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

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 89
Author(s):  
Jinxia Gao ◽  
Longjun Liu ◽  
Peng Gao ◽  
Yihuan Zheng ◽  
Wenxuan Hou ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Sensor System ◽  
Diagnosis And Treatment ◽  
Prototype System ◽  
Signal Acquisition ◽  
Occlusal Force ◽  
Stress Sensor ◽  
Stabilization Splint ◽  
High Prevalence ◽  
Parameter Configuration

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.

scholarly journals Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3790
Author(s):  
Zachary Choffin ◽  
Nathan Jeong ◽  
Michael Callihan ◽  
Savannah Olmstead ◽  
Edward Sazonov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pressure Sensor ◽  
Learning Algorithm ◽  
Flexible Substrate ◽  
Ankle Injuries ◽  
Sensor System ◽  
Measurement Unit ◽  
K Nearest Neighbor ◽  
Machine Learning Technique ◽  
Learning Technique

Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.

Distributed fiber optic stress sensor system based on P-OFDR

2010 ◽  
Author(s):  
Zhiyong Dai ◽  
Xiaoxia Zhang ◽  
Zengshou Peng ◽  
Jianfeng Li ◽  
Zhonghua Ou ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Sensor System ◽  
Stress Sensor

scholarly journals The Role of the Primary-Care Physician in Treating Relapses in Multiple Sclerosis Patients

2009 ◽  
Vol 11 (3) ◽  
pp. 122-126 ◽  
Author(s):  
Sarah A. Morrow ◽  
Marcelo Kremenchutzky
Keyword(s):  
Multiple Sclerosis ◽  
Primary Care ◽  
Primary Care Physicians ◽  
Tertiary Care ◽  
Care Physician ◽  
Diagnosis And Treatment ◽  
High Prevalence ◽  
Tertiary Care Centers ◽  
Multiple Sclerosis Patients

Multiple sclerosis (MS) is a common disabling neurologic disease with an overall prevalence in Canada of 240 in 100,000. Multiple sclerosis clinics are located at tertiary-care centers that may be difficult for a patient to access during an acute relapse. Many relapses are evaluated by primary-care physicians in private clinics or emergency departments, but these physicians' familiarity with MS is not known. Therefore, a survey was undertaken to determine the knowledge and experience of primary-care physicians regarding the diagnosis and treatment of MS relapses. A total of 1282 licensed primary-care physicians in the catchment area of the London (Ontario, Canada) Multiple Sclerosis Clinic were identified and mailed a two-page anonymous survey. A total of 237 (18.5%) responses were obtained, but only 216 (16.8%) of these respondents were still in active practice. Of these 216 physicians, only 9% reported having no MS patients in their practice, while 70% had one to five patients, 16.7% had six to ten, and 1.9% had more than ten (3.7% did not respond to this question). Corticosteroids were recognized as an MS treatment by 49.5% of the respondents, but only 43.1% identified them as a treatment for acute relapses. In addition, 31% did not know how to diagnose a relapse, and only 37% identified new signs or symptoms of neurologic dysfunction as indicating a potential relapse. Despite the high prevalence of MS in Canada, primary-care physicians require more education and support from specialists in MS care regarding the diagnosis and treatment of MS relapses.

Newborn Screening for Hermansky-Pudlak Syndrome Type 3 in Puerto Rico.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3290-3290
Author(s):  
Maribel Torres-Serrant ◽  
Sonia I. Ramirez ◽  
Carmen L. Cadilla ◽  
Maria E. Echevarria ◽  
Pedro J. Santiago
Keyword(s):  
Puerto Rico ◽  
Newborn Screening ◽  
Puerto Rican ◽  
Skin Pigmentation ◽  
The Other ◽  
Diagnosis And Treatment ◽  
Dna Pooling ◽  
Blood Samples ◽  
High Prevalence ◽  
Hermansky Pudlak Syndrome

