Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment

sensors

Article Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment

Jinxia Gao 1,2,*, Longjun Liu 3,*, Peng Gao 3, Yihuan Zheng 3, Wenxuan Hou 3 and Junhui Wang 3 1 Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an 710004, China 2 Department of Prothodontics, College of Stomatology, Xi’an Jiaotong University, Xi’an 710004, China 3 Institute of Artiﬁcial Intelligence and Robotics, College of Artiﬁcial Intelligence, Xi’an Jiaotong University, Xi’an 710049, China; [email protected] (P.G.); [email protected] (Y.Z.); [email protected] (W.H.); [email protected] (J.W.) * Correspondence: [email protected] (J.G.); [email protected] (L.L.); Tel.: +86-029-8721-8541 (J.G.); +86-029-8266-8672 (L.L.) Received: 2 November 2019; Accepted: 20 December 2019; Published: 22 December 2019

Abstract: Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study was to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device based on artificial intelligence (AI) algorithms for the diagnosis and treatment of bruxism. Stress sensors were integrated and embedded into a resin-based occlusion stabilization splint by using a layering technique (sandwich method). The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm was leveraged for occlusal force data processing and parameter configuration. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent splint. Experiment results showed reasonable parameter metrics for the sensors system and demonstrated the feasibility of the proposed scheme for bruxism treatment. The intelligent occlusion stabilization splint with a stress sensor system is a promising approach to bruxism diagnosis and treatment.

Keywords: bruxism; biofeedback treatment; occlusal splint; engineering; machine learning; artiﬁcial intelligence; data analysis; stress sensor system

1. Introduction Bruxism is one of chronic dental problems worldwide with multifactorial etiology and no golden standard for diagnosis and treatment [1]. The disorder is deﬁned as a repetitive jaw muscle activity characterized by clenching or grinding of teeth and/or by bracing or thrusting of the mandible [2]. Previous investigations found that the prevalence of sleep bruxism (SB) was about 50% in adults [3] whilst the prevalence of SB in children ranged from 3.5% to 40.6% [4]. Although signs and symptoms of bruxism vary, it is always supposed to be an etiological factor in causing damage to supporting structures of teeth, abnormal tooth wear, failure of dental restorations, and temporomandibular and musculoskeletal disorders [5]. Current diagnostic methods for bruxism mainly include self-report, clinical examination, electromyography (EMG), and polysomnography (PSG) [1]. According to an international consensus

Sensors 2020, 20, 89; doi:10.3390/s20010089 www.mdpi.com/journal/sensors Sensors 2020, 20, 89 2 of 15 discussion summary, the existing assessment of bruxism could also be classified into three main aspects: (1) noninstrumental approaches, (2) instrumental approaches, and (3) cut-off points grading [6]. Self-report and clinical examination are considered as noninstrumental approaches, which are also the primary choices in the clinical assessment of bruxism. However, their reliability and validity need further improvement [6]. EMG may provide good evidence of both sleep and awake bruxism, but there is also a risk of overestimating the number of true SB events [7]. PSG could be regarded as a reference standard for SB assessment; however, it is expensive and time-consuming [2]. To date, the reliability and validity of all the common techniques remain debatable, and consensus has yet to be established regarding the best method to diagnose bruxism. Therefore, exploring some new methods of bruxism diagnosis and management is a necessary and meaningful research topic. The use of biofeedback technologies (electrical, auditory, vibratory stimulus, etc.) as behavioral techniques of bruxism diagnosis and treatment has been considered a promising approach in both clinical and scientific fields in recent years [8]. Various biofeedback modalities have been reported in previous papers, and most of these are based on EMG recordings, except two studies that described force-based devices [8,9]. An intra-splint force detector (ISFD) for SB force detection was described in 2003 [10]. A detailed description of this SB inhibition system has been presented in a recently published paper [11]. This inhibition system consisted of ISFD, vibration, and control units. The ISFD used a modified occlusal stabilization splint (OSS) as a carrier, which was fabricated by a heat-curing resin. A 100 µm thick deformation-sensitive piezoresistive film was embedded in an oral appliance for occlusal force signal detection. Another kind of mini wireless biofeedback device for SB diagnosis and treatment was reported, in which a stress sensor, a button cell, and a mini monitoring circuit were embedded in the special splint and a watch style device was designed for monitoring and feedback [12]. Pilot studies found that biofeedback therapy based on occlusal force could monitor occlusal changes effectively [10–12]. However, the occlusal force-based detection system was mainly used in SB, and the sensors were packed only in the canines or premolars regions. The existing limitations in previous studies on this topic gave rise to several questions. According to the current concept, an OSS was characterized as conservative, reversible, and nonspecific in bruxism diagnosis and treatment [13]. An OSS had a full coverage of the maxilla or mandibular dental arch and has been proposed to balance the force distribution to the entire masticatory system [14]. Moreover, an OSS could provide an ideal occlusion and reduce abnormal muscle activity, and it can be used to protect teeth and support structures of bruxers [15]. In this regard, an OSS with a full coverage of dentition in combination with multi-site stress sensors can show better detection and protection capabilities of both sleep and awake bruxism. In recent years, artificial intelligence (AI) algorithms (e.g., deep learning algorithms) have developed rapidly along with continuous integration with the medical field, including medical imaging, health management, early detection of diseases, biotherapy, and precision medicine. Hannun et al. [16] used AI algorithms to diagnose heart rate irregularities based on arbitrary-length, single-conductance electrocardiogram (ECG) time series data. The study used a Zio heart rate monitor to collect ECG data and perform training and detection via an AI algorithm. Ravizza et al. [17] used AI algorithms to leverage clinical data to predict early risk of chronic kidney disease in diabetic patients. Their AI algorithms use clinical data with a complete data volume and consistent consistency to predict a diabetes-related chronic kidney disease model better than traditional clinical research data models. AI technology has also been extensively studied in the field of medical imaging, but the research on real-time, accurate and intelligent detection of oral occlusal stress has not been reported in detail [8]. With the progress of science and technology and the emergence of medical and engineering strategies, it is now possible to design and create a portable, intelligent and precise force-based bruxism feedback detection system. In this study, a real-time, quantitative, intelligent biofeedback system to detect occlusal force based on an AI algorithm is proposed. The purpose of this study is to test the hypothesis that this system can analyze occlusal contact precisely and detect occlusal force change quantitatively. At the Sensors 2020, 20, 89 3 of 15 Sensors 2019, xx, x 3 of 15 same time, this work also aims to assist doctors in monitoring bruxism by a mobilemobile application and assessment in real time for necessarynecessary interventions.interventions. This study employs an interdisciplinary interdisciplinary approachapproach that integrates integrates dentistry and engineering engineering concepts. Its Its main main contributions study study can can be be summarized into into three three aspects: (1) (1) the the propos proposalal of a design for for full-dentition, full-dentition, stress-sensitive stress-sensitive occlusion occlusion detection detection and and stress stress data data acquisition, acquisition, filtering, filtering, and denoising;and denoising; (2) the (2) calibration the calibration of occlusion of occlusion data based data onbased a machine on a machine learning learning algorithm algorithm and the studyand the of astudy real-time of a quantitative real-time quantitative biofeedback biofeedback method for methodocclusion for detection; occlusion (3) detection; the analysis (3) of the occlusal analysis stress of characteristicsocclusal stress andcharacteristics disease status and prediction.disease status The prediction. overall key The points overall of ourkey study points are of alsoour study shown are in Figurealso shown1. in Figure 1.

