Sensor Attachment Research Articles

ACCURACY RATING MEASUREMENT OF VIBRATION ELECTROMECHANICAL DEVICES

Electromechanical devices consist mainly of rotor systems. Vibration is the result of the interaction of the elements of the rotor system and is largely determined by the accuracy of manufacturing elements at the production stage and defects arising in the process of operation. The main components of the rotor systems that affect vibration are bearings. To determine the technical condition of the bearings and the service life of the rotor system, it is necessary to accurately measure the unobservable vibrations of the rotor. The article describes the model of the channel for measuring the vibration of an electromechanical system, built using the apparatus of bond graphs. The transfer function is obtained by analyzing the signal flow graph. The systematic and random errors of vibration measurement are analyzed depending on the mass ratio between the system case and the vibration transducer for various sensor masses and attachment rigidity.

Is there an Optimal Localization of Cardio-microphone Sensors for Phonocardiogram Analysis?

Heart auscultation is one of the most useful medical diagnostic tools for getting valuable information of heart valves and heart hemodynamics functions. However, the information acquired by a traditional stethoscope can be inaccurate and insufficient. Phonocardiogram (PCG) was developed to improve accuracy through visual inspection and analysis. Digitally processed, PCG can then be analyzed by automated heart sound analysis systems. But there is no standardization for PCG data acquisition unlike electrocardiogram (ECG). This study aims at analyzing the influence of cardiomicrophone localization on the chest for the study of cardiac sounds S1 and S2. For that purpose, simultaneous acquisitions of 12 PCG signals with one ECG signal were realized and a comparative analysis of delays between R waves of ECG and detected S1 and S2 sounds was conducted. Results show that there are significant differences between R-S1 (or R-S2) intervals obtained from different areas of sensor placement on the chest. For future works on PCG, studies dealing with the analysis of heart sounds or proposing new heart sounds detection algorithms may pay attention to the location and attachment of PCG sensors.

Application of Signed Distance Function Neural Network in Real-Time Feet Tracking.

Loss of locomotion is one major problem faced by the elderly currently. Various rehabilitation technologies have been developed to assist them in recovering walking capability. Gait monitoring is an important aspect of lower-limb function rehabilitation. By observing the walking behaviour of a patient, the stability and recovery progress can be evaluated. Despite gait is often measured by motion capture and force-based measurement, these methods are costly and non-portable, whereas inertial measurement units (IMUs) require the attachment of sensors onto the subject's body. A few contactless measurements have been proposed, however, none of them views the feet from the back, making it non-trivial to transfer the method to over ground rehabilitation robots. This paper proposes a method to track the poses of the feet in real time using a novel deep neural network, termed SDF-Net, that models the signed distance function (SDF) of an object. Independent of the viewing angle, the algorithm receives the colour and depth images of the feet as input and computes the pose of the feet. The tracking accuracy is evaluated by having a subject to perform various actions with the feet; the dynamic errors are found to be less than 9 mm and 8 degrees for position and orientation errors respectively, which are better than the state-of-arts reviewed.

Improved Acquisition of Vibroarthrographic Signals of the Knee Joint.

This paper presents an improved solution for vibroarthrographic measurements. Four different setups for sensor attachment to the knee were assessed with a focus on the stability and reproducibility of the measured signals. By means of power spectral density estimates, the main signal components were compared and afterwards evaluated by conducting a cross-correlation analysis.

Polymer Microarrays for the Discovery and Optimization of Robust Optical-Fiber-Based pH Sensors.

Polymer microarrays were utilized for the high-throughput screening and discovery of optimal polymeric substrates capable of trapping functional ratiometric fluorescence-based pH sensors. This led to the identification of poly(methyl methacrylate- co-2-(dimethylamino) ethyl acrylate) (PA101), which allowed, via dip coating, the attachment of fluorescent pH sensors onto the tips of optical fibers, resulting in robust, rapid, and reproducible sensing of physiological pHs.

Field measurement of U-value using multiple sensors at test chamber and EIFS building

ABSTRACT The purpose of this study is to analyse the heat flux difference and examine applicability of the heat flux sensor along with various sensor attachment methods. Using multiple sensors and various tapes, a KS F 2278 thermal test was performed in hot/cold chamber conditions with standard panel specimen. The measured heat flux values of sensors were stable. But values of sensors were smaller than the heat flux result of KS F 2278. Thus, thermal resistance of attachment tape on a sensor should be considered during on site U-value measuring process. This study also examined the differences between measured U-value and design U-value by attaching the different sensors in the same area using multiple heat flux sensors. All cases are shown to the same patterns on each heat flux and U-value graph, but U-values with regard to various sensors have shown some differences to the designed values. Using in-situ measurement, the EIFS wall was found to be difficult to measure accurately due to the small level of heat flux.

