detected object in the monitoring period; determining a teething event from the second facial myoelectricity data and the individual baseline intensity. According to the molar action detection scheme provided by the invention, before detection is started, the individual baseline intensity is determined based on the myoelectricity data of the detected object when specific biting muscle activity is executed, the myoelectricity threshold value suitable for the detected object can be set through the individual baseline intensity, the myoelectricity data during the monitoring period is identified on the basis, the molar action can be more accurately identified, and the influence of individual difference of the detected object on the detection result is avoided.
Description
Technical Field
The invention relates to the field of medical equipment, in particular to a method and a system for detecting tooth grinding actions.
Background
Bruxism is a common and frequently occurring disease of the stomatology department, the most common type of which is that the patient experiences bruxism after falling asleep at night. The long-term tooth grinding can cause abnormal tooth abrasion, and various diseases such as tooth ache, loosening, breakage, inflammation and the like are caused. To diagnose and treat bruxism, a doctor needs to know the bruxism that a patient takes place during the entire sleep. Facial myoelectricity data may be used to detect a molar motion and the subject may wear the myoelectricity detection device overnight to collect facial myoelectricity data during sleep.
When myoelectric data is analyzed, a corresponding detection standard, particularly a threshold value of myoelectricity, needs to be set, and when the myoelectric data exceeds the threshold value, the occurrence of the tooth grinding action at the moment is judged. However, different patients are very different, the same threshold cannot be adapted to all patients, the threshold is difficult to set manually, and when the threshold is set unsuitable for the current detection object, the accuracy of the analysis result is very poor, only manual analysis of myoelectricity data can be performed, and the efficiency is very low.
Disclosure of Invention
In view of the above, the present invention provides a method for detecting a tooth grinding motion, comprising: acquiring first facial myoelectricity data of a detection object when the detection object executes a preset action; determining an individual baseline intensity from the first facial myoelectricity data; acquiring second facial myoelectricity data of the detected object in the monitoring period; determining a teething event from the second facial myoelectricity data and the individual baseline intensity.
Optionally, the first facial myoelectricity data includes myoelectricity data acquired when the detection object performs the same preset action for a plurality of times.
Optionally, the preset actions include sustainable actions and/or transient actions.
Optionally, the preset action includes at least one action of forcefully biting, swallowing, chewing, coughing, switching from mouth-closing to mouth-opening and then restoring the mouth-closing, and grinding teeth.
Optionally, when there are multiple preset actions, determining corresponding individual baseline intensities according to the first facial myoelectricity data of each preset action.
Optionally, the preset motion is a force to bite the posterior teeth and the duration reaches a preset duration.
Optionally, determining the individual baseline intensity according to the first facial myoelectricity data specifically includes: determining a plurality of key points in time in the first facial myoelectricity data; calculating the individual baseline intensity using segments of data between a plurality of the key time points.
Optionally, the plurality of key time points includes at least a bite motion stabilization time and a bite motion destabilization time.
Optionally, the plurality of key time points further include a start biting time and an end biting time, and the start biting time, the biting motion stabilizing time, the biting motion destabilizing time and the end biting time are sequentially determined according to the change of the first facial myoelectricity data in the time dimension; the individual baseline intensity is calculated using a data segment between the bite motion stabilization time and the bite motion destabilization time.
Optionally, the individual baseline intensity is an average myoelectric power density over a data segment.
Optionally, determining a tooth grinding event according to the second facial myoelectricity data and the individual baseline intensity specifically comprises: screening all data segments from the second facial myoelectrical data that exceed a molar threshold, wherein the molar threshold is calculated based on the individual baseline intensity; at least two types of tooth event data segments are identified based on the time length of each of the data segments screened, the types including a persistent episode and a phased episode, wherein the length of the tooth event data segment of the persistent episode is greater than the length of the tooth event data segment of the phased episode.
Optionally, identifying at least two types of tooth event data segments based on the time length of each of the data segments selected, further comprising: for each data segment in the total data segments, respectively judging the duration t of the data segment 2 -t 1 Whether or not t is satisfied tonic ＞t 2 -t 1 ≥ t phasic Or t 2 -t 1 ≥t tonic The method comprises the steps of carrying out a first treatment on the surface of the For satisfying t tonic ＞t 2 -t 1 ≥ t phasic Is recorded as a phase onset tooth grinding event data segment for satisfying t 2 -t 1 ≥t tonic Is recorded as a continuous onset tooth grinding event data segment.
