Bruxism Detection System With Chin-Mounted Accelerometer Sensor

Abstract

A bruxism detection system is provided that includes a chin-mounted acceleration sensor for detection of teeth grinding and teeth tapping. The system generally includes an acceleration sensor that is adapted to be removably and externally mounted with respect to an individual's chin, a bruxism recording and processing system operable on a local processor, and bruxism analysis software that is operable on the local processor or an adjoint processor (or a combination thereof). The system is designed to be used in any convenient location, including an individual's home, and is generally reusable by multiple people, thus reducing the cost of bruxism diagnosis and bringing a reliable and effective diagnosis tool to the general public.

Claims

1. A bruxism detection system, comprising: a. a detection module that includes a single accelerometer and a local processor that includes a recording and processing system, the recording and processing system including (i) a sensor interface, (ii) a processor with memory, (iii) non-volatile storage, (iv) a communication interface, and (v) a power source; and b. an adjoint processor in communication with the recording and processing system of the detection module, wherein at least one of the local processor and the adjoint processor is programmed to detect bruxism from multi-axis motion data collected by the single accelerometer by calculating motion signature parameters that include one or more of mean, variance and frequency components of motion, and comparing the calculated motion signature parameters with preconfigured motion signature parameters that correspond to bruxism.

2. The bruxism detection system of claim 1, further comprising a mechanism for detachably securing the detection module to a chin of an individual.

3. The bruxism detection system of claim 2, wherein the mechanism is selected from the group consisting of one or more cloth tapes, gauze pads, straps, bandages and combinations thereof.

4. The bruxism detection system of claim 1, wherein the recording and processing system pre-filters motion data captured by the single accelerometer to eliminate data that is not relevant to detection of bruxism.

5. The bruxism detection system of claim 1, wherein the recording and processing system communicates with the adjoint processor in burst transmissions.

6. The bruxism detection system of claim 1, wherein the recording and processing system establishes an electronic handshake with the adjoint processor before communicating motion data captured by the single accelerometer.

7. The bruxism detection system of claim 1, wherein the adjoint processor is a computer.

8. The bruxism detection system of claim 7, wherein the adjoint computer is selected from the group consisting of a smartphone, laptop computer and a desktop computer.

9. The bruxism detection system of claim 1, wherein the adjoint processor is remotely located relative to the detection module and wherein motion data communicated from the recording and processing system is transmitted over a network.

10. The bruxism detection system of claim 1, wherein the local processor is programmed to detect bruxism from multi-axis motion data collected by the single accelerometer by calculating motion signature parameters that include one or more of mean, variance and frequency components of motion, and comparing the calculated motion signature parameters with preconfigured motion signature parameters that correspond to bruxism.

11. The bruxism detection system of claim 1, wherein the adjoint processor is programmed to detect bruxism from multi-axis motion data collected by the single accelerometer by calculating motion signature parameters that include one or more of mean, variance and frequency components of motion, and comparing the calculated motion signature parameters with preconfigured motion signature parameters that correspond to bruxism.

12. The bruxism detection system of claim 1, both the local processor and the adjoint processor are programmed to detect bruxism from multi-axis motion data collected by the single accelerometer by calculating motion signature parameters that include one or more of mean, variance and frequency components of motion, and comparing the calculated motion signature parameters with preconfigured motion signature parameters that correspond to bruxism.

13. A method for detecting bruxism, comprising: a. detachably mounting a detection module with respect to a chin of an individual, the detection module including a single accelerometer and a local processor that includes a recording and processing system, wherein the recording and processing system includes (i) a sensor interface, (ii) a processor with memory, (iii) non-volatile storage, (iv) a communication interface, and (v) a power source; b. collecting multi-axis motion data with the accelerometer included in the detection module; c. pre-filtering the multi-axis motion data to exclude data that is not relevant to bruxism detection; and d. transmitting the filtered motion data to an adjoint processor; and e. processing the filtered motion data at the local processor, the adjoint processor or a combination of the local processor and the adjoint processor to detect bruxism, the processing including calculation of motion signature parameters that include one or more of mean, variance and frequency components of motion, and comparison of the calculated motion signature parameters with preconfigured motion signature parameters that correspond to bruxism.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0018] To assist those of skill in the art in making and using the disclosed bruxism detection system, reference is made to the accompanying figures, wherein:

[0019] FIG. 1 is a schematic depiction of an individual's face with an exemplary bruxism detection module detachably mounted with respect to the individual's chin;

[0020] FIG. 2 is a schematic depiction of data collection and communication according to an exemplary embodiment of the disclosed bruxism detection system;

[0021] FIG. 3 is an exemplary flowchart depicting the processing of accelerometer measurements to assess the presence of bruxism according to the present disclosure;

[0022] FIG. 4 is an exemplary evaluative technique for three sets of acceleration data according to the detection system of the present disclosure; and

[0023] FIG. 5 is a tabular presentation of bruxism analysis with respect to the acceleration data set forth in FIG. 4.