Hybrid carbon nanostructured fibers: stepping stone for intelligent textile-based electronics.

The journey of smart textile-based wearable technologies first started with the attachment of sensors to fabrics, followed by embedding sensors in apparels. Presently, garments themselves can be transformed into sensors, which demonstrates the tremendous growth in the field of smart textiles. Wearable applications demand flexible materials that can withstand deformation for their practical use on par with conventional textiles. To address this, we explore the potential reasons for the enhanced performance of wearable devices realized from the fabrication of carbon nanostructured fibers with the use of graphene, carbon nanotubes and other two-dimensional materials. This review presents a brief introduction on the fabrication strategies to form carbon-based fibers and the relationship between their properties and characteristics of the resulting materials. The likely mechanisms of fiber-based electronic and storage devices, focusing mainly on transistors, nano-generators, solar cells, supercapacitors, batteries, sensors and therapeutic devices are also presented. Finally, the future perspectives of this research field of flexible and wearable electronics are discussed. The present study supplements novel ideas not only for beginners aiming to work in this booming area, but also for researchers actively engaged in the field of fiber-based electronics, dealing with advanced electronics and wide range of functionalities integrated into textile fibers.

Walking Simulation for VR Game Character Using Remote Sensoring Device Based on AHRS-Motion Recognition

In this paper, the remote sensoring device for controlling the avatar in the virtual reality (VR) game which is being worn by the head-mounted display (HMD) was investigated. This remote sensoring device, which people use while sitting on a swivel chair, minimizes sensor attachment, and removes parameters for pelvic rotation angle in standing position. The rotating chair, which also functions as the center-weighted stationary belt of the conventional treadmill-type input device, has the advantage that the head trekking function mounted on the HMD can be utilized freely and the balance feeling of the user can be stabilized in a sitting position on the chair. In this posture, the sensoring input device was developed using Bluetooth wireless communication to freely turn and walk. Using the data from the inertial measurement device, which was a combination of the speedometer and the accelerometer, a sensoring input device for recognizing the user's leg motion was created. The running speed of the recognized leg was designed to be close to the average walking speed and the running speed of the person, so that the running and walking in the game can be separated and implemented. The motion velocity of the leg would be calculated through each speedometer measurement, the value would be integrated, and the error would be corrected by the Kalman filter to obtain the user's knee angle. In other words, applying a Kalman filter to data obtained from sensors, such as accelerometers, angular speedometers, and geomagnetic sensor can determine the direction of motion of the legs. This integrated system is called the attitude and heading references system. The knee joint angle and the value of the pressure sensor were used as threshold values to determine the state of the leg when walking, and the angular velocity derived from the movement of the leg was used to determine the moving speed of the avatar in the VR game. The recognition rate was calculated by comparing the number of times the user attempts to move the avatar by moving the avatar and the number of times the avatar of the game was moved, and the distance of the avatar was measured to evaluate the performance of the proposed system.

Experimental study on the life prediction of servo motors through model-based system degradation assessment and accelerated degradation testing

The advent of smart factories has resulted in the frequent utilization of industrial robots within factories to increase production automation and efficiency. Due to the increase in the number of industrial robots, it has become more important to prevent any unexpected breakdowns of the factory. As a result, the lifespan prediction of machinery has become a crucial factor because such failures can be directly associated with factory productivity resulting in significant losses. Most of the failures occur within one of the core components of the robot arm, the servo motor, and thus we will focus on the analysis of the servo motor in this study. However, sensor attachment to such equipment is considered difficult due to the dynamic movement of the robot arm, meaning that internal instrumentation should be utilized during analysis. In addition, no definite measure to determine the degradation of the motor exists, and thus a new degradation index is proposed in this study. Therefore, in this study, the lifespan of the servo motor will be estimated through accelerated degradation testing methods based on a new system degradation assessment method, which estimates the fault of the system using observer-based residuals with encoder data obtained from internal instrumentation.

