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This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer's time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app's setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage. [ABSTRACT FROM AUTHOR]

Ocean waves play a significant role in coastal dynamics and the management of coastal development activities. However, there are limited instruments to measure the wave height, particularly in developing countries. This study introduces a novel instrument design for simple and cost-effective wave height measurement. The proposed instrument combines a Hall-effect sensor and an accelerometer to capture the rise and fall of a buoy in response to wave movements. The Hall-effect sensor as a rotary encoder detects the rotation of the buoy while the accelerometer measures the tilt of the instrument caused by ocean waves. The instrument is constructed using PVC pipes and incorporates long-range (LoRa) communication technology for real-time monitoring. Experimental tests were conducted at a study site in Banyuwangi Regency, Indonesia, where the performance of the instrument was compared with an ultrasonic sensor-based instrument. The results validate the effectiveness of the proposed instrument design for wave height measurement. The collected data were transmitted via the LoRa communication system, enabling convenient monitoring and analysis of sea-level changes. The valuable contribution of the instrument to the field stems from its precision in measuring the wave height, adaptability to diverse conditions, and potential applicability in shallow waters. [ABSTRACT FROM AUTHOR]

Background: Balance parameters derived from wearable sensor measurements during postural sway have been shown to be sensitive to experimental variables such as test duration, sensor number, and sensor location that influence the magnitude and frequency-related properties of measured center-of-mass (COM) and center-of-pressure (COP) excursions. In this study, we investigated the effects of test duration, the number of sensors, and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers. Methods: Twelve volunteers without any prior history of balance disorders were enrolled in the study. They were asked to perform two 2-min quiet standing tests with two different testing conditions (eyes open and eyes closed). Five inertial measurement units (IMUs) were employed to capture postural sway data from each participant. IMUs were attached to the participants' right legs, the second sacral vertebra, sternum, and the left mastoid processes. Balance parameters of interest were calculated for the single head, sternum, and sacrum accelerometers, as well as, a three-sensor combination (leg, sacrum, and sternum). Accelerometer data were used to estimate COP-based and COM-based balance parameters during quiet standing. To examine the effect of test duration and sensor location, each 120-s recording from different sensor locations was segmented into 20-, 30-, 40-, 50-, 60-, 70-, 80-, 90-, 100-, and 110-s intervals. For each of these time intervals, time- and frequency-domain balance parameters were calculated for all sensor locations. Results: Most COM-based and COP-based balance parameters could be derived reliably for clinical applications (Intraclass-Correlation Coefficient, ICC ≥ 0.90) with a minimum test duration of 70 and 110 s, respectively. The exceptions were COP-based parameters obtained using a sacrum-mounted sensor, especially in the eyes-closed condition, which could not be reliably used for clinical applications even with a 120-s test duration. Conclusions: Most standing balance parameters can be reliably measured using a single head- or sternum-mounted sensor within a 120-s test duration. For other sensor locations, the minimum test duration may be longer and may depend on the specific test conditions. [ABSTRACT FROM AUTHOR]

Target detection on the surface of water using wireless sensor networks (WSNs) should be able to collect information as much as possible. The previous studies are only able to collect speed and direction of the target. In addition to previous studies, we collect not only speed and direction but also real time location of the target. In this paper, we propose a target localization algorithm based on Time Different of Arrival (TDoA). The proposed algorithm takes into account grid topology of sensors because it is more tractable. The algorithm is validated using experiments to compute the mean average error (MAE); in these experiments, the estimated and real location coordinates were compared. The experimental results achieved the smallest MAE at 0.25 m. The experimental results indicated that the MAE is inversely proportional to the distance between the sensors. Moreover, the MAE of the x-axis is always greater than that of the y-axis is well explained using the principle of the Abbe error. [ABSTRACT FROM AUTHOR]

A MS6.8 earthquake occurred on 5th September 2022 in Luding county, Sichuan, China, at 12: 52 Beijing Time (4:52 UTC). We complied a dataset of PGA, PGV, and site vS30 of 73 accelerometers and 791 Micro-Electro-Mechanical System (MEMS) sensors within 300 km of the epicenter. The inferred vS30 of 820 recording sites were validated. The study results show that: (1) The maximum horizontal PGA and PGV reaches 634.1 Gal and 71.1 cm/s respectively. (2) Over 80% of records are from soil sites. (3) The vS30 proxy model of Zhou J et al. is superior than that of Wald and Allen and performs well in the study area. The dataset was compiled in a flat file that consists the information of strong-motion instruments, the strong-motion records, and the vS30 of the recording sites. [ABSTRACT FROM AUTHOR]

Postural instability is a risk factor for falls in older adults. It is possible to detect postural stability using an integrated accelerometer (ACC) sensor in a smartphone. Therefore, a novel ACC-based smartphone application running on the Android operating system called BalanceLab was created and tested. This study aimed to determine the validity and reliability of a novel ACC-based Android smartphone application for assessing balance in older adults. Using BalanceLab, 20 older adults completed three balance assessments: the Modified Clinical Test of Sensory Interaction in Balance (MCTSIB), a single-leg stance test (SLST), and a limit of stability test (LOS). The validity of this mobile application was investigated using a three-dimensional (3D) motion analysis system and the Fullerton Advanced Balance (FAB) scale. The test-retest reliability of this mobile application was determined on two separate occasions within one day, at least two hours apart. The two static balance assessments (the MCTSIB and SLST) demonstrated moderate to excellent correlation with the 3D motion analysis system (r = 0.70–0.91) and the FAB scale (r = 0.67–0.80). However, the majority of the dynamic balance tests (the LOS tests) did not demonstrate any correlation with the 3D motion analysis system or the FAB scale. This novel ACC-based application demonstrated good to excellent test-retest reliability (ICC = 0.76–0.91). A static, but not dynamic, balance assessment tool that uses a novel ACC-based application for Android can be used to measure balance in older adults. This application has moderate to excellent validity and test-retest reliability. • A new accelerometer-based app was examined for its reliability and validity. • A balance app demonstrated high test-retest reliability. • An app showed a high level of validity in static but not in dynamic balance tests. • An older adult's static balance can be evaluated with a new balance app. [ABSTRACT FROM AUTHOR]