Optionally, after identifying at least two types of tooth event data segments according to the time length of each of the data segments selected, further comprising: determining, for the identified phased attack type, whether a time interval between each two adjacent tooth grinding event data segments is below an interval threshold; when the time interval of two adjacent tooth-grinding event data segments is below the interval threshold, the two adjacent tooth-grinding event data segments are merged.
Optionally, iteratively performing said merging process until the time interval of all every two adjacent tooth grinding event data segments is above an interval threshold; after the merging process, further comprising: aiming at each combined tooth grinding event data segment, acquiring the number of the combined tooth grinding event data segments; and judging the merging result with the merging quantity larger than the quantity threshold value as a phasic seizure event.
Optionally, before acquiring the first facial myoelectricity data when the detection object performs the preset action, the method further includes: guiding and instructing the detection object to execute a preset action.
Optionally, guiding and instructing the detection object to perform the preset action specifically includes: the action content is prompted with speech and/or images, as well as duration and/or number of repetitions.
The present invention also provides a system for detecting a molar event, comprising: the wearable recorder is suitable for being worn on the face of a person and used for acquiring myoelectricity data, and the host can acquire the myoelectricity data through the recorder and is used for executing the tooth grinding action detection method.
According to the method and the system for detecting the tooth grinding action, provided by the embodiment of the invention, before starting detection, the individual baseline intensity is determined based on the myoelectricity data when the detection object executes the specific biting muscle activity, the myoelectricity threshold suitable for the detection object can be set through the individual baseline intensity, the myoelectricity data during the monitoring period can be identified on the basis, the tooth grinding action can be more accurately identified, and the influence of the individual difference of the detection object on the detection result is avoided.
The invention provides some alternatives which can distinguish two different molar events, in particular can accurately identify the phased attack event, and indexes such as the frequency and the occurrence time of the two events play a good auxiliary role in diagnosing and treating the molar.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a molar event detection system;
FIG. 2 is a flow chart of a method of detecting a molar motion;
FIG. 3 is a schematic illustration of a tooth grinding event detection process;
FIG. 4 is myoelectric data of a preset motion;
fig. 5 is a segment of myoelectrical data containing two seizure types.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the terms "first," "second," and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Fig. 1 shows a bruxism event detection system comprising a host 1 and a wearable recorder 2, wherein the wearable recorder 2 is adapted to be worn on a person's face for acquiring myoelectric data, the host 1 being able to acquire myoelectric data via the wearable recorder 2. The host 1 may be equipped with one or more wearable recorders 2, two wearable recorders 2 being shown in fig. 1. The wearable recorder 2 can be taken out from the charging groove of the host 1, is inserted with the flexible myoelectricity acquisition electrode, and is worn in the region of the masseter on two sides of the face.
In the working state, the host computer 1 and the wearable recorder 2 communicate in a wireless mode, and control instructions and data acquired by the recorder are transmitted. When the host 1 is closed, the wearable recorder 2 is in a dormant state; when the host 1 is electrified and started, the host 1 and the equipped wearable recorder 2 automatically complete wireless connection, a user can disconnect one or more wearable recorders 2 and convert the one or more wearable recorders into a dormant state through the software system operation of the host 1, and only the wearable recorder 2 which is not dormant is used; when the host 1 is turned off, all the wearable recorders 2 are disconnected and turned into a sleep state.
The data collected by the wearable recorder 2 may also be transmitted to a general electronic device, such as a smart phone. The electronic device preloaded with the supporting software can implement all software functions equivalent to those of the host 1.
As shown in fig. 2, an embodiment of the present invention provides a method for detecting a tooth grinding motion, which may be performed by the above-mentioned host 1 or a general-purpose electronic device, including the following operations:
s1, acquiring first facial myoelectricity data when a detection object executes a preset action. The preset actions may be one or more actions that cause the bite muscle to produce apparent activity.