DESCRIPTION OF EXEMPLARY EMBODIMENT(S)

[0024] The disclosed bruxism detection system uses an accelerometer sensor that is adapted to be detachably mounted relative to an individual's chin to sense and collect motion data in three axes, i.e., the x-axis, the y-axis and the z-axis. The motion data is initially stored on local hardware associated with the module that contains the acceleration sensor. Since an individual's chin has the maximum mandibular acceleration during episodes of teeth grinding or teeth clenching, the best mounting position for accelerometer sensor is the chin.

[0025] With reference to FIG. 1 which schematically depicts an individual's face 10, a module 20 is detachably mounted with respect to chin 12 of the individual's face 10. Module 20 contains, inter alia, an acceleration sensor 22 that is adapted to sense motion in three axes, i.e., the x-axis, the y-axis and the z-axis. Mounting of module 20 relative to chin 12 is generally accomplished with an anti-allergy material, such as hospital-grade cloth tape with a protective gauze pad. Alternative mounting mechanisms may be employed, as will be readily apparent to persons skilled in the art, e.g., using strap(s), suitably sized bandage(s), adhesives or the like. This method of mounting of module 20 relative to an individual's chin advantageously permits reuse of the acceleration sensor 22 and electronics associated with module 20.

[0026] The acceleration sensor or accelerometer 22 is connected to and in communication with a sensor interface of the disclosed bruxism recording and processing system 24 operating on a local processor contained within module 20. Thus, with reference to FIGS. 1 and 2, the bruxism recording and processing system 24 contains or includes (i) a sensor interface 26, (ii) a processor with memory 28, (iii) storage 30, (iv) a communication interface 32, and (v) a power source 34. Storage 30 can be any non-volatile storage, such as flash memory. Communication interface 32 can be any wired interface, such as USB or Ethernet, or any wireless interface, such as Bluetooth, Bluetooth-Low-Energy(BLE), Wifi or Zigbee. The power source 34 can be a battery or a USB charger.

[0027] In use, an individual affixes module 20 to his/her chin and goes to sleep with accelerometer 22 (which is within module 20) positioned to sense movements of the chin 12. The bruxism recording and processing system 24 (associated with local hardware/processor also within module 20) is connected to and in communication with accelerometer 22. As soon as the bruxism recording and processing system 24 is turned on, the system locally acquires, processes and stores the accelerometer data.

[0028] More particularly, the bruxism recording and processing system 24 is advantageously programmed to minimize energy use, thereby extending battery life and ensuring sufficient power for overnight use of the disclosed bruxism detection system. Thus, in exemplary embodiments of the present disclosure, the bruxism recording and processing system 24 pre-processes motion-related measurements/data, but does not automatically activate/initiate its communication interface 32 to wirelessly transmit such information to a remote electronic device. The pre-processing of the bruxism recording and processing system 24 filters out data that is not relevant to bruxism detection, and typically transmits in burst transmissions relevant data that satisfies a threshold content level to a remote processing unit. To effectuate such transmission, the bruxism recording and processing system 24 and the remote/adjoint device engage in an electronic handshake to confirm/establish that applicable transmission parameters are satisfied.

[0029] When transmissions are to be undertaken, the bruxism recording and processing system 24 engages in communication with a remote/adjoint electronic device via the communication interface 32 which forms a data transfer link 36 with the remote/adjoint electronic device, e.g., computer 38. Computer 38 may be in close proximity to the individual undergoing the bruxism testing, e.g., a smartphone, laptop, desktop, etc., or may be remotely located and in communication with the bruxism recording and processing system 24 through conventional network-based communication systems, e.g., by way of a modem to processor(s) that operate remotely, e.g., in the “cloud”. Thus, the pre-processed accelerometer data is sent to the computer 38, wherever located and in whatever hardware form, via the data transfer link 36.

[0030] The adjoint computer 38 is generally programmed to run bruxism analysis software 40 according to the present disclosure. However, significant flexibility is provided by the present disclosure in that the disclosed bruxism analysis software may operate, in whole or in part, on the local processor positioned within module 20 as well. Thus, the required processing capability for bruxism analysis may be deployed in one or both hardware locations (i.e., local and adjoint processor), and may be operated in one or both hardware locations (i.e., local and adjoint processor), as desired by the user (or dictated by other factors, such as energy/power requirements and availability). Of note, the adjoint computer 38 is generally programmed to provide visualization/display and reporting functionalities for an end user interacting with the adjoint computer 38.