The growing diversity of animal‐borne sensor types is revolutionizing our understanding of wildlife biology. For example, researcher‐developed sensors, such as audio and video loggers, are being increasingly attached to wildlife tracking collars to provide insights into a range of topics from species interactions to physiology. However, such devices are often prohibitively power‐intensive, relative to conventional wildlife collar sensors, and their retrieval without compromising long‐term data collection and animal welfare remains a challenge. We present an open‐source system (SensorDrop) for remotely detaching individual sensors from wildlife collars. SensorDrop facilitates the retrieval of power‐intensive sensors while leaving non‐resource‐intensive sensors intact on animals. SensorDrop systems can be made using commercially available components and are a fraction of the cost of other timed drop‐off devices that detach full wildlife tracking collars. From 2021 to 2022, eight SensorDrop units were successfully deployed on free‐ranging African wild dog packs in the Okavango Delta as part of audio‐accelerometer sensor bundles attached to wildlife collars. All SensorDrop units detached after 2–3 weeks and facilitated the collection of audio and accelerometer data while leaving wildlife GPS collars intact to continue collecting locational data (>1 year), critical for long‐term conservation population monitoring in the region. SensorDrop offers a low‐cost method to remotely detach and retrieve individual sensors from wildlife collars. By selectively detaching battery‐depleted sensors, SensorDrop maximizes the amount of data collected per wildlife collar deployment and mitigates ethical concerns on animal rehandling. SensorDrop adds to the growing body of open‐source animal‐borne technologies being utilized by wildlife researchers to innovate and expand upon data collection practices and supports the continued ethical use of novel technologies within wildlife studies. [ABSTRACT FROM AUTHOR]

High–sensitivity shaken lattice interferometry (SLI) based sensors have the potential to provide deep space missions with the ability to precisely measure non–gravitational perturbing forces. This work considers the simulation of the OSIRIS-REx mission navigation in the vicinity of Bennu with the addition of measurements from onboard SLI–based accelerometers. The simulation is performed in the Jet Propulsion Laboratory's (JPL) Mission Analysis, Operations and Navigation Toolkit (MONTE) and incorporates OSIRIS-REx reconstructed trajectory and attitude data from the Navigation and Ancillary Information Facility (NAIF) database. The use of the reconstructed data from NAIF provides realistic true dynamical errors and JPL's MONTE software allows for a high–fidelity simulation of an integrated trajectory for the filter. The navigation performance and reduction of tracking and complex modelling enabled by the onboard SLI–based sensor are presented for two orbital phases of the OSIRIS–REx mission. Overall, the results show that the addition of SLI–based accelerometer measurements improves navigation performance, when compared to a radiometric tracking only configuration. In addition, results demonstrate that highly–precise accelerometer measurements can effectively replace at least one day of DSN passes over a three–day period, thereby reducing tracking requirements. Furthermore, it is shown that lower–fidelity surface force modeling and parameter estimation is required when using onboard SLI–based accelerometers. [ABSTRACT FROM AUTHOR]

Changes in gait are a well-documented symptom for several conditions, such as degenerative diseases, stroke, foot conditions, neurological conditions (i.e., Parkinson's) [1]. Gait speed is thus a well-known indicator for assessing an individual's functional mobility [2]. Machine learning (ML) shows great potential in estimating gait speed due to its ability to reveal hidden patterns in large datasets and has already shown promising results over standard strap-down integration methods for foot-mounted inertial sensors (IMU) [3]. For the present investigation, we evaluated various supervised ML models to estimate gait speed based on IMU data collected on the most dominant lower limb. The Gait Events Data Set by Miraldo et al. [4] was used for the analysis. This dataset consists of metadata and inertial measurements (tri-axial accelerometer and gyroscope @6.75ms/sample) from sensors fixed to the flat part of the tibia on the dominant side, collected from 22 healthy subjects. Each subject walked barefoot six times at three randomly ordered, self-paced speeds (comfortable, slow, fast) on a 40-m long 2-m wide walkway, without curves, with a flat and rigid surface. The trials lasted 30-to-60 seconds. Overall, the dataset contains 9,661 gait strides. The IMU data for each trial was segmented into strides by the method proposed in [5]. Several features (Table 1) were extracted from each stride [6] and then averaged over the length of the trial to be used as input to several multiple linear regression and tree-based models along with participant anthropometrics. The number of trials were split 80%/20% into training and test sets with data from the same subject appearing only in either one or the other. A grid-search was performed on the training set using leave-one-subject-out cross-validation (LOSO-CV) to find the best performing model and mean absolute percentage error (MAPE) reported as the validation metric. The selected model was compared to a dummy regression model used as a baseline. Tables 2-3 show the results for the different models. Ridge regression showed the best results with MAE equal to 0.12 m/s and MAPE to 9.13%, almost three times lower compared to baseline. Fig. 1 shows the difference between ground truth and predicted speed across different subjects, the related Bland-Altman plot, and a sample of the obtained error for one trial. Results obtained are comparable to results in [5,6]. [Display omitted] This investigation shows that Ridge regression using features extracted from detected gait events in IMU data and participant anthropometrics is an appropriate approach to estimate gait speed, outperforming several other linear and tree-based regression models. The final model achieved promising results with a considerably lower error than the baseline, although further investigation is needed to confirm efficacy at atypical gait speeds, i.e., with populations with movement disorders. Funding: This work was supported in part by Enterprise Ireland (EI) and Abbvie, Inc. under agreement IP 2017 0625; and in part by the Science Foundation Ireland which are Co-Funded through the European Regional Development Fund under Grant 12/RC/2289-P2-INSIGHT. [ABSTRACT FROM AUTHOR]