By way of example, the preset actions include, but are not limited to, forcefully biting, swallowing, chewing, coughing, turning a closed mouth to a mouth-opening, and then restoring a closed mouth, and bruxism. Wherein swallowing, chewing, coughing and bruxism are instantaneous actions, forceful biting and mouth closing are converted into mouth opening and mouth closing recovery are sustainable actions. It is understood that sustainable motion means that at least one link in motion can be controllably held by the subject for a certain period of time, such as a bite hold for several seconds, a mouth hold for several seconds, etc.
It should be noted that any preset action in this step is an action that is performed autonomously and controllably by the subject, not an action that happens to be performed involuntarily during sleep.
In a preferred embodiment, the operation of guiding and instructing the detection object to perform the preset action may be performed before step S1, such as prompting the action content in voice and/or image, and duration and/or repetition number by the host or the smart phone.
By way of example, when the test subject completes the wearing of the recorder and activates the instrument host, an automatic wireless connection between the recorder and host is established. The host machine sends out an order-set instruction, and the detection object is guided to actively execute the following preset actions according to the instruction content:
1. the maximum force bites the posterior teeth for more than 3 seconds and is repeated twice;
2. swallowing oral liquid, repeating twice;
3. cough, repeated twice;
4. closing the upper mouth, keeping the maximum opening, keeping for more than two seconds, and repeating the closing of the upper mouth twice;
after the four instructions are completed, the user is prompted to enter the next waiting start-up stage, and the user keeps the equipment and the electrodes stably attached to the face.
The above actions respectively obtain myoelectricity data of 2 events 1, myoelectricity data of 2 events 2, myoelectricity data of 2 events 3 and myoelectricity data of 2 events 4 according to sampling frequency
And after the designed band-pass filter filters, myoelectricity information in a main frequency band is obtained.
If the detected object does not execute the related action according to the requirement, or the acquired data is interfered and cannot be subjected to subsequent calculation, the acquisition is carried out again.
S2, determining the baseline intensity of the individual according to the first facial myoelectricity data. The individual baseline intensity in this scheme refers to the highest myoelectricity level (the maximum autonomic contraction value, i.e., the signal at 100% muscle activation) when the subject is actively engaged in the bite muscle activity, and the specific calculation modes are various, and the calculation modes adopted for different preset actions are also different. For example, for the embodiment with only one preset action, according to the characteristics of the action, corresponding key points or key segments can be extracted from myoelectricity data, and average myoelectricity or peak value and the like in the key points or key segments are calculated to serve as individual baseline intensity; for embodiments with multiple preset actions, the corresponding individual baseline intensity may be determined based on the first facial myoelectric data of each preset action, respectively, and the final individual baseline intensity may be further synthesized based on these results, such as averaging, etc.
And S3, acquiring second facial myoelectricity data of the detection object in the monitoring period. In general, the scheme is used for detecting sleepThe above steps S1-S2 may be performed at any time other than during sleep. If the detected object does not immediately enter the night sleep state after the personal baseline acquisition is completed, a waiting start time T can be set after the baseline acquisition and before the night tooth grinding monitoring is started wait (if the subject is taking personal baseline acquisitions before sleeping, T can be set wait =0). And during the starting waiting period, the recorder enters a dormant state, is disconnected with the host, is connected with the host again after the starting waiting time is over, and enters a continuous monitoring state. There are various ways for the system to enter the continuous monitoring state from the wait for start state, for example: presetting T in software system wait The device is automatically triggered after the self clock timing is finished; not preset T in software system wait The user issues an instruction to trigger through a software system; presetting T in software system wait When the timing is not finished, the user sends an instruction trigger through the software system, the timing is stopped, and the waiting starting state is used for entering the continuous monitoring state.
During the monitoring period, the wearable recorder and the host continuously acquire and transmit myoelectricity data, and the duration T during the monitoring period monitor The control method comprises the following steps: presetting T in software system monitor The monitoring is automatically finished after the self clock of the equipment is finished; not preset T in software system monitor The user issues an instruction to finish monitoring through a software system; presetting T in software system monitor When the timing is not finished, the user gives an instruction to finish monitoring through the software system.