Figure 1. Overall illustration of the interdisciplinary research and three main contributions of this study. Figure 1. Overall illustration of the interdisciplinary research and three main contributions of this study. The rest of this paper is organized as follows: in Section2, materials and methods are presented in detail. The experimental results are described in Section3. In Section4, the sensor system and future The rest of this paper is organized as follows: in Section 2, materials and methods are presented research are discussed. At last, we conclude our study in Section5. in detail. The experimental results are described in Section 3. In Section 4, the sensor system and 2.future Materials research and are Methods discussed. At last, we conclude our study in Section 5.

2.1.2. Mate Overallrials Treatment and Methods Scheme: Biofeedback System Scheme

2.1. OverallGiven Treatment that biofeedback Scheme: trainingBiofeedback is growingSystem Scheme increasingly popular in recent years [18], a sensor device in this concept was designed to record bruxism behaviors. An occlusal force produced during teethGiven bracing that and biofeedback/or grinding training events is as growing input signals increasingly for bruxism popular was in recent detected years and [18], transmitted a sensor edeviceﬀectively in this by concept embedded was chips. designed A control to record chip bruxism then converted behaviors. the An inputted occlusal occlusal force pro forceduced signals during into digitalteeth bracing signals byand/or using grinding a digital-to-analog events as converterinput signals and transmittedfor bruxism data was to detected a Bluetooth and sending transmitted chip. Then,effectively the Bluetooth by embedded receiving chips. segment A control of achip mobile then phone convert receiveded the inputted the signals occlusal based force on the signals Bluetooth into digital signals by using a digital-to-analog converter and transmitted data to a Bluetooth sending chip. Then, the Bluetooth receiving segment of a mobile phone received the signals based on the Bluetooth protocol. Wireless Bluetooth transmission was mainly used to send the occlusal force data

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Sensors 2019, xx, x 4 of 15 protocol. Wireless Bluetooth transmission was mainly used to send the occlusal force data processed processby a microprogrammeded by a microprogrammed control unit control (MCU) unit to (MCU) a server to terminal. a server te Abnormalrminal. Abnormal and frequent and frequent occlusal occlusalforce signals force were signals converted were converted to vibratory to vibratory stimulus signalsstimulus through signals a smartthrough watch, a smart and watch the bruxer, and wasthe bruxerreminded was to reminded prompt the to immediate prompt the relaxation immediate of the relaxation jaw muscles. of the The jaw biofeedback muscles. The system biofeedback aimed to systemgenerate aim a learneded to generate response a andlearned ﬁnally response terminate and the finally bruxism terminate behavior the without bruxism wearing behavior a feedbackwithout wearingsystem. Ina addition,feedback occlusion system. In time, addition, occlusal occlusion force distribution, time, occlusal and occlusal force distribution, contact could and be displayed occlusal contacton the smartphone, could be displayed thus providing on the real-time smartpho trackingne, thus for self-care.providing Moreover, real-time clinicians tracking can for also self- sharecare. Moreand analyzeover, clinicians data for real-time, can also remoteshare and monitoring analyze in data clinical for real practice.-time, The remote protocol monitoring of the biofeedback in clinical practice.system is The presented protocol in of Figure the biofeedback2. system is presented in Figure 2.