Running power is a popular measure to gauge objective intensity. It has recently been shown, though, that foot-worn sensors alone cannot reflect variations in the exerted energy that stems from changes in the running economy. In order to support long-term improvement in running, these changes need to be taken into account. We propose leveraging the presence of two additional sensors worn by the most ambitious recreational runners for improved measurement: a watch and a heart rate chest strap. Using these accelerometers, which are already present and distributed over the athlete's body, carries more information about metabolic demand than a single foot-worn sensor. In this work, we demonstrate the mutual information between acceleration data and the metabolic demand of running by leveraging the information bottleneck of a constrained convolutional neural network. We perform lab measurements on 29 ambitious recreational runners (age = 28 ± 7 years, weekly running distance = 50 ± 25 km, V ˙ O 2 max = 60.3 ± 7.4 mL · min−1·kg−1). We show that information about the metabolic demand of running is contained in kinetic data. Additionally, we prove that the combination of three sensors (foot, torso, and lower arm) carries significantly more information than a single foot-worn sensor. We advocate for the development of running power systems that incorporate the sensors in watches and chest straps to improve the validity of running power and, thereby, long-term training planning. [ABSTRACT FROM AUTHOR]

Pitching biomechanical research is highly focused on injury prevention with little attention to how biomechanical data can facilitate skill development. The overall purpose of this study was to explore how sensor-derived segment kinematics and timing relate to command and ball velocity during baseball pitching. We used a cross-sectional design to analyze a series of pitches thrown from 10 collegiate baseball pitchers. We collected biomechanical data from six inertial sensors, subjective command from the pitchers, and ball velocity from a radar device. Stepwise regression analyses were used to explore biomechanical variables associated with command for all pitches and ball velocity for fastballs only. We found that only peak forearm linear acceleration was significantly associated with command, whereas several segment kinematic measures were significantly associated with ball velocity. Our results suggest that different biomechanical variables are linked to specific pithing skills. Our findings suggest that end-effector (forearm) movement is more important for pitch command, whereas proximal-to-distal (pelvis, trunk, upper arm, forearm) segmental movement is important for ball velocity. [ABSTRACT FROM AUTHOR]

We present a theoretical model for transient analysis of the micro thermal convective accelerometer (MTCA). By leveraging this model, efficient parametric prediction can be calculated compared with time-consuming and complex CFD simulation and experimental testing. Based on the calculation results, it is recommended that a device with a thin film, a small size, and a small distance between detectors and microheater will achieve a faster frequency response, which is demonstrated by the experimental results using a serial of MTCAs fabricated with a CMOS compatible method. In addition, combined with the optimization of sensitivity, a guideline can be found that in order to improve the frequency response and sensitivity, the detector should be placed closer to the microheater when the sensor length of the device is larger. While, for the device with a smaller sensor length, detectors should be designed in the moderate region of the ratio of D/L. Except for frequency improvement, the relationship between the sensor size and sensitivity is also described and a device with an outstanding sensitivity of 7, $500~\mu \text{V}$ /g is created with a novel parallel stake method. Combining sensitivity and frequency variation with sensor size, a trade-off should be made when designing the MTCA. Above all, the recovery of this work is meaningful for the researchers to optimize their designs for the improvement of both sensitivity and frequency response. [2022-0112] [ABSTRACT FROM AUTHOR]

A simplified gravitational reference sensor (S-GRS) is an ultra-precise inertial sensor for future Earth geodesy missions. These sensors measure or compensate for all non-gravitational accelerations of the host spacecraft to remove them in the data analysis and recover spacecraft motion due to Earth's gravity field. Low–low satellite-to-satellite tracking missions like GRACE-FO that use laser ranging interferometers (LRI) are limited by the acceleration noise performance of their electrostatic accelerometers and temporal aliasing associated with Earth's gravity field. The current accelerometers, used in the GRACE missions, have a limited sensitivity of ∼ 10 - 10 m/s 2 /Hz 1 / 2 around 1 mHz. The S-GRS is estimated to be at least 40 times more sensitive than the GRACE accelerometers and over 500 times more sensitive if operated on a drag-compensated platform. This improvement is enabled by increasing the mass of the sensor's test mass, increasing the gap between the test mass and its electrode housing, removing the grounding wire used in GRACE, and replacing it with a UV LED-based charge management system. This allows future missions to take advantage of the sensitivity of the GRACE-FO LRI in the gravity recovery analysis. The S-GRS concept is a simplified version of the flight-proven LISA Pathfinder (LPF) GRS. Performance estimates are based on models vetted during the LPF flight and the expected spacecraft environment based on GRACE-FO data. The relatively low volume ( ∼ 10 4 cm 3 ), mass (∼ 13 kg), and power (∼ 20 W) enable the use of S-GRS on microsatellites, reducing launch costs and allowing more satellite pairs to improve the temporal resolution of gravity field maps. The S-GRS design and analysis, as well as its gravity recovery performance in two candidate mission architectures, are discussed in this article. [ABSTRACT FROM AUTHOR]

Early drone anomaly inspection is vital to ensure the drone's safety and effectiveness. This process is often overlooked, especially by amateur drone pilots; however, some faulty conditions are difficult to notice by the naked eye or discover, even though the drone inspection process has been conducted; therefore, a real-time early drone inspection approach based on vibration data is proposed in this study. Firstly, the reliability of several microelectromechanical systems (MEMS) sensors, namely the ADXL335 accelerometer, ADXL 345 accelerometer, ADXL377 accelerometer, and SW420 vibration sensor in detecting faulty conditions, were tested and compared. The experimental results demonstrated that the vibration parameter measured using ADXL335 and ADXL345 accelerometers are the best choice as most of the faulty conditions can be detected, in contrast to other MEMS sensors. The output produced from the anomaly inspection algorithm is then converted to the "Healthy" or "Faulty" state, which is displayed in a mobile application for easy monitoring. [ABSTRACT FROM AUTHOR]