S4, determining the tooth grinding event according to the second facial myoelectricity data and the individual baseline intensity. This operation may be performed automatically after the end of the monitoring period, or may be performed by a user opening. For example, a physician may operate in a software system to perform data analysis and generate statistical reports. This step does not have to be performed after each monitoring, and may be performed uniformly after a plurality of monitoring, or may be performed separately for a certain historical monitoring data.
A threshold value for the teeth of a subject may be determined based on the individual baseline intensity, facial myoelectrical data during the monitoring period is compared to the threshold value, and a segment of data above the threshold value is determined to have occurred to the subject as a tooth grinding action, i.e., a tooth grinding event occurred. It is of course also possible to combine multiple molar events from the time dimension, such as for two molar events that are very closely spaced in time, into one molar event.
Thus, indexes such as the total number of times and the like of the tooth grinding event of the detected object at which time points can be obtained in the whole monitoring period.
FIG. 3 illustrates an exemplary bruxism event detection process, wherein T1-T3 refer to the time at which the subject performs the corresponding preset action and is electromyographically acquired, and the example steps of determining the baseline intensity of the individual employ multiple preset actions and repeat the same multiple times. Step S4 is not labeled in the figure, as this step may be performed at any time after step S3.
According to the method for detecting the tooth grinding action, provided by the embodiment of the invention, before starting detection, the individual baseline intensity is determined based on the myoelectricity data when the detection object executes the specific biting muscle activity, the myoelectricity threshold suitable for the detection object can be set through the individual baseline intensity, the myoelectricity data during the monitoring period can be identified on the basis, the tooth grinding action can be more accurately identified, and the influence of the individual difference of the detection object on the detection result is avoided.
In one embodiment, the predetermined action is a hard bite into the posterior tooth and the duration is a predetermined period of time, which may specifically be a maximum force bite into the posterior tooth for more than 3 seconds. Under the prompt and guidance of the host, the detected object performs this action, and the collected facial myoelectricity data is shown in fig. 4, where the abscissa is time and the ordinate is myoelectricity voltage value.
Further, determining an individual baseline intensity from the first facial myoelectricity data comprises:
a plurality of key time points are determined in the first facial myoelectricity data. Individual baseline intensities are calculated using segments of data between a plurality of key time points. The key time points of different preset actions are different, taking the action of forcefully biting the back teeth as an example, and the key time points at least comprise the biting action stabilization time and the biting action destabilization time. For other actions, defining corresponding key time points according to the characteristics of the bite muscle activities of the actions, such as chewing actions, wherein the key time points comprise a plurality of pairs of periodic muscle activity starting time and ending time; for example, the mouth is changed into the mouth is opened, and then the mouth is closed again, and the key time points comprise the myoelectric activity starting time of the mouth opening action and the myoelectric activity ending time after the mouth is closed.
As a preferred embodiment, the multiple key points in time for the act of forcefully biting the posterior teeth also include a start bite time t r And ending bite time t q Sequentially determining the starting tooth biting time t according to the change of facial myoelectricity data in the time dimension r Time t for stabilizing biting motion b Time t of instability of biting action s And ending bite time t q . In the embodiment shown in fig. 4, the time t is stabilized according to the biting action b To the instability time t of the biting action s The myoelectric data therebetween calculates individual baseline intensities, preferably the average myoelectric power density of the data segment.
More specifically, firstly, the rest amplitude of the detection object and the equipment is calculated by using myoelectric data in a rest state before the action starts, then the key time point can be identified through a pre-established statistical model based on the myoelectric data after the rest amplitude is analyzed frame by frame. The statistical model is used for specifying the magnitude relation between various key time points, and can be obtained by statistics of a large amount of experimental data or can be set according to experience values. With respect to the average myoelectric power density, a person skilled in the art should know how to calculate the average myoelectric power density given the frequency information, the target time period and the myoelectric voltage value at each moment.
In one embodiment, the present regimen distinguishes between two types of molar events, a sustained-onset event and a phased-onset event, respectively. A sustained (tonic) episode, defined as a long-lasting (e.g., 1-3 seconds) primary biting muscle activity with myoelectric data exceeding a threshold (which is set according to the individual's baseline intensity) described above; phasic (phasic) episodes are defined as a number of myoelectrical activities of at least N consecutive shorter duration (e.g., 0.1-0.5 seconds), shorter interval, myoelectrical data exceeding a threshold.