Figure 2. OverallOverall treatment treatment scheme of the biofeedback system we proposed for bruxism bruxism.. 2.2. Full-Dentition Stress Sensors Integration and Imbedding Methodology—"Sandwich Method” 2.2. Full-Dentition Stress Sensors Integration and Imbedding Methodology—"Sandwich Method” There are many challenges for stress sensor devices integration and imbedding in the mouth. There are many challenges for stress sensor devices integration and imbedding in the mouth. How to successfully integrate a sensor chip into a pad and operate it normally is a key problem. How to successfully integrate a sensor chip into a pad and operate it normally is a key problem. A A full-dentition occlusion detector needs to be worn on a patient’s upper or lower jaw dentition, so the full-dentition occlusion detector needs to be worn on a patient's upper or lower jaw dentition, so the embedded chip must be as small as possible to achieve actual embedded installation requirements; embedded chip must be as small as possible to achieve actual embedded installation requirements; the embedded chip should be isolated from the patient’s mouth to achieve a sealed package. the embedded chip should be isolated from the patient's mouth to achieve a sealed package. 2.2.1. Early-Stage Preparation 2.2.1. Early-Stage Preparation An extensive clinical examination was conducted to assess occlusal features, and a silicone bite registrationAn extensive material clinical (DMG,examination O-Bite, was Hamburg, conducted Germany) to assess was occlusal used tofeatures, measure and occlusal a silicone contact bite registrareas ination the material maximum (DMG, intercuspal O-Bite, positionHamburg [19, Germany)]. Full-arch was impression used to measure was taken occlusal by using contact a silicone areas inimpression the maximum material intercuspal (Silagum position Putty/Silagum [19]. Full Light,-arch DMG, impression Hamburg, was Germany). taken by using Gypsum a silicone casts (Heraeus,impression water material/powder: (Silagum 22 mL Putty/Silagum/100 g) were obtained Light, DMG, from Hamburg silicone impression, Germany). and Gypsum mounted casts on (Heraeus,an articulator water (Stratos300,/powder: 22 Ivoclar mL/100 Vivadent, g) were Liechtenstein)obtained from combinedsilicone impression with occlusal and registrationmounted on and an articulatorface-bow transfer. (Stratos300, Ivoclar Vivadent, Liechtenstein) combined with occlusal registration and face-bow transfer. 2.2.2. Sandwich Method 2.2.2. Sandwich Method The OSS had a coverage of an approximately 2 mm width on both the buccal and lingual surfaces to provideThe OSS friction had retention. a coverage The of desiredan approximately border was initially2 mm width marked on on both the the maxillary buccal working and lingual cast withsurfaces a red to pencil. provide Then, friction an about retention. 1 mm The thick desired light-curing border transparent was initially resin marked (3M, Z350 on the XT, maxillary St. Paul, workingMN, USA) cast was with evenly a red applied pencil. to Then, cover an the about buccal, 1 mm occlusal, thickand light lingual-curing surfaces transparent of the resin upper (3M, full Z350 arch. AllXT, theSt. Paul, residual MN resin, , USA from) was the evenly working appl castied was to removedcover the accordingbuccal, occlusal, to the outlined and lingual ﬁnish surfaces line by of using the uppera laboratory full arch. knife, All afterthe residual which stress-sensitive resin from the chipsworking and cast control was chipsremoved (approximately according to 0.3 the mm outlined thick) finish line by using a laboratory knife, after which stress-sensitive chips and control chips (approximately 0.3 mm thick) were embedded in the marked sites of the OSS. Another layer of the transparent resin (about 1 mm thick) was evenly applied to cover the chips and the first layer of resin before light-curing (3M, Elipar™ LED Curing Light, Light curing unit power: 1200 mW/cm², Light

Sensors 2020, 20, 89 5 of 15 were embedded in the marked sites of the OSS. Another layer of the transparent resin (about 1 mm thick) was evenly applied to cover the chips and the ﬁrst layer of resin before light-curing (3M, Elipar™ LED Curing Light, Light curing unit power: 1200 mW/cm2, Light curing time: 20 s, St. Paul, MN, USA). Finally, the occlusal adjustment of the ideal contact areas was conducted, and the device was polished. When considering an OSS for diagnosis and treatment of bruxism, occlusal adjustment plays an important role, and the ideal occlusal contacts should achieve the following goals. In maximal intercuspation, mandibular posterior teeth must come into contact with a device providing even force and contact slightly more heavily than anterior teeth. Posterior teeth should be separated in protrusive and lateral movements [20]. During the protrusive movement, mandibular anterior teeth must come into contact with the device providing even force. In the lateral movement, only mandibular canines should exhibit laterotrusive contact with the device [20]. As the entire force of a protrusion on any single tooth may cause occlusal trauma, any interferences must be eliminated during occlusal adjustment.

2.3. Sensors Components and System Design The sensor system mainly consisted of three parts: a pressure signal acquisition module, a main control module, and a server terminal.

The pressure signal acquisition module included a set of sensitive piezoresistive chips (HuaLanHai, • BHF350-3AA, GuangZhou, China). The sensor chip can output corresponding voltage changes with dental pressure variation. The main control module was mainly used for receiving occlusal force signals collected by a • stress sensor, while also processing, storing and packing the collected data. In this system, a ultralow-power MCU (Nordic Semiconductor, NRF52832, Oslo, Norway) was used as the main controller to control the acquisition of multiple stress signals, convert analog signals into digital signals through a multichannel analog-to-digital converter (ADC) (Nordic Semiconductor, SAADC, Oslo, Norway) conversion module and then send the processed data to a server for further analysis. The power supply of the control module was accomplished by using a button battery (Panasonic Semiconductor, Panasonic CR2032, Celebes, Indonesia), which can work continuously for at least six months. The MCU should turn oﬀ automatically, whenever the required operating voltage could not be supplied by the battery. The wireless transceiver module (ST Microelectronics, STM32, Geneva, Switzerland) was mainly • used to send the occlusal force data processed by the MCU to a server terminal. Bluetooth wireless transmission has the advantage of low power consumption and signiﬁcant reduction of power consumption of a sensor system, which can be used to update occlusal data in real time. The server terminal parsed the data package according to the Bluetooth protocol and stored the parsed data in the server for further data analyses. The server terminal module utilized a common server (Hewlett-Packard Development Company, • L.P, HP Z840, Beijing, China) with a Graphics Processing Unit (GPU) coprocessor (NVIDIA, TiTan XP, Santa Clara, CA, USA) for machine learning algorithm training. The server platform ran application software to receive data from the sensor system and showed the curves generated based on sensor-captured pressure.

2.3.1. Sensor System Design Consideration A whole sensor system integration solution was designed from scratch, taking care of both hardware and ﬁrmware designs. In order to achieve real-time, dynamic monitoring of bite forces, an occlusal splint must be placed inside the oral cavity, and the comfort of a patient’s wearing needs to be taken into consideration to be compatible with human tooth structures. Therefore, the entire system must have the characteristics of microsize, low power consumption, and stable performance. The main control module is the core and foundation of the entire detection system, which determines whether the entire system can detect and transmit data stably and accurately Sensors 2020, 20, 89 6 of 15