Inter-Method Reliability of Pulse Volume Related Measures Derived Using Finger-Photoplethysmography

Abstract. Pulse volume (PV) and its related measures, such as modified normalized pulse volume (mNPV), direct-current component (DC), and pulse rate (PR), derived from the finger-photoplethysmogram (FPPG), are useful psychophysiological measures. Although considerable uncertainties exist in finger-photoplethysmography, little is known about the extent of the adverse effects on the measures. In this study, we therefore examined the inter-method reliability of each index across sensor positions and light intensities, which are major disturbance factors of FPPG. From the tips of the index fingers of 12 participants in a resting state, three simultaneous FPPGs having overlapping optical paths were recorded, with their light intensity being changed in three steps. The analysis revealed that the minimum values of three coefficients of Cronbach’s α for ln PV, ln mNPV, ln DC, and PR across positions were .948, .850, .922, and 1.000, respectively, and that those across intensities were .774, .985, .485, and .998, respectively. These findings suggest that ln mNPV and PR can be used for psychophysiological studies irrespective of minor differences in sensor attachment positions and light source intensity, whereas and ln DC can also be used for such studies but under the condition of light intensity being fixed.

Noncontact Sleep Study Based on an Ensemble of Deep Neural Network and Random Forests

Sleep quality, which is an undervalued health issue that affects well-being and daily lives, is checked through the polysomnography (PSG), considered as the gold standard for determining sleep stages. Due to the obtrusiveness of its sensor attachments, recent sleep stage classification algorithms using noninvasive sensors have been developed and commercialized. However, the newly developed devices and algorithms used in the previous studies have lacked the detection of non-rapid eye movement and rapid eye movement sleep, which are known to be correlated with the development of sleep disorders, cardiovascular disease, metabolic disease, and neurodegeneration. We devise a novel approach to employ ensemble of deep neural network and random forest for the performance of noncontact sleep stage classification. Notably, this paper is designed based on the PSG data of sleep-disordered patients, which were received and certified by professionals at Hanyang University Hospital. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance with previously proposed methods and a commercialized sleep monitoring device called ResMed S+. The proposed algorithm was assessed with random patients following gold-standard measurement schemes (PSG examination), and results show a promising novel approach for determining sleep stages in an economical and unobtrusive manner.

Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation.

Validity of contact skin temperature sensors under different environmental conditions with and without fabric coverage: characterisation and correction.

Contact skin temperature (Tsk) sensors are calibrated under uniform thermal conditions but used in the presence of a skin-to-environment temperature gradient. We aimed to characterise the validity of contact Tsk sensors when measuring surface temperature under a range of environmental and fabric coverage conditions, to estimate practical temperature limits for a given measurement bias and to explore correcting for bias. Using two types of contact Tsk sensors (thermistors, n = 5; iButtons, n = 5), we performed experiments in three phases: (1) conventional calibration (uniform thermal environment) over 15-40°C in 5°C steps (at t = 0, and 24h, 12 weeks later), (2) surface temperature measurements of a purpose-made aluminium plate (also 15-40°C) at different environmental temperatures (15, 25, 35°C) with different sensor attachments and fabric coverings to assess measurement bias and calculate correction factors that account for the next-to-surface microclimate temperature and (3) surface measurements (33.1°C in 20°C environment) for assessing generated corrections. The mainresults were as follows: (1) afterinitial calibration, Tsk sensors were valid under uniform thermal conditions [mean bias < 0.05°C, typical error of the estimate < 0.1°C]. (2) For the surface measurements, bias increased with increasing surface-to-microclimate temperature difference for both sensor types. The range of surface temperatures possible to remain within given bias limits could be estimated for the various conditions. (3) For a given measurement, using corrections encompassing the microclimate temperature (mean difference - 0.1 to 0.5°C) performed better than conventional calibration alone (mean difference - 2.1 to - 0.3°C). In conclusion, the bias of Tsk sensors is influenced by the microclimate temperature and, therefore, body coverings. Where excessive bias is expected, the validity can be improved through sensor and attachment selection and by applying corrections that account for the local temperaturegradient.