The operation of determining a teething event based on the second facial myoelectricity data and the individual baseline intensity in this embodiment specifically includes:
screening all data segments from the second facial myoelectric data that exceed a threshold of teeth grinding, wherein the threshold of teeth grinding is calculated based on the individual baseline intensity, the threshold of teeth grinding is specifically a percentage of the individual baseline intensity and can be expressed as V base ×w%，V base And (5) representing the individual baseline intensity, wherein the value of w is 20-40.
At least two types of tooth event data segments are identified based on the time length of each data segment selected, the types including a permanent episode and a phased episode, wherein the length of the tooth event data segment for the permanent episode is greater than the length of the tooth event data segment for the phased episode.
Two time thresholds t may be set tonic And t phasic To distinguish between two types of bruxism events, t tonic ＞t phasic . For each of the entire data segments, the duration t of the data segment is determined separately 2 -t 1 Whether or not t is satisfied tonic ＞t 2 -t 1 ≥ t phasic Or t 2 -t 1 ≥t tonic The method comprises the steps of carrying out a first treatment on the surface of the Wherein t is 2 Representing a point in time, t, corresponding to the end of a data segment 1 Indicating a point in time corresponding to the start position of the data segment.
For satisfying t tonic ＞t 2 -t 1 ≥ t phasic Is recorded as a phase onset tooth grinding event data segment for satisfying t 2 -t 1 ≥t tonic Is recorded as a continuous onset tooth grinding event data segment.
Fig. 5 shows typical myoelectric data segments for two events, a continuous-episode molar event data segment, and a plurality of phased-episode molar event data segments, can be identified according to the procedure described above. For the identified data segment of the type of persistent episodes, it may be determined directly as a persistent episode; for the data segments of the phasic episode type, the number, interval, etc. can be counted, and the data segments can be directly used as output results, or can be further processed.
As an alternative embodiment, the following processing may be further performed:
determining, for the identified phase seizure type of the tooth-grinding event data segments, whether a time interval of each two adjacent tooth-grinding event data segments is below an interval threshold; when the time interval of two adjacent tooth-grinding event data segments is below the interval threshold, the two adjacent tooth-grinding event data segments are merged. It should be noted that, in this scheme, two data segments are combined, and instead of performing fusion calculation on the data at two ends to form one data segment, the two data segments are regarded as one data segment.
Specifically, first, the initial two phase onset tooth grinding event data segments (abbreviated as phase data segments) are extracted, and the time interval delta of the two phase data segments is determinedtWhether or not it is less than the interval thresholdt interval . If so, the two phase data segments belong to the same phase attack, the two phase data segments are combined into one phase action record, and then delta is carried out with the next phase data segmenttJudging; if not, the two phase data segments do not belong to the same phase attack, and the second phase data segment is continued to be delta-processed with the next phase data segmenttAnd (5) judging. By pushing in this way a until the last two phase data segments are completedtJudging to obtain the merged phase action record. The merging process is performed iteratively, and the time interval between adjacent phase data segments after merging is higher than the interval threshold.
On this basis, the following processing can be further performed:
for each of the merged segments of the resulting teething event data, the number of segments of the teething event data that it merges is obtained. And in the merging process of the phase data segments, recording the merging number of each new phase data segment. And judging the merging result with the merging quantity larger than the quantity threshold value as a phasic seizure event. Taking myoelectric data shown in fig. 5 as an example, 5 pieces of phase data can be screened initially, and after the merging process, they are merged into 1 piece of phase data, and the merging number is 5. Assuming a number threshold of 3, there are 1 phased seizure events in the example shown in fig. 5; assuming there is a merge result with a merge number less than 3, the merge result does not belong to a phasic episode event.