SensorsThe 2019 server, xx, x mainly used a machine learning algorithm to further process bite force data6 of and 15 combined digital signal processing (signal ﬁltering and smoothing) and statistical analysis to analyze signals.analyze Thesign statisticalals. The statistical analysis mainlyanalysis included mainly include ﬁve aspects:d five (1) aspects: average (1) value average (AVGx valuei) of ( biteAVG forcesxi) of inbite a force monitorings in a monitori duration,ng e.g.,duration, 24 hours e.g., or24 onehours week, or one (2) week, maximum (2) maximum (MAXxi) ( andMAX minimalxi) and minimal (MINxi) amplitude(MINxi) amplitude values of value bite forces,s of bite (3) force standards, (3) deviationstandard (deviationσxi) value ( ofσx bitei) value forces of duringbite force a monitorings during a period,monitoring (4) variance period, value(4) variance (ψxi) of value bite forces (ψxi) inof a bite monitoring forces in duration, a monitoring (5) covariance duration, value (5) covariance (ϕxi) of all bitevalue forces (φxi) during of all bite a monitoring forces during period. a monitoring According toperiod. these analyzedAccording results, to these the algorithmsanalyzed results, can give the a conclusionalgorithms whethercan give thea con subjectclusion has whether bruxism the or occlusion subject h problemsas bruxism (e.g., or occlusion the type, theproblem severity,s (e.g., and the trendtype, the of the severity illness)., and the trend of the illness).

2.3.2. Workflow Workﬂow of an IntraoralIntraoral Sensor Pressure DetectionDetection SystemSystem Initially, thethe sensorsensor systemsystem isis inin aa sleepsleep statestate toto reducereduce systemsystem powerpower consumption.consumption. When the patient experiences occlusal contact and and produces produces a a resultant resultant force force to to the the stress stress sensor, sensor, thisthis causes the voltage voltage value value of of the the sensor sensor output output to tochange change,, which which is transmitted is transmitted to the to AD the converter AD converter for data for dataconversion. conversion. In order In to order prevent to preventfalse positives false positivesand record and the record resultant the force resultant start forcetime accurately, start time accurately,the occlusionthe detection occlusion systemdetection stores system an initial stores threshold an initial set threshold by the dentist set by in thethe dentistfirst appointment. in the ﬁrst appointment.During biofeedback During testing, biofeedback the dentist testing, can remotely the dentist reset can the remotely threshold reset base thed on threshold feedback based analysis on feedbackdata. When analysis the digital data. signalWhen thecollected digital by signal the ADcollected converter by the is AD greater converter than isthe greater threshold, than the threshold,abnormal occlusionthe abnormal eventocclusion is considered event to is occur. considered The MCU to occur. is awakened The MCU, and is the awakened, multichannel and AD the multichannelconversion-processing AD conversion-processing port detection mod portule detection is implemented, module is which implemented, mainly includes which mainly the following includes theoperation following steps, operation as shown steps, in Figure as shown 3. in Figure3.

Figure 3. FFlowlow chart of an i intraoralntraoral s sensorensor p pressureressure detectiondetection system.system.

2.4. Data Collection and Analysis 2.4. Data Collection and Analysis 2.4.1. Data Denoising 2.4.1. Data Denoising In this study, a data signal is denoised by wavelet transform, that is, a noise- and stress-induced In this study, a data signal is denoised by wavelet transform, that is, a noise- and stress-induced signal is transformed into the wavelet domain, and then the wavelet transform of the signal is separated signal is transformed into the wavelet domain, and then the wavelet transform of the signal is from the wavelet transform of the noise; finally, the noise transform coefficient is discarded, along separated from the wavelet transform of the noise; finally, the noise transform coefficient is with the remaining transform coefficients. The inverse transform is used to obtain a denoising signal discarded, along with the remaining transform coefficients. The inverse transform is used to obtain a (relevant parameters are obtained by medical statistics experiments). First, the wavelet transform is denoising signal (relevant parameters are obtained by medical statistics experiments). First, the wavelet transform is used to transform an occlusion stress data signal into the wavelet domain, after which the wavelet transform of the signal is separated from the wavelet transform of the noise.

Sensors 2020, 20, 89 7 of 15 usedSensors to 2019 transform, xx, x an occlusion stress data signal into the wavelet domain, after which the wavelet7 of 15 transform of the signal is separated from the wavelet transform of the noise. Finally, the noise transform coeFinally,ﬃcients the are noise discarded, transform and coefficients the residual are transform discarded, coe ﬃandcients the areresidual inversely transform transformed coefficients to obtain are theinversely denoising trans signal.formed to obtain the denoising signal. InIn many many wavelet wavelet transform transform denoising denoising methods, methods, the the translation translation invariant invariant wavelet wavelet denoising denoising methodmethod waswas selected selected to to denoise denoise the the number. number. In particular, In particular, if the if original the original signal signal obtained obtained is X(t) , is where X(t), where 0 ≤ t ≤ n, and the signal Sh(t) = X(t + h) is obtained by the time-domain translation of a positive 0 t n, and the signal Sh(t) = X(t + h) is obtained by the time-domain translation of a positive integer), integer),≤ ≤ then under the condition that Sh is reversible, the translation invariant wavelet denoising then under the condition that Sh is reversible, the translation invariant wavelet denoising method of nmethod-cycle translation of n-cycle cantranslation be expressed can be as: expressed as: Tx（ ;(S) ）=Ave(T(S)) hhHhHhnn T(x; (Sh)h H ) = Aveh Hn (T(Sh)) n ∈ ∈ wherewhereH Hnn == {h:{h:0 ≤ t ≤ nn},}, Ave Ave means the average value, and T(ST(Sh) conductsconducts thethe denosingdenosing processprocess forfor ≤ ≤ h signalsignalS S withwith thethe DonhoDonho thresholdthreshold method.method.

