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Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU's location has on running data, a reference IMU was 'correctly' placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was 'misplaced' 0.05 m away, simulating a 'worst-case' misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to -10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

Mass-spring is one of the most used structures in sensors and actuators of MEMS technology, including accelerometers, gyroscopes, harvesters, etc. Some of the major design parameters of the structure are natural frequency and displacement sensitivity. The limitations of microfabrication processes should be considered in the design process. The optimized geometry topology can reduce the area occupied by the structure in the fabrication and increases the number of possible structures per wafer. This study aims to provide a novel systematic design and optimization of a MEMS mass-spring system by using a dimensionless design flowchart. Based on the dimensionless design, this algorithm provides the designer with the optimized dimensions of the mass-spring by following the provided dimensionless diagrams, taking into account stress, displacement sensitivity, natural frequency, and fabrication limitations. Also, the optimized dimensions of the mass-spring system are obtained using the dimensionless design flowchart with respect to the desired natural frequency and displacement sensitivity. In addition, genetic algorithms and dimensionless diagrams present a procedure to minimize the footprint of the structure while keeping the desired natural frequency and displacement sensitivity. Results demonstrate that up to a 3% change in both natural frequency and displacement sensitivity, leads to a 45% reduction in the structure area compared to the related literature of MEMS accelerometers. [ABSTRACT FROM AUTHOR]

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance. [ABSTRACT FROM AUTHOR]

This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because they are both autonomous and passive at the same time and are therefore very attractive. Their calibration and systematic errors or bias are known problems, briefly discussed in the article due to their importance, and are relatively simple to solve. However, problems related to the accumulation of these errors over time and their autonomous and dynamic correction remain. This article proposes a solution to the problem of IMU tilt calibration, i.e., the pitch and roll and the accelerometer bias correction in dynamic conditions, and presents the process of calculating these parameters based on combined accelerometer and gyroscope records using a new approach based on measuring increments or differences in tilt measurement. Verification was performed by simulation under typical conditions and for many different inertial units, i.e., IMU devices, which brings the proposed method closer to the real application context. The article also addresses, to some extent, the issue of navigation, especially in the context of dead reckoning. [ABSTRACT FROM AUTHOR]

This paper presents a non-invasive methodology for structural health monitoring (SHM) integrated with inertial sensors and signal conditioning techniques. The proposal uses the signal of an IMU (inertial measurement unit) tri-axial accelerometer and gyroscope to continuously measure the displacements of a structure in motion due to seismic vibrations. A system, called the "Inertial Displacement Monitoring System" or "IDMS", is implemented to attenuate the signal error of the IMU with methodologies such as a Kalman filter to diminish the influence of white noise, a Chebyshev filter to isolate the frequency values of a seismic motion, and a correction algorithm called zero velocity observation update (ZVOB) to detect seismic vibrations and diminish the influence of external perturbances. As a result, the IDMS is a methodology developed to measure displacements when a structure is in motion due to seismic vibration and provides information to detect failures opportunely. [ABSTRACT FROM AUTHOR]

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs. [ABSTRACT FROM AUTHOR]

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human–computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human–computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials. [ABSTRACT FROM AUTHOR]

The article describes the on-board control-measurement system designed to record the main flight parameters of a micro-convertiplane type unmanned aerial vehicle and the element base used for this purpose. The methodology of conducting tests in stationary mode through the on-board control-measurement system was developed and tests were carried out. The engine's diagnostic check and condition assessment has been performed using the on-board control-measuring system. The possibility of on-ground check of the micro unmanned aerial vehicles power elements before and after the flight, as well as the possibility of monitoring the flight events that may occur in the air during the flight has been shown. The data on the power consumption and temperature of each engine in the stationary mode, the direction of the aircraft axes and the velocity vector have been recorded by mean of the developed on-board control-measurement system, as well as time dependence diagrams of the corresponding parameters have been built in comparative order based on the obtained values. A methodology has been developed for the diagnostics of the engine operation of the micro-unmanned aerial vehicle before and during the flight. It has been shown that the temperature, power consumption and number of revolutions of the engines included in the power system of the aircraft, as well as the aircraft axes and velocity vector direction data is recorded during the flight, by mean of the on-board control-measurement system designed to collect data on the flight-technical parameters of the convertiplane-type micro-unmanned aerial vehicles. [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]

This paper characterizes the sensitivity of a time domain MEMS accelerometer. The sensitivity is defined by the increment in the measured time interval per gravitational acceleration. Two sensitivities exist, and they can be enhanced by decreasing the amplitude and frequency. The sensitivity with minor nonlinearity is chosen to evaluate the time domain sensor. The experimental results of the developed accelerometer demonstrate that the sensitivities span from −68.91 μs/g to −124.96 μs/g and the 1σ noises span from 8.59 mg to 6.2 mg (amplitude of 626 nm: −68.91 μs/g and 10.21 mg; amplitude of 455 nm: −94.51 μs/g and 7.76 mg; amplitude of 342 nm: −124.96 μs/g and 6.23 mg), which indicates the bigger the amplitude, the smaller the sensitivity and the bigger the 1σ noise. The adjustable sensitivity provides a theoretical foundation for range self-adaption, and all the results can be extended to other time domain inertial sensors, e.g., a gyroscope or an inclinometer. [ABSTRACT FROM AUTHOR]