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm. [ABSTRACT FROM AUTHOR]

Like gravitational wave detection, inter-spacecraft geodesy is a measurement of gravitational tidal accelerations deforming a constellation of two or more orbiting reference test masses (TM). The LISA TM system requires TM in free fall with residual stray accelerations approaching the fm/s 2 /Hz 1 / 2 level in the mHz band, as demonstrated in the LISA Pathfinder "Einstein's geodesic explorer" mission. Current geodesy missions are limited by accelerometers with 100 pm/s 2 /Hz 1 / 2 level, due to intrinsic design limitations, as well as the challenging low Earth orbit environment and operating conditions. A reduction in the TM acceleration noise could lead to an important improvement in the scientific return of future geodesy missions focusing on mass change, especially in a scenario with multiple pairs of geodesy satellites. We present here a preliminary assessment of how the LISA TM system, known as the "gravitational reference sensor" (GRS), could be adapted for use in future geodesy missions aiming at residual TM accelerations noise at the pm/s 2 /Hz 1 / 2 level, addressing the major design issues and performance limitations. We find that such a performance is possible in a geodesy GRS that is simpler and smaller than that used for LISA, with a lighter, sub-kg TM and gaps reduced from 4 mm to less than 1 mm. Acceleration noise performance limitations will likely be closely tied to the required levels of applied actuation forces on the TM. [ABSTRACT FROM AUTHOR]

Wireless sensor networks usually suffer from the issue of time synchronization discrepancy due to environmental effects or clock management collapse. This will result in time delays between the dynamic responses collected by wireless sensors. If non-synchronized dynamic response data are directly used for structural modal identification, it leads to the misestimation of modal parameters. To overcome the non-synchronization issue, this study proposes a time synchronization approach to detect and correct asynchronous dynamic responses based on frequency domain decomposition (FDD) with frequency-squeezing processing (FSP). By imposing the expected relationship between modal phase angles extracted from the first-order singular value spectrum, the time lags between different sensors can be estimated, and synchronization can be achieved. The effectiveness of the proposed approach is fully demonstrated by numerical and experimental studies, as well as field measurement of a large-span spatial structure. The results verify that the proposed approach is effective for the time synchronization of wireless accelerometer sensors. [ABSTRACT FROM AUTHOR]

As one type of the sensors, piezoelectric accelerometer may transform the acceleration signal into the electric signal, which are widely utilized for measurement, monitoring, or controlment. The mostly utilized piezoelectric materials are piezoelectric ceramics [e.g., PZT, ((1 – x)Pb(Mg1/3Nb2/3)O3 – xPbTiO3) (PMN-PT)] for their large piezoelectric stress constant (${d}_{{33}}$). However, their high dielectric permittivity ($\varepsilon _{r}$) leads to the relatively small piezoelectric voltage constant (${g}_{{33}}$), thus the low response sensitivity. In an effort to meet the demand of sensors with high sensitivity, low weight, and small size, in this work, a series of poly(vinylidene-trifluoroethylene) (P(VDF-TrFE)) films are annealed at elevated temperature and fabricated into sensors. Thanks to its rather low permittivity, P(VDF-TrFE) sensor exhibits much larger ${g}_{{33}}$ and sensitivity than that of PMN-PT. Its sensitivity closely depends on the annealing temperature and film thickness. On a hand-made touch sensing instrument, about 10%–20% larger output voltage is obtained in P(VDF-TrFE) sensor than that of PMN-PT under the same stimulating force, although the thickness of P(VDF-TrFE) is only one twenty-fifth of PMN-PT. The results show the excellent piezoelectric sensing performance and great commercial potential of P(VDF-TrFE) for not only its excellent sensitivity but also the feasible processability for thin film in large scale. [ABSTRACT FROM AUTHOR]

This paper presents a power efficient, low delay and rate adaptive dual chamber pacemaker (PLRDPM) using heart rate and accelerometer sensor. In recent years, number of modifications have been done in the pacemaker design. However, design of an implantable device on an open source is still challenging. Through this paper, we are proposing a "proof of concept" for the design of PLRDPM on FPGA for improving the vital parameters: delay and power consumption. The proposed PLRDPM comprises of accelerometer and heart rate sensors to measure physical activity's effect on heart rate of the bradycardia patients. A rate adaptive pacing algorithm has been designed using two sensor's data, to reduce the delay and power consumption. However, delay in the responses of various components in the circuitry produces an accumulative delay effect in any practical circuit. The delay and the power consumption for the proposed PLRDPM are found to be 2.82 ns and 9 mW, respectively. [ABSTRACT FROM AUTHOR]

• OC-SVM and CNN are compared for knock detection in a heavy-duty engine. • The STFT of the knock sensor signal is used as the main input in both methods. • The effect of various sensor configurations over each method is analysed. • The classification performance of each method is discussed. Knock detection is critical to engine control as it prevents damage and ensures optimal performance. However, it still presents significant challenges, particularly in alternative combustion systems, due to its complex nature. This study compares two machine learning (ML) approaches (supervised and unsupervised) for detecting knock in a heavy-duty engine, using various knock-sensor setups. It focuses on using accelerometer data as the primary input, a widely used on-road sensor whose compatibility with ML approaches requires further exploration. The analysis includes One-Class Support Vector Machine (OC-SVM) and Convolutional Neural Networks (CNN). Both methods demonstrated their ability to detect engine knock effectively, achieving sensitivity levels over 80% compared to MAPO (maximum amplitude pressure oscillation) index. The sensor placement revealed different effects over the results according to the method used, whereas the number of sensors showed a minor influence on the outcomes. The advantages and disadvantages of each method are discussed. [ABSTRACT FROM AUTHOR]