2.4.2.2.4.2. DataData AnalysisAnalysis withwith IntelligentIntelligent MachineMachine LearningLearning AlgorithmsAlgorithms BasedBased on on the the collected collected occlusal occlusal force force data, ﬁvedata, important five important characteristic charac parametersteristic parameters in the occlusion in the adjustmentocclusion adjustment per unit time per areunit extracted time are asextracted AI algorithm as AI inputs:algorithm magnitude inputs: magnitude of the average of the occlusion average force,occlusion duration force, ofduration the average of the occlusal average force,occlusal average force, contactaverage area contact of occlusal area of occlusal forces, contactforces, contact points ofpoints occlusal of occlusal forces, force and feedbacks, and feedback of a patient of a patient pain perception pain perception level. Thelevel. bite The force bite andforce the and force the timeforce aretime obtained are obtained by a sensorby a sensor detector. detector. The patientThe patient feedback feedback sensation sensation level level refers refers to the to painthe pain during during the occlusionthe occlusion process process that that the the patient patient feeds feeds to to the the intraoral intraoral detector detector through through the the biofeedback biofeedback system.system. GradeGrade isis initiallyinitially deﬁneddefined byby aa doctordoctor andand isis determineddetermined byby multiplemultiple setssets ofof experimentsexperiments in this project. TheThe contactcontactarea area andand the the number number of of occlusal occlusal contact contact points points areare obtained obtained byby conventional conventional occlusion occlusion paperpaper andand thethe T-ScanT-Scan method.method. Then,Then, aa one-dimensionalone-dimensional deepdeep convolutionalconvolutional neuralneural networknetwork isis usedused forfor intelligentintelligent analysisanalysis andand auxiliary diagnosis, as as shown shown in in Figure Figure 4.

FigureFigure 4.4. NeuralNeural networknetwork modelmodel designeddesigned inin thethe presentpresent study.study.

The algorithm uses the neural network of residual structure to improve the test accuracy of the The algorithm uses the neural network of residual structure to improve the test accuracy of the network, where pwconv1 and pwconv2 represent one-dimensional pointwise convolutions, dwconv network, where pwconv1 and pwconv2 represent one-dimensional pointwise convolutions, dwconv represents a one-dimensional depth-wise convolution, BN represents batch normalization and Relu6 represents a one-dimensional depth-wise convolution, BN represents batch normalization and Relu6 represents a nonlinear activation function. The output of the neural network is the level of occlusion represents a nonlinear activation function. The output of the neural network is the level of occlusion adjustment, as shown in Figure4. Each level has a special altitude for occlusion adjustment, e.g., adjustment, as shown in Figure 4. Each level has a special altitude for occlusion adjustment, e.g., each level represents an operation that rises or decreases by 0.5 mm. The initial value of the level each level represents an operation that rises or decreases by 0.5 mm. The initial value of the level of of occlusion adjustment is set by a dentist. Then, the neural network is trained based on the initial occlusion adjustment is set by a dentist. Then, the neural network is trained based on the initial experimental value and the training sample. According to the abundant input data for training the experimental value and the training sample. According to the abundant input data for training the sample (about larger than 50 thousands training samples), the neural network can learn to output sample (about larger than 50 thousands training samples), the neural network can learn to output a a proper level of occlusion adjustment based on the unknown input of the neural network, which proper level of occlusion adjustment based on the unknown input of the neural network, which can can guide the occlusion adjustment for the dentist. In this project, the occlusion adjustment was guide the occlusion adjustment for the dentist. In this project, the occlusion adjustment was finally finally determined by multiple sets of experiments. Prior to the neural network algorithm training, determined by multiple sets of experiments. Prior to the neural network algorithm training, the the clinician must first calibrate the degree and level of the input data and the corresponding occlusion clinician must first calibrate the degree and level of the input data and the corresponding occlusion adjustment, so that the algorithm model first obtains the network parameters on the calibrated data set adjustment, so that the algorithm model first obtains the network parameters on the calibrated data set and then analyzes the actual data based on the learned model parameters. As for the test, the neural network training process used a momentum gradient descent method with a coefficient of 0.9 to train the network, and the batch size was set to 128. In addition, the migration learning method

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Sensorsand then 2019, analyzesxx, x the actual data based on the learned model parameters. As for the test, the8 neural of 15 network training process used a momentum gradient descent method with a coefficient of 0.9 to train wasthe network,used to pretrainand the batch the network size was withset to external 128. In addition, data, beforethe migration the traininglearning bega methodn to obtain was used better to initializationpretrain the networkweights. with external data, before the training began to obtain better initialization weights. ThroughThrough training training and testing experimentsexperiments toto improveimprove the the accuracy accuracy of of the the algorithm, algorithm, 80 80 sets sets of theof thesample sample calibration calibration training training data data were were setin set the in specific the specific implementation, implementation, and 20 and sets 20 of sets the sample of the sampleinference inference data were data tested. were The tested. neural The network neural trainingnetwork data training were data collected were from collected different from sick different periods siofck the periods same of patient, the same thus patient, forming thus a data forming set. The a data occlusion set. The behavior occlusion and behavior degree level and identificationdegree level identificationwas performed was through performed the stress through data the of thestress patient data perof the unit patient time obtained per unit in time real obtained time to assist in real the timedoctor to assist in diagnosis the doctor and in treatment. diagnosis and treatment.

2.5.2.5. D Diagnosisiagnosis and and Treatment Treatment Scheme Scheme TheThe initial initial occlusion occlusion detection detection algorithm algorithm parameters parameters and and thresholds thresholds are are set set by by analyzing analyzing the the occlusionocclusion status status of of different diﬀerent patients patients with with the the initial initial diagnosis. diagnosis. After After the the patients patients wear wear the the monitor, monitor, theirtheir real real-time-time occlusion occlusion str stressess data areare collectedcollected basedbased on on the the set set threshold, threshold, thus thus providing providing a basisa basis for forgenerating generating biofeedback biofeedback events. events. TheThe occlusion occlusion data data are are stored stored in in a a mobile mobile application application via via the the Bluetooth Bluetooth transmission transmission technology. technology. BasedBased on the the occlusion occlusion stress stress determination determination algorithm algorithm integrated integrated in the in mobile the mobile application, application the patients’, the patients’real-time real occlusion-time occlusion changes arechanges analyzed, are analyzed, and the patients and the are patients sent a message are sent according a message to according the algorithm to theresult. algorithm The patients result. The are remindedpatients are to reminded check the currentto check occlusion the current stress occlusion condition, stress and condition, the current and painthe currlevelsent are pain recorded levels are on the recorded mobile on application. the mobile The application mobile application. The mobile records application the patients’ records input, the patients’and the collectedinput, and data the arecollected then uploadeddata are then to the uploaded server for to the analysis. server for analysis. OnOn the the server, the the patient’s patient's occlusion occlusion analysis analysis results results are are obtained obtained by an by AI an algorithm. AI algorithm. The doctor The doctorcan view can theview occlusion the occlusion stress stress data atdata any at timeany time through through the doctor’sthe doctor's mobile mobile application. application Based. Based on onthe the analysis analysis results, results, the the parameter parameter threshold threshold value value during during the the biofeedback biofeedback treatment treatment process process can can be beadjusted adjusted remotely remotely in real in realtime time to achieve to ach theieve purpose the purpose of real-time of real quantitative-time quantitative biofeedback biofeedback detection detectionand recording, and recording, as shown as in shown Figure 5in. Figure 5.