[0031] The bruxism analysis software 40 functions to analyze the processed accelerometer data, assess whether the accelerometer data corresponds to behaviors consistent with bruxism, and generate reports regarding the existence of bruxism. The bruxism analysis can be done in real-time mode or off-line mode. In the real-time mode, the accelerometer data is generally acquired, processed, stored and sent over the data transfer link by the local processor to the adjoint computer 38 where the bruxism analysis software 40 is running. In the off-line mode, the accelerometer data is acquired, processed and stored while the patient is sleeping. When the patient wakes up (or whenever convenient), the stored accelerometer data may be transferred over the data link 36 to the adjoint computer 38, at which time it is analyzed by the bruxism analysis software 40 running on the adjoint computer 38.

[0032] The system of the present disclosure detects teeth grinding, teeth tapping and teeth clenching by means of detecting, measuring and processing mandibular motion. The accelerometer sensor 22 included in the module 20 measures acceleration. When the mandibular motion starts, acceleration is recorded. Teeth grinding and teeth tapping both have rapid back-and-forth mandibular motion. So the accelerometer 22 senses and records a time-varying acceleration.

[0033] Clenching has a motion at the beginning and then at the end. So the accelerometer 22 senses and records acceleration change at the beginning of clenching and at the end of clenching.

[0034] The bruxism motion has “signature” motion-related characteristics which are characterized by parameters such as mean of the readings, variance of the readings, #of mean crossings, average time to cross the mean, frequency components of motion and shape of the waveform. By examining the acceleration readings and comparing them against known ‘signature’ of different types of bruxism conditions, the analysis software can detect bruxism.

[0035] During sleep there are other motions unrelated to bruxism. Turning the head, changing the sleeping position and swallowing are quite common. Sleep apnea and acid reflux are among the clinical conditions that can result in motion in the mandibular region, particularly at the chin. Each of these unrelated motions has its own ‘signature’. The analysis software of the present disclosure is programmed to recognize these ‘signatures’ and to reject the motions unrelated to bruxism.

[0036] FIG. 3 illustrates a flow chart of exemplary operation of bruxism analysis software 40 according to the present disclosure for detection of bruxism and rejection of unrelated motions, e.g., head turns/sleeping position change. The detection is based on deriving signature parameters of current time window and comparing it against known signature parameters of bruxism. The signature parameters include, but are not limited to, mean, variance, #of mean-crossings, average time to cross the mean, frequency components of motion, and shape of the waveform.

[0037] As shown in FIG. 3, bruxism analysis according to the present disclosure may advantageously include sorting the accelerometer data into separate columns/fields based on the axis of the measured motion, i.e., x-axis, y-axis and z-axis. A time window for data evaluation is defined, e.g., 5 seconds, and then determine whether motion is reflected in the time window. To the extent no motion is found in the window, the window is classified as “not interesting” and, depending on how many windows have been reviewed, another time window may be sampled. To the extent motion is found in the time window, the mean movement, frequency component of motion and variance movement values are compared to threshold values to determine whether the movement values correspond to parameters consistent with actions distinct from bruxism, e.g., head movement. To the extent the movement values correspond to parameters consistent with bruxism, the noted time window is classified as indicative of bruxism.

[0038] FIG. 4 shows exemplary accelerometer readings for simulated teeth grinding/teeth tapping and head turning.

[0039] FIG. 5 shows in tabular form how the bruxism analysis software 40 of the present disclosure processes the accelerometer readings set forth in FIG. 4 to identify/report bruxism events and reject head turn events. As described herein, the bruxism determination is made based on accelerometer data from a single accelerometer that is mounted with respect to an individual's chin using one or more of the following signature parameters, namely mean, variance and frequency components of motion.

[0040] As described herein, the disclosed bruxism detection system works better than EMG-based sensor systems for teeth grinding and teeth tapping because the motion detection signal level available from an accelerometer sensor is stronger than the electrical activity signal level available from EMG sensors. In addition, the disclosed bruxism detection system is better than an intra oral system because the accelerometer sensor is mounted on face and thus none of the issues of an intraoral system—safety, size, power consumption—arise. Still further, accelerometer sensors are less expensive than EMG sensors as they are used in high volume in many products, such as smart phones, gaming controllers, laptops, airbags, so the cost of our bruxism detection system is lower. Also, all the components of the device are reusable. Thus, the disclosed bruxism detection system offers many advantages and brings at-home, low-cost bruxism diagnosis to entire population without the need for health care professional intervention or involvement.

[0041] Although the present disclosure has been described with reference to exemplary embodiments and implementations, the present disclosure is not limited by or to such exemplary embodiments/implementations. Rather, the disclosed bruxism detection system is susceptible to various modifications, refinements and/or enhancements without departing from the spirit or scope of the present invention.