This study proposes the use of a high-speed camera as a holographic visual sensor to obtain the dense full-field dynamic parameters of the main beam of a bridge by the field of view through uniaxial rotation photography. Based on the basic principle that the frequency and mode of a structure are inherent characteristics, the mode coordinates obtained from each field of view are unified, normalized, and matched according to the same name pixels to obtain the dense full- field dynamic parameters of the entire bridge. The frequency and first three order modes of a self-anchored suspension test bridge are collected by the method proposed in this study. The frequency comparison between the accelerometers and dial gauges is within 3%, and the mode shapes are more holographic and more realistic than those obtained by limited measuring points. In addition, the difference in the curvature mode under various damage conditions obtained by limited measurement points is compared with that obtained by the method proposed in this study. Results shows that the dense full-field modal curvature difference can reflect the change in the damage location even in a low order, which means the sensitivity of the change of damage location in low-order modal. [ABSTRACT FROM AUTHOR]

In this paper, a liquid-based micro thermal convective accelerometer (MTCA) is optimized by the Rayleigh number (Ra) based compact model and fabricated using the 0.35 μm CMOS MEMS technology. To achieve water-proof performance, the conformal Parylene C coating was adopted as the isolation layer with the accelerated life-testing results of a 9-year-lifetime for liquid-based MTCA. Then, the device performance was characterized considering sensitivity, response time, and noise. Both the theoretical and experimental results demonstrated that fluid with a larger Ra number can provide better performance for the MTCA. More significantly, Ra based model showed its advantage to make a more accurate prediction than the simple linear model to select suitable fluid to enhance the sensitivity and balance the linear range of the device. Accordingly, an alcohol-based MTCA was achieved with a two-order-of magnitude increase in sensitivity (43.8 mV/g) and one-order-of-magnitude decrease in the limit of detection (LOD) (61.9 μg) compared with the air-based MTCA. [2021-0092] [ABSTRACT FROM AUTHOR]

The use of inertial sensors in fast bowling analysis may offer a cheaper and portable alternative to current methodologies. However, no previous studies have assessed the validity and reliability of such methods. Therefore, this study aimed to assess the validity and reliability of collecting tibial accelerations and spinal kinematics using inertial sensors during in vivo fast bowling. Thirty-five elite male fast bowlers volunteered for this study. An accelerometer attached to the skin over the tibia was used to determine impacts and inertial sensors over the S1, L1 and T1 spinous processes used to derive the relative kinematics. These measurements were compared to optoelectronic and force plate data for validity analysis. Most acceleration and kinematics variables measured report significant correlations > 0.8 with the corresponding gold standard measurement, with intraclass correlation coefficients greater than 0.7. Low standard error of measurement and consequently small minimum detectable change (MDC) values were also observed. This study demonstrates that inertial sensors are as valid and reliable as current methods of fast bowling analysis and may provide some advantages over traditional methods. The novel metrics and methods described in this study may aid coaches and practitioners in the design and monitoring of fast bowling technique. Graphical abstract illustrating the synopsis of the findings from this paper. [ABSTRACT FROM AUTHOR]

Wearable sensor systems are a emerging tools for the evaluation of the sport's activity and can be used to quantify the external workload of the athlete. The main goal of this paper was to evaluate the validity and reliability of the "Armbeep inertial measurement unit" (IMU) sensor both in a closed tennis exercise and in open matchplay. Twentyfour junior tennis players performed a baseline drill and played matches, during which they wore a combined accelerometer and gyroscope sensor. Video footage was concomitantly recorded using a digital video camera. The agreement between the measurements was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). A simple linear regression was used to predict the number of shots registered from the video and from the Armbeep IMU sensor's data. The number of total forehand and backhand shots during the drill repetitions showed an excellent test and re-test reproducibility (ICC≥0.90). There was a significant relationship between the Armbeep IMU sensor's number of contacts and the total number of shots (R2 = 0.938) which indicated the excellent reliability of the tested Armbeep IMU sensor for those parameters. Considering the accuracy of the total tennis shots and the small magnitude of error for wrist speed and acceleration, the Armbeep IMU sensor appears to be an appropriate on-court tool that can be used to monitor the hitting load during tennis practice and matches. [ABSTRACT FROM AUTHOR]