Abstract Background: Hermansky-Pudlak Syndrome (HPS) is an autosomal recessive disorder characterized by albinism, mucocutaneous bleeding, and storage of ceroid material in macrophages (Hermansky and Pudlak, 1959). Many of these patients develop pulmonary fibrosis and colitis from which about 68% eventually die (Witkop et al, 1990). Patients that are not easily identified by physical characteristics (mostly HPS-3 patients) may have serious hemorrhagic complications when suffer severe injuries or surgical interventions. HPS is a rare disease worldwide, but it is the most common single-gene disorder among persons of Puerto Rican descent (Witkop et al, 1990). Two founder mutations (HPS-1 and HPS-3) account for most HPS cases in Puerto Rico (PR). The first one is widely prevalent in the Northwestern region (Oh et al, 1996) and the other (HPS-3) appeared in a small mid-central region of the island (Anikster et al,2000). HPS-3 patients usually present minimal skin pigmentation deficiency and thus diagnosis of albinism often is missed. Visual acuity problems often are detected late in infancy and childhood. This usually results in poor school progress, late diagnosis and treatment of patients, and delayed counseling of parents. Objective: To determine the prevalence of HPS-3 in Puerto Rican (PR) newborns using DNA pooling technique. Design/Methods: An aleatory sample of 4,690 PR infants born in 2005 (representing approximately 10% of annual PR births) was tested for the HPS-3 mutation, using DNA extracted from dried blood samples (Drocopoli et al, 1996). PCR analysis was carried out as described (Oh et al, 1996; Anikster et al,2000). Samples were tested in DNA pools of 5 newborns each. The validation of the PCR pooling technique for HPS-3 had been carried out in earlier studies in our laboratory by testing 1,500 newborn dried blood samples individually and in 300 total 5-sample pools. All positive samples detected individually were also unequivocally identified as positive when tested in pools. Results: Among the 4,690 newborns tested, 56 presented the HPS-3 mutation and they were confirmed in repeated testing. Two newborns were found to be HPS-3 homozygous. This finding was confirmed several times. The HPS-3 carrier frequency in the island-wide newborn population was 1:84 (1.19%). Both homozygous infants were born close to but outside of the high prevalence region previously reported in PR (Anikster et al,2001). Forty five percent of infants heterozygous for the HPS-3 mutation and one homozygous were found in the high prevalence area and the surrounding 10 miles radius; the other 56% of cases were distributed throughout the rest of the island. Conclusions: Our study has shown that the high prevalence area previously described by Anikster et al, where the founder mutation was identified has been spreading out throughout the rest of the island. Apparently, this is the result of rapid mobility of the Puerto Rican population during the last decades. Our data also demonstrate that the relatively high prevalence of the HPS-3 mutation (1.19%) justifies universal newborn screening. The use of DNA pooling reduces time and labor in newborn screening thus facilitating early diagnosis and treatment of children with HPS-3 and the provision of genetic counseling to patient’s parents and relatives.

scholarly journals EVALUATION OF A MOBILE MULTI-SENSOR SYSTEM FOR SEAMLESS OUTDOOR AND INDOOR MAPPING

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 31-35
Author(s):  
D. Iwaszczuk ◽  
Z. Koppanyi ◽  
J. Pfrang ◽  
C. Toth
Keyword(s):  
Sensor System ◽  
Prototype System ◽  
Mobile Mapping ◽  
Gps Receivers ◽  
Indoor Scene ◽  
Mapping System ◽  
Laser Scanners ◽  
Mobile Mapping System ◽  
Total Lack ◽  
Indoor And Outdoor

<p><strong>Abstract.</strong> Indoor mapping has been gaining importance recently. One of the main applications of indoor maps is personal navigation. For this application, the connection to the outdoor map is very important, as users typically enter the building from outside and navigate to their destination inside. Obtaining this connection, however, is challenging, as the georeferencing of indoor maps is difficult due to the weak or total lack of GPS signal which makes positioning impossible in general. One solution for this problem could be matching indoor and outdoor datasets. Unfortunately, this is difficult due to the very low or non-existing overlap between the indoor and outdoor datasets as well as the differences in different. To overcome this problem, we propose a mobile mapping system, which can seamlessly capture the outdoor and indoor scene. Our prototype system contains three laser scanners, six RGB cameras, two GPS receivers and one IMU. In this paper, we propose an approach to seamlessly map a building and define the requirements for the mapping system. We primarily describe the construction phase of this system. Finally, we evaluate the performance of our mapping system with regard to the defined requirements.</p>