The preferred embodiment provided by the invention can distinguish two different molar events, in particular can accurately identify the phased attack event, and indexes such as the frequency and the occurrence time of the two events play a good auxiliary role in diagnosing and treating the molar.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (10)
1. A method of detecting a tooth grinding action, comprising:
acquiring first facial myoelectricity data when a detection object executes a preset action, wherein the preset action comprises forceful biting of the rear teeth, and the duration time reaches a preset duration time;
determining a plurality of key time points in the first facial myoelectricity data, wherein the key time points comprise a starting tooth-biting time, a tooth-biting motion stabilizing time, a tooth-biting motion destabilizing time and an ending tooth-biting time, sequentially determining the starting tooth-biting time, the tooth-biting motion stabilizing time, the tooth-biting motion destabilizing time and the ending tooth-biting time according to the change of the first facial myoelectricity data in a time dimension, and calculating individual baseline intensity by utilizing a data segment between the tooth-biting motion stabilizing time and the tooth-biting motion destabilizing time;
acquiring second facial myoelectricity data of the detected object in the monitoring period;
screening all data segments from the second facial myoelectrical data that exceed a molar threshold, wherein the molar threshold is calculated based on the individual baseline intensity;
identifying at least two types of tooth event data segments according to the time length of each data segment selected, wherein the types comprise a persistent attack and a phased attack, and the length of the tooth event data segment of the persistent attack is greater than that of the tooth event data segment of the phased attack;
determining, for the identified phase seizure type of the tooth-grinding event data segments, whether a time interval of each two adjacent tooth-grinding event data segments is below an interval threshold; combining the two adjacent tooth-grinding event data segments when the time interval of the two adjacent tooth-grinding event data segments is lower than the interval threshold value, and iteratively executing the combined processing until the time interval of all every two adjacent tooth-grinding event data segments is higher than the interval threshold value;
and acquiring the number of the combined tooth grinding event data segments according to the tooth grinding event data segments obtained by each combining process, and judging the combined result that the combined number is larger than a number threshold value as a phase attack event.
2. The method of claim 1, wherein the first facial myoelectrical data comprises myoelectrical data acquired by the subject while performing the same preset action multiple times.
3. The method according to claim 1, wherein the preset actions comprise sustainable actions and/or transient actions.
4. The method of claim 3, wherein the preset actions further comprise at least one of swallowing, chewing, coughing, switching from closed mouth to open mouth, and returning to closed mouth, and grinding teeth.
5. The method of claim 4, wherein when there are a plurality of said preset actions, determining a corresponding individual baseline intensity from first facial myoelectric data for each of said preset actions, respectively.
6. The method of any one of claims 1-5, wherein the individual baseline intensity is an average myoelectric power density over a data segment.
7. The method of claim 1, wherein identifying at least two types of tooth-grinding event data segments based on the time length of each of the data segments screened, further comprises:
for each data segment in the total data segments, respectively judging the duration t of the data segment 2 -t 1 Whether or not t is satisfied tonic ＞t 2 -t 1 ≥ t phasic Or t 2 -t 1 ≥t tonic ；
For satisfying t tonic ＞t 2 -t 1 ≥ t phasic Is recorded as a phase onset tooth grinding event data segment for satisfying t 2 -t 1 ≥t tonic Is recorded as a continuous onset tooth grinding event data segment.
8. The method of claim 1, further comprising, prior to acquiring the first facial myoelectricity data of the test subject when performing the preset action:
guiding and instructing the detection object to execute a preset action.
9. The method of claim 8, wherein directing and instructing the detection object to perform the preset action specifically comprises: the action content is prompted with speech and/or images, as well as duration and/or number of repetitions.
10. A system for detecting a tooth grinding event, comprising: a host and a wearable recorder, wherein the wearable recorder is adapted to be worn on a human face for acquiring myoelectricity data, the host acquiring the myoelectricity data through the wearable recorder and for performing the molar motion detection method of any one of claims 1-9.
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| CN109528165A (en) * | 2018-11-26 | 2019-03-29 | 广东小天才科技有限公司 | Reminding system, method and device based on intelligent equipment |
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| DK1610862T3 (en) * | 2003-04-01 | 2017-03-06 | Sunstar Suisse Sa | Apparatus for monitoring muscle activity |
| CN1810319A (en) * | 2005-01-25 | 2006-08-02 | 电子科技大学 | Electromechanical bruxism monitoring and treating equipment |
| JP5418666B2 (en) * | 2010-03-19 | 2014-02-19 | 富士通株式会社 | Bruxism detection apparatus and computer program for bruxism detection |
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| CN109528165A (en) * | 2018-11-26 | 2019-03-29 | 广东小天才科技有限公司 | Reminding system, method and device based on intelligent equipment |
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