Patients requiring occlusal adjustment

Dentist diagnoses

Biofeedback Provider proposes the monitoring reminds Intraoral stress design method of the patients to view and acquisition device stress monitor initially record pain-related data in real time Data collected through Bluetooth

Patient-side mobile application

Upload the collected data through WiFi Parameter Server adjustment (Analysis of artificial intelligence algorithms、 Medical statistical analysis algorithms, Database) Doctor processes and predicts data of occlusal Download the stress of patients based processed data on algorithm through the network Doctors check occlusal Doctor-side mobile Stress data and improve application/ treatment at any time computer

FigureFigure 5. 5. OOverallverall diagnosis diagnosis and and treatment treatment scheme scheme for for bruxism bruxism..

3. Results In this section, we present the prototype of our sensor system. We show the experiment results with the sandwich method and the electrical measurements of the sensors; we also demonstrate the

Sensors 2020, 20, 89 9 of 15

3. Results

SensorsIn 2019 this,, xx section,,, xx we present the prototype of our sensor system. We show the experiment results9 of 15 with the sandwich method and the electrical measurements of the sensors; we also demonstrate the feasibilityfeasibility of of the the sensors sensors scheme scheme we we proposed. proposed. In In future experiments, our sensor system will be be embeddedembedded inin patientspatients intraorally.

3.1.3.1. Sandwich Method VeriﬁcationVerification WeWe completedcompleted thethe sensorssensors embeddingembedding in anan OSSOSS basedbased onon ourour sandwichsandwich method,method, asas shownshown inin FigureFigure6 6.. FigureFigure6 a6a presents presents the the stress-sensitive stress-sensitive chips chips and and the the control control chips. chips. Six Six sensors sensors were were used used in in thisthis paperpaper forfor datadata acquisitionacquisition andand bruxismbruxism diagnosis.diagnosis. The data collected from the six sensors werewere voltagevoltage signals,signals, as shownshown in FigureFigure7 7.. InIn thisthis paper,paper, wewe onlyonly presentpresent thethe datadata graphgraph ofof one one sensor sensor shownshown in in Figure Figure7. 7 Figure. Figure6b presents6b presents the preliminary the preliminary production production of a traditional of a traditional OSS, i.e., OSS, the occlusal i.e., the contactsocclusal ofcontacts anterior of guidance, anterior guidance, canine protection, canine protection, and maximal and intercuspation maximal intercuspation after occlusal after adjustment. occlusal Figureadjustment.6c shows Figure the sensors6c shows were the packaged sensors in were the OSS. packaged We further in the studied OSS. the We reasonable further stud parameteried the settingreasonable for the parameter sandwich setting method, for the as shown sandwich in Table method,1. as shown in Table 1.

(a) (b) (c)

FigureFigure 6.6. ((a)) Stress-sensitiveStress-sensitive chipschips andand controlcontrol chips;chips; ((bb)) occlusalocclusal contactscontacts ofof anan occlusalocclusal stabilizationstabilization splintsplint (OSS);(OSS); ((cc)) sensorssensors packagedpackaged inin thethe OSS.OSS.

Little variation

Dramatic variation

Receptive zone: Received data: 3571Byte,, Receive speed:18Byte//sec.. Figure 7.7. Data a acquisitioncquisition from stress sensorsensor system.system. Table 1. Reasonable parameters for the sandwich method. Table 1. Reasonable parameters for the sandwich method. Item Parameter Item Parameter Each layer of the light-cured resin 1 mm ThicknessEach layer of theof the piezoresistive-ﬁlm light-cured resin sensor 0.3 mm1 mm Thickness of theLight-curing piezoresistive time-film sensor 5 min0.3 mm LightThickness-curing of thetime OSS 2 mm5 min Thickness of the OSS 2 mm 3.2. Sensor Prototype System Building 3.2. Sensor Prototype System Building We conducted a sensor prototype system from scratch. Figure8a shows the pressure signal acquisitionWe conduct module,ed whicha sensor included prototype a group system of piezoresistive-ﬁlm from scratch. Figure stress 8 sensorsa shows and the a pressure microcontroller. signal acquisition module, which included a group of piezoresistive-film stress sensors and a microcontroller. The module was embedded in the OSS with our sandwich method. The current size of the module was about 10.5 mm × 8.2 mm. We further customized a circuit board and removed several test pads, when it was embedded in the OSS. In this way, the size was reduced to 6 mm × 6 mm. The button battery was stacked up on the back of the circuit board. Figure 8b shows

Sensors 2020, 20, 89 10 of 15

The module was embedded in the OSS with our sandwich method. The current size of the module was about 10.5 mm 8.2 mm. We further customized a circuit board and removed several test pads, × when it was embedded in the OSS. In this way, the size was reduced to 6 mm 6 mm. The button × batterySensors 2019 was, xx stacked, x up on the back of the circuit board. Figure8b shows how the microcontroller10 of 15 circuit board was placed in a test board. Based on the test board, we were able to fully verify the how the microcontroller circuit board was placed in a test board. Based on the test board, we were functionable to offully the verify pressure the signal function acquisition of the pressure module. signal Figure acquisition8c shows module. the electric Figure schematic 8c shows diagram the ofelectric the pressure schematic signal diagram acquisition of the module. pressure Figure signal8d presents acquisition our moprototypedule. Figure system 8d connected presents to our the serverprototype for real-time system connected debugging to withthe server a serial for port. real-time debugging with a serial port.