Attitude information obtained from low-cost magnetic, angular rate, and gravity (MARG) sensors is prone to be deteriorated by disturbances such as external acceleration and magnetic anomaly in operational environments. To address the problem, a robust attitude estimation algorithm using low-cost MARG sensors is proposed. An error state Kalman filter (ESKF) is used for data fusing, in which the attitude errors are interpreted as a small rotation vector, and the gyro bias variation is also estimated. The nominal state and the error state are propagated with unit quaternion and rotation vector, respectively. An attitude correction is followed which combines the nominal and error parts by quaternion multiplication. The robustness of the proposed algorithm is embodied in resisting accuracy degradation of the attitude estimation caused by both external acceleration and magnetic anomalies. While external accelerations and magnetic disturbances are detected online, the accuracy reduction in attitude estimation is prevented by adaptively adjusting the related measurement noise covariance matrix. Besides, a two-step measurement update strategy is designed to guarantee that the roll and pitch update is separated from the yaw update. Various rotation and land vehicle dynamic tests have been conducted to validate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Self-calibrating an array of 3-D field sensors, such as three-axis magnetometers and accelerometers, requires estimation of two variable sets—each sensor’s intrinsic model that maps its input field to the corresponding measurement and each sensor’s coordinates relative to a common frame of reference within the array. In this work, we propose the first unified self-calibration method for arrays of same-type 3-D field sensors, which is robust to anomalous sensor measurements unlike previous algorithms. The method breaks down the array calibration task into three steps of more easily subproblems, first estimating the intrinsic variables of each sensor independently, second computing the sensor coordinates with respect to a common reference frame, and last refining both these intrinsics and orientations jointly to minimize physically meaningful sensor estimation errors. Each stage has been carefully designed to maintain robustness to anomalies without compromising estimation quality. The performance of our method is compared against other state-of-the-art algorithms on both simulation and real data from a magnetometer array and accelerometer array, demonstrating significant improvements in accuracy and precision of the estimated array variables in versatile real-world self-calibration environments. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT), people’s demand for location-based services is increasingly urgent. Based on magnetometer, accelerometer, and rate gyro, i.e., MARG sensors, the position and attitude estimation methods such as complementary filter (CF), Kalman filter (KF), and their various modifications have been the research hot spot. However, the CF-based methods are empirical and lack robustness; the KF-based methods are memory-free observers, whose solution may diverge when the filter lacks uniform observability. In this article, a virtual-measurement-combined extended KF (VMC-EKF) method is proposed by fusing the carrier’s motion state with EKF method. Similar to the graph optimization, the proposed method can measure the key information in VMC phase, and thus remove the requirement of uniform observability. The number of virtual measurements can be estimated based on carrier’s motion state and its gradient, which determines the number of iterations of the prediction phase and the correction phase. In order to verify the performance of the proposed method, a series of numerical simulation experiments, turntable experiments, and foot-mounted experiments are carried out. The corresponding testing platform is set up based on MPU9250, which is a typical and low-cost motion tracking integrated circuit (IC) of MARG sensors. The raw data of MARG sensors can communicate with the host computer via wired or wireless communication, and then be imported into MATLAB for processing and analysis by the compared methods. The test results show that the proposed method can achieve fast convergence of attitude estimation and avoid the divergence of position estimation compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The article presents the possibilities of using popular MEMS inertial sensors in the object tilt angle estimation system and in the system for stabilizing the vertical position of the balancing robot. Two research models were built to conduct the experiment. The models use microcontroller development board of the STM32F3 series with the Cortex-M4 core, equipped with a three-axis accelerometer, magnetometer and gyroscope. To determine the accuracy of the angle estimation, comparative tests with a pulse encoder were performed. [ABSTRACT FROM AUTHOR]

Accelerometers are indispensable for detecting accelerating forces in automotive electronic systems. Although several accelerometers have been developed, they are still unstable for vehicle dynamics’ applications in the low-frequency range (0–20 Hz). Here, we report a novel type of accelerometer based on a coiled carbon nanotube (CNT) yarn as a self-powered and low-frequency range-covered acceleration sensor. The proposed sensor is designed in a compact fiber-like structure for practical applications. Open-circuit voltage (OCV) signals are consistently generated during the stretch-and-release process of the coiled CNT yarn by the applied sinusoidal accelerations, and the OCV changes increase linearly with increasing acceleration from 4.84 to 48.37 m/s2. Our accelerometer exhibits excellent dynamic sensing performance in the low-frequency range compared with commercial accelerometers. In an application as a CNT yarn device configured with a mass load, the OCV change is linearly proportional to the applied acceleration. When our accelerometer is attached to a seatbelt in a vehicle, it generates OCV changes from the movement of the body mass underlying a certain acceleration change. Given its excellent sensing performance, the CNT yarn acceleration sensor could further be developed for practical applications, such as seatbelts and car seats with fabric and textile. [ABSTRACT FROM AUTHOR]

The Virtual Point Transformation (VPT) makes it possible to experimentally identify the full DoF FRF matrix by projecting the measured displacements onto the Interface Deformation Modes (IDMs). The VP FRFs were already successfully used in Frequency-Based Substructuring (FBS); however, the VPT is susceptible to deviations in the impact location and orientation, as well as to deviations in the sensor's sensitivity and positioning. Uncertainties associated with the sensors can be decreased by using the expanded VPT. This expanded VPT allows the projection of a directly measured rotational response onto the Interface Deformation Modes (IDMs). The consistency of the transformation is achieved by using a rotational weighting matrix, which is formulated to minimize the norm of the overall displacements due to the rotational residual at the VP for each rotational sensor. The rotational response is measured using a direct piezoelectric rotational accelerometer. In this paper the application of the expanded virtual point transformation and the possible advantages are explored on a complex and engineering-like test structure. Both transformations, standard and expanded, are performed for each VP to enable a side-by-side comparison. [ABSTRACT FROM AUTHOR]

• This study develops an accelerometer based on an FBG sensor with a novel structure. • It is suitable for high acceleration and medium frequency measurements. • It can measure accelerations appearing from two directions of one-dimensional space. • It exhibits good sensitivity and linearity in response to random acceleration. • The sensor structure is 3D printed, inheriting many outstanding properties. This study aims to develop a shock accelerometer that can measure signals from two directions in one-dimensional space for medium frequency measurement applications and high acceleration range. A fiber Bragg grating (FBG) sensor is attached to a structure fabricated using Fused Deposition Modeling technology (FDM) to achieve this. The finite element method is utilized to observe the sensor's operating mechanism and harmonic response, as well as to demonstrate the accelerometer's customizability. The sensor's design enables it to detect acceleration resulting from shock motion on both sides (+X and –X direction) of the Hopkinson bar system. When the elastic structure deforms, the wavelength inside the FBGs' core changes. During the test with different random accelerations, FBG1 showed a total Bragg wavelength shift of 6.403 nm within the 450 g to 1800 g range, while FBG2 showed a total shift of 2.033 nm within the 330 g to 1200 g range. According to the findings of the linearity analysis conducted on the proposed sensor, the linear values of the data obtained from FBG1 were over 96%, while those from FBG2 were close to 94%. In addition, the sensor's harmonic response was examined, and it was recommended that the sensor be operated within a frequency range of 20 to 600 Hz, with the resonant frequency being 660 Hz. The sensor's amplitude anti-interference is also considered negligible when the horizontal noise signal relative to the working axis signal is less than 3% in both directions. These findings suggest that the proposed accelerometer has great potential for various applications. Furthermore, the proposed FBG accelerometer sensor utilizes 3D printing technology, which offers several advantages, including rapid fabrication time, reduced production costs, and high customizability. [ABSTRACT FROM AUTHOR]