A Multi-sensor Fall Detection System Based on Multivariate Statistical Process Analysis

Physical injuries of elders resulting from fall accidents have become a serious social problem. An automatic fall detection system can provide timely feedback regarding the activities of elders and improve the quality of life of the elderly. This article aims to design an automatic fall detection system using multiple wearable sensors and find optimal sensor attachment locations. Six sensors are fixed on different parts of the human body to obtain real-time movement data. The autoregressive integrated moving average model is adopted to remove autocorrelation from the original data. Then, the multidimensional data are processed using principal component analysis. Finally, a novel threshold method based on a multivariate control chart is proposed for detection falls. This method can achieve high detection accuracy and can adapt to different individuals because the detection threshold is established using individual historical data. Different activities and simulated falls were performed by volunteers. Compared with most other single-sensor systems, the proposed multi-sensor system achieved higher sensitivity (94.8%) and specificity (95.2%), especially at a low sampling frequency (20 Hz). By evaluating the performance of different sensor attachment locations, the results showed that three sensors fixed on the waist, arm and thigh were able to efficiently distinguish falls from non-falls.

Monitoring of Cardiorespiratory Signals Using Thermal Imaging: A Pilot Study on Healthy Human Subjects.

Heart rate (HR) and respiratory rate (RR) are important parameters for patient assessment. However, current measurement techniques require attachment of sensors to the patient’s body, often leading to discomfort, stress and even pain. A new algorithm is presented for monitoring both HR and RR using thermal imaging. The cyclical ejection of blood flow from the heart to the head (through carotid arteries and thoracic aorta) leads to periodic movements of the head; these vertical movements are used to assess HR. Respiratory rate is estimated by using temperature fluctuations under the nose during the respiratory cycle. To test the viability and feasibility of this approach, a pilot study was conducted with 20 healthy subjects (aged 18–36 and 1 aged 50 years). The study consisted of two phases: phase A (frontal view acquisitions) and phase B (side view acquisitions). To validate the results, photoplethysmography and thoracic effort (piezoplethysmography) were simultaneously recorded. High agreement between infrared thermography and ground truth/gold standard was achieved. For HR, the root-mean-square errors (RMSE) for phases A and B were 3.53 ± 1.53 and 3.43 ± 1.61 beats per minute, respectively. For RR, the RMSE between thermal imaging and piezoplethysmography stayed around 0.71 ± 0.30 breaths per minute (phase A). This study demonstrates that infrared thermography may be a promising, clinically relevant alternative for the assessment of HR and RR.

Speech Adaptation to Kinematic Recording Sensors: Perceptual and Acoustic Findings.

This study used perceptual and acoustic measures to examine the time course of speech adaptation after the attachment of electromagnetic sensor coils to the tongue, lips, and jaw. Twenty native English speakers read aloud stimulus sentences before the attachment of the sensors, immediately after attachment, and again 5, 10, 15, and 20 min later. They read aloud continuously between recordings to encourage adaptation. Sentence recordings were perceptually evaluated by 20 native English listeners, who rated 150 stimuli (which included 31 samples that were repeated to assess rater reliability) using a visual analog scale with the end points labeled as "precise" and "imprecise." Acoustic analysis began by segmenting and measuring the duration of the fricatives /s/ and /ʃ/ as well as the whole sentence. The spectral center of gravity and spectral standard deviation of the 2 fricatives were measured using Praat. These phonetic targets were selected because the standard placement of sensor coils on the lingual surface was anticipated to interfere with normal fricative production, causing them to become distorted. Perceptual ratings revealed a decrease in speech precision after sensor attachment and evidence of adaptation over time; there was little perceptual change beyond the 10-min recording. The spectral center of gravity for /s/ decreased, and the spectral standard deviation for /ʃ/ increased after sensor attachment, but the acoustic measures showed no evidence of adaptation over time. The findings suggest that 10 min may be sufficient time to allow speakers to adapt before experimental data collection with Northern Digital Instruments Wave electromagnetic sensors.

IR thermography-based monitoring of respiration phase without image segmentation

BackgroundRespiratory rate is an essential parameter in biomedical research and clinical applications. Most respiration measurement techniques in preclinical animal models require surgical implantation of sensors. Current clinical measurement modalities typically involve attachment of sensors to the patient, causing discomfort. We have previously developed a non-contact approach to measuring respiration phase in head-restrained rodents using infrared (IR) thermography. While the non-invasive nature of IR thermography offers many advantages, it also bears the complexity of extracting respiration signals from videos. Previously reported algorithms involve image segmentation to identify the nose in IR videos and extract breathing-relevant pixels which is particularly challenging if the videos have low contrast or suffer from suboptimal focusing. New methodTo address this challenge, we developed a novel algorithm, which extracts respiration signals based on pixel time series, removing the need for nose-tracking and image segmentation. Results & comparison with existing methodsWe validated the algorithm by performing respiration measurements in head-restrained mice and in humans with IR thermography in parallel with established standard techniques. We find the algorithm reliably detects inhalation onsets with high temporal precision. ConclusionsThe new algorithm facilitates the application of IR thermography for measuring respiration in biomedical research and in clinical settings.