A fiber optic Sagnac multi-stress sensor system

2011 ◽  
Author(s):  
HanChul Kang ◽  
JuneHo Lee ◽  
JongKil Lee ◽  
HyunJin Kim ◽  
Minho Song
Keyword(s):  
Fiber Optic ◽  
Sensor System ◽  
Stress Sensor

scholarly journals Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning

2018 ◽  
Vol 5 (4) ◽  
pp. 107
Author(s):  
Jamie Scanlan ◽  
Francis Li ◽  
Olga Umnova ◽  
Gyorgy Rakoczy ◽  
Nóra Lövey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Large Population ◽  
The Elderly ◽  
Signal Acquisition ◽  
Impulse Responses ◽  
Electronic Stethoscope ◽  
Increased Risk ◽  
Bone Condition

Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of the bone condition. Therefore, the aim of the authors’ project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient’s tibia and an electronic stethoscope to acquire the impulse responses. The signals are processed as mel frequency cepstrum coefficients and passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia’s impulse responses. Following some discussions of the mechanism and procedure, this paper details the signal acquisition using the stethoscope and the subsequent signal processing and the statistical machine learning algorithm. Pilot testing with 12 patients achieved over 80% sensitivity with a false positive rate below 30% and accuracies in the region of 70%. An extended dataset of 110 patients achieved an error rate of 30% with some room for improvement in the algorithm. By using common clinical apparatus and strategic machine learning, this method might be suitable as a large population screening test for the early diagnosis of osteoporosis, thus avoiding secondary complications.

scholarly journals Supervised Machine Learning Approach for Detecting Missing Clamps in Rail Fastening System from Differential Eddy Current Measurements

2021 ◽  
Vol 11 (9) ◽  
pp. 4018
Author(s):  
Praneeth Chandran ◽  
Florian Thierry ◽  
Johan Odelius ◽  
Stephen M. Famurewa ◽  
Håkan Lind ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eddy Current ◽  
Learning Algorithm ◽  
Field Measurements ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Sensor System ◽  
Current Sensor ◽  
Field Frequency ◽  
Eddy Current Sensor

The rail fastening system forms an integral part of rail tracks, as it maintains the rail in a fixed position, upholding the track stability and track gauge. Hence, it becomes necessary to monitor their conditions periodically to ensure safe and reliable operation of the railway. Inspection is normally carried out manually by trained operators or by employing 2-D visual inspection methods. However, these methods have drawbacks when visibility is minimal and are found to be expensive and time consuming. In the previous study, the authors proposed a train-based differential eddy current sensor system that uses the principle of electromagnetic induction for inspecting the railway fastening system that can overcome the above-mentioned challenges. The sensor system includes two individual differential eddy current sensors with a driving field frequency of 18 kHz and 27 kHz respectively. This study analyses the performance of a machine learning algorithm for detecting and analysing missing clamps within the fastening system, measured using a train-based differential eddy current sensor. The data required for the study was collected from field measurements carried out along a heavy haul railway line in the north of Sweden, using the train-based differential eddy current sensor system. Six classification algorithms are tested in this study and the best performing model achieved a precision and recall of 96.64% and 95.52% respectively. The results from the study shows that the performance of the machine learning algorithms improved when features from both the driving channels were used simultaneously to represent the fasteners. The best performing algorithm also maintained a good balance between the precision and recall scores during the test stage.

scholarly journals Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1070 ◽  
Author(s):  
Yibeltal Chanie Manie ◽  
Jyun-Wei Li ◽  
Peng-Chun Peng ◽  
Run-Kai Shiu ◽  
Ya-Yu Chen ◽  
...  
Keyword(s):  
Sensor Network ◽  
Measurement Accuracy ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Sensor System ◽  
Strain Sensing ◽  
Fbg Sensor ◽  
Signal Measurement ◽  
Fbg Sensors ◽  
Long Short Term Memory

In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

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