Fisheries management is generally based on the regulation of the fishing effort, by limiting fishing capacity and activity. The fishing capacity can be quantified objectively, however, the calculation of the fishing activity requires knowing the effective fishing time, for which it is essential to monitor the vessels activity. The European Vessel Monitoring System (VMS) only describes the geographical position, course and speed of the vessel at 2-h intervals, it is an expensive system used only in vessels over 12 m in length and there are no common criteria to infer the fishing activity from the VMS data. To evaluate more precisely the fishing activity, we propose to incorporate new sensors in the vessels that provide additional information. The sensors of mobile devices offer an economic solution that would allow their implementation throughout the fishing fleet. The objective of this work is to evaluate whether the most common sensors integrated in current mobile devices: GPS, accelerometer, gyroscope, and magnetic field, offer relevant information to identify the different phases of bottom trawling fishing activity. The results obtained indicate that these sensors detect, with very high precision, foreseeable changes in the movement of the vessel during the towing manoeuvre. [ABSTRACT FROM AUTHOR]

Smartphones are an indispensable tool in modern day-to-day life. Their widespread use has spawned numerous applications targeting diverse domains, such as biomedical, environment sensing, and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable since these devices are not originally designed for high-accuracy sensing purposes. In this article, we investigate the accuracy limits of one of the commonly used sensors, namely, smartphone accelerometer. We focus on the efficacy of a smartphone as an acceleration measuring device, rather than focusing only on the accuracy of its internal accelerometer chip. This holistic approach includes additional errors that arise from the device operating system, such as sampling time uncertainty. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors represented by a white Gaussian process. The model is validated experimentally using shake table experiments, and the maximum likelihood estimation (MLE) is used to estimate the sampling time uncertainty standard deviation. Moreover, we derive the Cramer–Rao lower bound (CRLB) of acceleration estimation based on the proposed model. [ABSTRACT FROM AUTHOR]

Modern navigation systems integrate the global positioning system (GPS) with an inertial navigation system (INS), which complement each other for correct attitude and velocity determination. The core of the INS integrates accelerometers and gyroscopes used to measure forces and angular rate in the vehicular inertial reference frame. With the help of gyroscopes and by integrating the acceleration to compute velocity and distance, precision and compact accelerometers with sufficient accuracy can provide small‐error location determination. Solid‐state implementations, through coherent readout, can provide a platform for high performance acceleration detection. In contrast to prior accelerometers using piezoelectric or capacitive readout techniques, optical readout provides narrow‐linewidth high‐sensitivity laser detection along with low‐noise resonant optomechanical transduction near the thermodynamical limits. Here an optomechanical inertial sensor with an 8.2 µg Hz−1/2 velocity random walk (VRW) at an acquisition rate of 100 Hz and 50.9 µg bias instability is demonstrated, suitable for applications, such as, inertial navigation, inclination sensing, platform stabilization, and/or wearable device motion detection. Driven into optomechanical sustained‐oscillation, the slot photonic crystal cavity provides radio‐frequency readout of the optically‐driven transduction with an enhanced 625 µg Hz−1 sensitivity. Measuring the optomechanically‐stiffened oscillation shift, instead of the optical transmission shift, provides a 220× VRW enhancement over pre‐oscillation mode detection. [ABSTRACT FROM AUTHOR]

Smart phones and tablets have turned into an indispensable piece in our society. Accordingly, these devices are equipped with many sensors that can be exploited in many applications such as monitoring road surface condition. It becomes important to find an optimal method for detecting road speed bumps and potholes in order to monitor the road conditions and to alert other moving vehicles in order to slow their speed and avoid any damage and experience safe and comfortable driving. Moreover, data collected by smartphones about road conditions can help city authorities to repair and maintain the roads. The aim of this paper is to introduce a model for detecting road speed bumps and potholes using an Android application based on accelerometer sensor. This application uses data gathered by accelerometer sensor that was collected from various roads in order to determine the accuracy of this proposed approach. The proposed model was implemented as an Android application as a proof of concept and it was tested to check its accuracy. [ABSTRACT FROM AUTHOR]

This work is based on an already developed electronic system, comprised by inertial sensors, without magnetometer. This is unable to provide an orientation measure that allows obtaining gait parameters, since the signals to gather from it are: angular velocity and linear acceleration. To add this functionality to the system, several algorithms developed in the literature were analyzed and implemented. A comparison between the previous implementations is made from experimental signals. These signals are registered from rotations with different values of angle with respect to the 3 coordinate axes. Among the algorithms consulted are the straight integration of the gyroscope signal and the fusion of sensors. In this context, some problems were found related to the way to obtain the angles from the output signals of the accelerometer and the gyroscope. Therefore, in this work it is proposed an implementation, based on the Kalman filter, to obtain the orientation without using a magnetometer and that minimizing the deviation produced by the integration of the gyroscope signal. This algorithm was applied to dynamic equilibrium tests that are carried out in the health institution. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD.Methods: Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis.Results: First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set.Conclusions: Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices. [ABSTRACT FROM AUTHOR]