Skin Temperature Measurement Using Contact Thermometry: A Systematic Review of Setup Variables and Their Effects on Measured Values.

Background: Skin temperature (Tskin) is commonly measured using Tskin sensors affixed directly to the skin surface, although the influence of setup variables on the measured outcome requires clarification.Objectives: The two distinct objectives of this systematic review were (1) to examine measurements from contact Tskin sensors considering equilibrium temperature and temperature disturbance, sensor attachments, pressure, environmental temperature, and sensor type, and (2) to characterise the contact Tskin sensors used, conditions of use, and subsequent reporting in studies investigating sports, exercise, and other physical activity.Data sources and study selection: For the measurement comparison objective, Ovid Medline and Scopus were used (1960 to July 2016) and studies comparing contact Tskin sensor measurements in vivo or using appropriate physical models were included. For the survey of use, Ovid Medline was used (2011 to July 2016) and studies using contact temperature sensors for the measurement of human Tskin in vivo during sport, exercise, and other physical activity were included.Study appraisal and synthesis methods: For measurement comparisons, assessments of risk of bias were made according to an adapted version of the Cochrane Collaboration's risk of bias tool. Comparisons of temperature measurements were expressed, where possible, as mean difference and 95% limits of agreement (LoA). Meta-analyses were not performed due to the lack of a common reference condition. For the survey of use, extracted information was summarised in text and tabular form.Results: For measurement comparisons, 21 studies were included. Results from these studies indicated minor (<0.5°C) to practically meaningful (>0.5°C) measurement bias within the subgroups of attachment type, applied pressure, environmental conditions, and sensor type. The 95% LoA were often within 1.0°C for in vivo studies and 0.5°C for physical models. For the survey of use, 172 studies were included. Details about Tskin sensor setup were often poorly reported and, from those reporting setup information, it was evident that setups widely varied in terms of type of sensors, attachments, and locations used.Conclusions: Setup variables and conditions of use can influence the measured temperature from contact Tskin sensors and thus key setup variables need to be appropriately considered and consistently reported.

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans.

Prehension, the act of reaching to grasp an object, is central to the human experience. We use it to feed ourselves, groom ourselves, and manipulate objects and tools in our environment. Such behaviors are impaired by many sensorimotor disorders, yet our current understanding of their neural control is far from complete. Current technologies for investigating human reach-to-grasp movements often utilize motion tracking systems that can be expensive, require the attachment of markers or sensors to the hands, impede natural movement and sensory feedback, and provide kinematic output that can be difficult to interpret. While generally effective for studying the stereotypical reach-to-grasp movements of healthy sighted adults, many of these technologies face additional limitations when attempting to study the unpredictable and idiosyncratic reach-to-grasp movements of young infants, unsighted adults, and patients with neurological disorders. Thus, we present a novel, inexpensive, and highly reliable yet flexible protocol for quantifying the temporal and kinematic structure of idiosyncratic reach-to-grasp movements in humans. High speed video cameras capture multiple views of the reach-to-grasp movement. Frame-by-frame video analysis is then used to document the timing and magnitude of pre-defined behavioral events such as movement start, collection, maximum height, peak aperture, first contact, and final grasp. The temporal structure of the movement is reconstructed by documenting the relative frame number of each event while the kinematic structure of the hand is quantified using the ruler or measure function in photo editing software to calibrate 2 dimensional linear distances between two body parts or between a body part and the target. Frame-by-frame video analysis can provide a quantitative and comprehensive description of idiosyncratic reach-to-grasp movements and will enable researchers to expand their area of investigation to include a greater range of naturalistic prehensile behaviors, guided by a wider variety of sensory modalities, in both healthy and clinical populations.

Effect on kinematic gait variables of two methods of inertial measurement unit sensor attachment to the extremities of horses under controlled conditions of treadmill exercise in sound horses at the walk and trot: A pilot study

Effect on kinematic gait variables of two methods of inertial measurement unit sensor attachment to the extremities of horses under controlled conditions of treadmill exercise in sound horses at the walk and trot: A pilot study