Stress piezoresistive film sensor NRF52832 Micro Controller

(a) (b)

FigureFigure 8. 8.( a()a Pressure) Pressure signal signal acquisition acquisition module; module (; b()b test) test board board for for a microcontrollera microcontroller circuit circuit board board (the microcontroller(the microcontroller circuit circuit board board is indicated is indicated by aby red a red rectangle); rectangle) (c); electric(c) electric schematic schematic diagram diagram of of the pressurethe pressure signal signal acquisition acquisition module; module (d); prototype(d) prototype system system connected connected with with the the server. server.

3.3.3.3. Experimental Experimental DataData fromfrom thethe SensorSensor Prototype S Systemystem WeWe collected collected the the data data from from stress stress signals signal ands dataandtransmission data transmission and receiving and receiving processes processes separately. Figureseparately.7 shows Figure the data7 shows collected the data from collected the pressure from signalthe pressure acquisition signal module, acquisiti whichon module, were which also the inputwere signals also the for input the subsequent signals for modulethe subsequent of statistical module analysis of statisti and neuralcal analysis network and analysis. neural networkThe input voltagesanalysis. in The the input experiment voltage weres in the shown, experiment and as were can be shown, seen, thereand as are can two be typicalseen, there areas: are littletwo typical variation andareas: dramatic little variationvariation, and referring dramatic to small vari occlusalation, referring forces and to smalllarge occlusal occlusal forces, forces respectively,and large occlusal obtained fromforce thes, respectively, piezoresistive-film obtained stress from sensor. the piezoresistive Figure7 also-film demonstrates stress sensor. the Figure voltage 7 also variation demonstrates of strain gauges,the voltage which variation indirectly of reflected strain gauges, the resistance which change indirectly of strain reflected gauges. the From resistance such data,change we of can strain obtain thegauges. severity From degree such of data, a patient’s we can bruxism obtain the episode. severity Although degree of several a patient’s noise bru dataxism from episode. the pressure Although signal acquisitionseveral noise module data can from be found, the pressure our denoising signal algorithm acquisition can module effectively can remove be found, such our abnormal denoising noise data.algorithm The feedback can effectively signals remove in this paper such abnormal are the level noise of occlusiondata. The adjustmentfeedback signals from thein this neural paper network, are the level of occlusion adjustment from the neural network, which can be input into the biofeedback which can be input into the biofeedback system for bruxism diagnosis. Both the input signals (Figure7) system for bruxism diagnosis. Both the input signals (Figure 7) and the feedback signals can be and the feedback signals can be collected at any time, which can be demonstrated in multiple cycles. collected at any time, which can be demonstrated in multiple cycles. We further tested the data transmission from the phone to the server based on the Bluetooth protocol. The data were coded for transmission based on the transferred equation: coded data = Sample_Voltage × 3.6/1024. The coded data were sent to the server and stored on the

Sensors 2020, 20, 89 11 of 15

We further tested the data transmission from the phone to the server based on the Bluetooth protocol. The data were coded for transmission based on the transferred equation: coded data = Sample_VoltageSensors 2019, xx, x 3.6/1024. The coded data were sent to the server and stored on the server, as shown11 of 15 × in Figure9a. The data can be visualized with various curves by a mobile application, so that a doctor server, as shown in Figure 9a. The data can be visualized with various curves by a mobile can check a patient’s condition at any time, as shown in Figure9b. application, so that a doctor can check a patient's condition at any time, as shown in Figure 9b.

(a) (b)

Figure 9. 9. (a) Data stored inin aa tabletable onon thethe server; server; ( b()b) data data visualized visualized with with various various curves curve ins in a mobilea mobile application. application.

3.4.3.4. Experiment of the ConfigurationConfiguration of a Machine Learning AlgorithmAlgorithm To intelligently intelligently analyze analyze the the bruxism bruxis datam data collected collected from from the sensor the sensor system, system, we leveraged we leverage a machined a learningmachine algorithm. learning algorithm. As is well-known, As is well various-known, machine various learning machine models learning can be models used to can analyze be used data. to Inanalyze this paper, data. weIn this tested paper, many we current tested machine many current learning machine algorithms learning and algorithms found that theand neural found networkthat the isneural a good network choice because is a good it features choice very because efficient it features and simple very data efficient analysis and for simple processing data bruxism analysis data. for Finally,processing we found bruxism a useful data. and Finally, feasible we neural found network a useful model and feasible for our neural system, network as shown model in Table for2 . our system, as shown in Table 2. Table 2. The model configuration of a neural network.

Operator InputTable of the 2. NeuralThe model Network configuration Output of a of neural the Neural network Network. Recursion Time Conv1d 16Input of the Output of 32 the Recursion 1 Operator Maxpooling Neural32 Network Neural Network 32 Time 1 Bottleneck1d Conv1d 3216 32 321 6 Conv1d 32 64 1 MaxpoolingMaxpooling 6432 32 641 1 Bottleneck1dBottleneck1d 6432 32 646 6 Conv1d Conv1d 6432 64 1281 1 MaxpoolingMaxpooling 12864 64 1281 1 Bottleneck1d 128 128 6 Bottleneck1d 64 64 6 Conv1d 128 256 1 MaxpoolingConv1d 25664 128 256 1 1 GAP Maxpooling 256128 128 256 1 1 Fully ConnectedBottleneck1d 256128 12864 6 1 Fully ConnectedConv1d 64128 256 31 1 Maxpooling 256 256 1 4. Discussion GAP 256 256 1 Fully Connected 256 64 1 4.1. Main Results Fully Connected 64 3 1 The use of biofeedback for treatment of diurnal and nocturnal bruxism has been discussed comprehensively4. Discussion over the years [21]. The clinical use of occlusal splints for biofeedback has been

4.1. Main Results The use of biofeedback for treatment of diurnal and nocturnal bruxism has been discussed comprehensively over the years [21]. The clinical use of occlusal splints for biofeedback has been reported in previous studies as well. Biofeedback by using a flat occlusal splint to remind a diurnal

Sensors 2020, 20, 89 12 of 15 reported in previous studies as well. Biofeedback by using a ﬂat occlusal splint to remind a diurnal bruxer of abnormal tooth contacts obtained an immediate success of approximately 50% [22]. To the best of our knowledge, this is the initial attempt to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device for diagnosis and treatment of bruxism. The study found that a full-dentition stress sensor will be a promising device in monitoring changes of a patient’s bracing or grinding and recalling self-awareness to behaviors eﬀectively in future practice.