This paper is a nontrivial extension of prior work on rate and position table leveling and inertial and magnetic sensors calibration techniques. The table leveling is essential for a class of calibration techniques using known stimuli, herein referred to as classical techniques. Such techniques are effective for the calibration of inertial sensors as they usually lead to closed-form solutions and body frame calibrations. However, the classical techniques are negatively affected by table leveling and azimuth orientation errors or by changes in the direction of the reference field. These errors are collectively referred to as field alignment errors in this paper. The impact of field alignment errors is usually avoided by using reference-free calibrations which rely on satisfying the invariance of the field modulus constraint. This constraint works well for orthogonal sensor triads as it leads to unique solutions. There is a great body of work dedicated to studying such calibration techniques for orthogonal sensor triads. However, redundant sensor configurations have too many degrees of freedom to satisfy the invariance of the modulus constraint; therefore, require special techniques. This paper introduces a novel approach that can be used to produce body frame calibrations for skew redundant sensor configurations in the presence of field alignment errors. Unlike prior work, no simplifying assumptions are made on the redundant sensor configuration (e.g., multiple orthogonal triads). The new method is showcased for the dodecahedron configuration, one of the most complex skew redundant configurations. [ABSTRACT FROM AUTHOR]

• Inertial measurement units are reasonably accurate for standing balance evaluation. • Accelerometers are accurate and recommended for standing balance evaluation. • Use of accelerometers eliminates the need for complex sensor fusion algorithms. • Use of accelerometers reduces power-consumption for I-o-T clinical research. • Our methodology enables in-field evaluation of standing balance. Observational balance tests (e.g., Berg Balance Scale) are used to evaluate fall-risk. However, they tend to be subjective, and their reliability and sensitivity can be limited. The use of in-lab equipment for objective balance evaluation has not been common in clinical practice, due to the requirement of an equipped lab space. While inertial measurement units (IMUs) enable objective out-of-lab balance assessment, their accuracy has not been validated. This study aims to investigate the accuracy of IMUs against in-lab equipment for characterizing standing balance. Ten non-disabled individuals participated in a two-minute standing test on a force-plate. Four approaches were used for estimating inter-segmental moments and center of pressure (COP) position in a four-segment model: (1) camera-based bottom-up approach; (2) camera-based top-down approach; (3) IMU-based (accelerometer) top-down approach; and (4) IMU-based (accelerometer and gyroscope) top-down approach. Approaches 2 to 4 resulted in high accuracy compared to the reference, Approach 1. The root-mean-square errors in estimating the segments' orientation, ground reaction forces, COP position, and joint moments were smaller than 0.3°, 0.2 N/kg, 1.5 mm, and 0.016N·m/kg, respectively. Since no significant differences were observed between the accuracy of Approaches 3 and 4, only accelerometer recordings are needed and could be recommended for monitoring standing balance. [ABSTRACT FROM AUTHOR]

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an Internet of things network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to the United States federal agencies standards. With this information, the building inventory can be classified into immediate occupancy, life safety, or collapse prevention categories. In this paper, we propose a zero velocity update technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy. [ABSTRACT FROM AUTHOR]

The industrial progress has reached a level in which it is necessary to understand the behavior of mechanical components and to monitor their conditions without disassembling them. Nowadays, a suitable methodology is based on vibrational analysis usually performed through acceleration signals measured directly on the system to be tested. However, in the last years, the industrial scenario has deeply changed due to the need for time reduction, in particular, for the control operations at the end of the productive line. The genuine methods based on acceleration measurements, for example, through piezoelectric accelerometers, came into conflict with the industrial need as the sensors used for the quality control have to be easily and fastly mounted and unmounted. A valid alternative is represented by the exploitation of laser triangulation sensors that are able to measure the dynamic displacement in a contactless way, strongly reducing the (un)mounting time. The target of this paper is to highlight pros and cons of the contactless displacement analysis through laser triangulation sensors with respect to the contact one through genuine accelerometers by means of a comparison between the results obtained both for experimental modal analysis and vibrational diagnostics of rotating machines. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in hand movements in various application fields. However, the human hand is too dexterous and only has limited space. Developing a proper method to monitor and capture hand motion is meaningful. Conventionally, an optical measurement method is widely used to capture hand gestures. Nevertheless, this method is costly and has the problem of line-of-sight shielding. In this paper, a sensor fusion algorithm is proposed to calculate a more accurate result by calculating acceleration, magnetic field strength, and angular speed from the inertial measurement unit (IMU). The core concept of this algorithm is based on the idea of feedback control. To validate the performance, the active rotary platform is set up to compare the measured results to the references directly. Most of the error results are less than 3°, and the standard deviations are less than 1°. The measurement results ensure the feasibility of the inertial measurement method with the sensor fusion algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration. The proposed algorithm utilizes the majorization-minimization (MM) algorithm and the duality principle to find the optimal configuration. Unlike the state-of-the-art approaches which are mainly geometrical in nature and restricted to sensors’ noise being uncorrelated, the proposed algorithm gives the exact orientations of the sensors and can easily deal with the cases of correlated noise. The proposed algorithm has been implemented and tested via numerical simulation in the MATLAB. The simulation results show that the algorithm converges to the optimal configurations and shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Objective: Measuring three-dimensional (3-D) foot trajectories with foot-worn inertial measurement units (IMUs) is essential for a variety of applications, such as gait analysis and fall risk assessment. IMU-based foot trajectory is usually reconstructed by double integrating the global coordinate acceleration, in which drifts of signals are accumulated and lead to unbounded error increase. To reduce drift errors, a smoother-based method is proposed in this paper. Methods: The smoother-based method not only corrects initial values of integrations, but also smooths integrating processes through a backward update. Both the orientation estimation and the velocity estimation are improved in this concept, which contribute to the improvement of the trajectory estimation. Results: The final results are compared with an optical motion capture system as reference. Accuracy is evaluated with ground level walking of nine adult participants, 2302 strides in total. Errors are reduced by 62% on stride length and 44% on stride width of the estimation without our method, with final errors $-\text{0.24}\pm \text{1.11}\ \text{cm}$ and $-\text{0.02}\pm \text{0.95}\ \text{cm}$. Conclusion/Significance: Results prove that our method can improve the accuracy of 3-D foot trajectory estimation. Furthermore, this smoother-based method can reduce drift-related errors when estimating trajectories, which will allow it to expand its applications into other IMU-based measurements. [ABSTRACT FROM AUTHOR]