4.2. Occlusal Adjustment The advantages and limitations of using the modiﬁed new device are discussed in this subsection. Accurate occlusal adjustment is essential in achieving multiple contacts and stable occlusion [23]. In conventional methods, articulating paper is a time-consuming, subjective, and often qualitative tool commonly used in OSSs for the determination of contact points that require adjustments. Although an OSS is widely used in clinical practice, there is a lack of understanding about the utility of an OSS for a technology that could guide occlusal adjustment [24]. In other words, the current knowledge of OSSs in treatment of bruxism has mainly depended on dentists’ professional experience. Our proposed sensor device now makes it possible to change the way we treat bruxism from a typical subjective judgment to a data-based objective approach by combining the advantages of using chips and OSSs. The modiﬁed OSS shows a similar function as the T-Scan III system (Tekscan Inc. Boston, MA USA) and allows occlusal forces to be assessed objectively and dynamically during an articulation cycle [25]. Using the device, a bruxism patient can also learn to voluntarily control abnormal grinding or clenching events based on an occlusal force detection system and prompt the immediate relaxation of the jaw muscles [18]. The ultimate goal is to reduce or eliminate the symptoms of bruxism by self-regulation without the aid of a feedback device.

4.3. Chip Selection The whole sensor system integration solution was designed from scratch, considering both the hardware and ﬁrmware designs. In order to achieve real-time, dynamic monitoring of bite forces, a full-dentition occlusion detector must be worn on a patient’s upper or lower jaw dentition. When exposed to an oral environment, special requirements, such as miniature, low power consumption, and stable performance, must be considered to determine the size and the power consumption of the selected module. The main control module is the core and foundation of the entire detection system, which determines whether the entire system can detect and transmit data in a stable and accurate manner. The chips in the modiﬁed OSS have a thickness of approximately 300 µm, whereas the digital sensor of the T-scan III system is 100 µm thick [25]. The impact of chip thickness is thus a main study direction in the future.

4.4. Embedding Challenges Achieving stress sensor device integration and embedding in the mouth is subject to various challenges, and the core problem has to do with how we can successfully integrate a sensor chip into an OSS and operate it normally. In conventional methods, an OSS is fabricated by a manual workflow with the polymethyl methacrylate (PMMA) material [26]. With the emergence of digital technologies, computer-aided design and computer-aided manufacturing (CAD-CAM) additive (printing) or subtractive (milling) production of OSSs with PMMA-based resins have been proposed as great alternatives [26–28]. Additionally, recently introduced intraoral scanners can be integrated with the described system, in order to have a direct three-dimensional (3D) reproduction of dental arches without intermediate passages. These impression systems allow for an easy interaction with CAD/CAM printers and thus a completely digital workflow, with sufficient clinical precision and greater patients’ satisfaction if compared with conventional impression methods [29]. Stress-sensing chips may experience degraded performance under high temperature and high stress. Therefore, how to effectively combine traditional materials that require high-temperature and high-stress fabrication Sensors 2020, 20, 89 13 of 15 with chips that cannot withstand high temperature and high stress is the most basic problem that must be solved. At present time, both traditional approaches and digital methods are unable to propose an optimal strategy for embedding chips into OSS resins [10–12]. Although the modified OSS could be accomplished by light-curing, digital technology manufacturing and comparable resin selection may be new directions for future research. Moreover, future research may consider larger samples and sufficient monitoring periods for identifying clinical reliability, validity, sensitivity, and specificity features.

4.5. Experiments and Future Work One of the main novelties of this paper is using an AI algorithm for occlusal force adjustment. The input of the machine learning algorithm includes: (1) magnitude of the average occlusion force, (2) duration of the average occlusal force, (3) average contact area of occlusal forces, (4) contact points of occlusal forces, and (5) feedback of a patient pain perception level; the output of the machine learning algorithm is the level of occlusion adjustment. Therefore, the experiment results included all the inputs and the output. As well-known, machine learning algorithms need a mass of training with abundant data to obtain more accurate results. We proposed a new idea and implemented a primitive prototype in this paper. We did not obtain very large data for the machine learning algorithm. Therefore, we showed part of demonstrations of the whole system with one group of sensors and related control devices. The sampling frequency of the input signal is high enough for the bruxism frequency (the frequency of bruxism is much less than the sampling frequency). All our experiment data came from real sensors and related control devices. The research of this concept is a complex process and requires a lot of experimental veriﬁcation. In the current paper, the demonstration of the results was based on the laboratory study. We hope to share this biofeedback treatment concept and the preliminary in vitro results, which may be useful in future research of bruxism treatment.

5. Conclusions In this paper, we proposed a quantitative diagnosis and treatment framework for the treatment of bruxism using a full-dentition, intelligent occlusion stress sensor system. This study tested and veriﬁed a full-dentition, stress sensor integration and embedding approach using the proposed sandwich method combined with occlusal force data processing based on a machine learning algorithm. Experiment results showed the reasonable parameter metrics for the sensors system and demonstrated the feasibility of the scheme for eﬀective bruxism treatment.

Author Contributions: Conceptualization, J.G. and L.L.; methodology, J.G. and P.G.; software, Y.Z.; validation, Y.Z. and W.H.; formal analysis, J.G. and J.W.; resources, J.G. and L.L.; writing of the original draft preparation, J.G. and P.G.; writing of review and editing, J.G. and L.L. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the Fundamental Research Funds for the Central Universities (grant number: xjj2018285) and the Young Scholar Fund from Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research. Conﬂicts of Interest: The authors declare no conﬂicts of interest.

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