Featured Application: The potential use of presented methods for the selection of appropriate sensors for measurement systems and synthesis of measurement algorithms. The establishment of the application possibilities, limitations, and appropriate operating conditions of existing measurement systems. MEMS (micro-electro-mechanical system) gyroscopes and accelerometers are used in several applications. They are very popular due to their small size, low price, and accessibility. The design of MEMS accelerometers enables the measurement of vibrations, with frequencies from tenths of hertz to even 1 kHz. MEMS gyroscopes can be applied to measure angular rates, and indirectly also angular oscillations with frequencies similar to accelerometers. Despite significant stochastic errors, MEMS sensors are used not only in popular domestic appliances (e.g., smartphones) but also in safety-critical units, such as aeronautical attitude and heading reference systems (AHRSs). In engineering, methods of stochastic properties analysis are important tools for sensor selection, verification, and the design of measurement algorithms. In this article, three methods used for the analysis of stochastic properties of sensors are presented and comparative analyses are shown. The applied measurement frequencies (1 kHz) were much higher than those typically found in MEMS sensor applications. Additionally, an exemplary analysis of temperature drift frequency, as well as the possibility for the synthesis of complementary filter parameters with the use of the described methods, is shown. Assessment of the stochastic properties of MEMS accelerometers and gyroscopes was performed under both constant and variable temperature conditions (during warm-up after switching on) with the use of classic methods, such as power spectral density (PSD) and Allan variance (AV), as well as the less known but very promising generalized method of wavelet moments (GMWM). [ABSTRACT FROM AUTHOR]

In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F 1 -scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food. [ABSTRACT FROM AUTHOR]

The main advantage of an atomic accelerometer when compared to a classical accelerometer is negligible bias drift, allowing for stable long-term measurements, which opens the potential application in navigation. This negligible drift arises from the fact that the measurements can be traced back to natural constants, and the system is intrinsically stable due to the simple design. In this manuscript, we extend this property of long-term stability to gyroscopic measurements by considering an array of atomic accelerometers, and comparing the performance to atomic gyroscopes, which are technologically more prone to bias drifts. We demonstrate that an array consisting of four three-axis atomic accelerometers can outperform state-of-the-art atomic gyroscopes with respect to long-term stability. [ABSTRACT FROM AUTHOR]

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, human activity recognition (HAR) has received significant interest in industrial and academic research due to the widespread sensor deployments like accelerometers and gyroscopes, in products such as smartphones and smartwatches. HAR is utilized in different applications where valuable information about an individual's functional ability and lifestyle is needed namely human computer interaction (HCI), military, healthcare, surveillance, etc. The recent advances of machine learning (ML) and deep learning (DL) models pave a way for the effective design of HAR under restricted experimental setup and adequate training dataset. Therefore, this study develops a new cuckoo search with deep belief network based HAR and classification, named CSODBN-HAR model. The proposed CSODBN-HAR model mianly classifies six different types of human activities such as standing, sitting, lying, walking, walking upstairs, and walking downstairs. The presented CSODBN-HAR model involves standard scalar approach as a pre-processing step. In addition, the DBN model was utilized for the recognition and classification of human activities. Besides, the CSO algorithm is applied for the hyperparameter tuning of the DBN model so that the recognition rate gets considerably enhanced, shows the novelty of the work. A wide range of experimental result analyses is conducted using benchmark dataset and the outcomes are assessed under different aspects. Extensive comparative results stated the superior outcomes of the CSODBN-HAR model over the current state of art HAR systems. [ABSTRACT FROM AUTHOR]

In mechatronic-related applications, estimating orientation from a magnetic, angular rate, and gravity (MARG) sensor array is a significant topic. Representing attitude orientation is a well-known topic in the aerospace industry, where it plays a critical role in airplanes and unmanned aerial vehicles (UAVs), but it has also gained relevance in other sectors. However, most of the sensors utilized are quite expensive, heavy, and large, making them unsuitable for modest applications. This paper examines the performance of several sensors in low-cost hardware and high-acceleration environments. A theorical method was adopted to estimate Euler angles by using accelerometer, gyroscope and magnetometer, and a robust and easy to implement method calibration was proposed to calibrate the MARG sensor without any external equipment. An experimental verification of the proposed calibration method was completed. The experimental results are then interpreted to provide an insight to advantages and disadvantages for using each sensor separately. [ABSTRACT FROM AUTHOR]

The effective operation of surface electromyography (sEMG) signal-based controlled active prostheses and humanmachine interaction systems in daily life is crucial to work with high accuracy in different angles and positions of the arm. In this study, sEMG recordings from three different positions and angles of the arm were combined with accelerometer and gyroscope data to classify four different hand movements. The classification data (8-channel sEMG, accelerometer, and gyroscope) were collected from the right forearm of 13 participants. To create the dataset, six features were extracted from sEMG signals and three from accelerometer and gyroscope data. As a result, the methodological investigation was carried out on how different arm positions and angles affect the classification of hand movements. Evaluations were also made regarding whether the adverse effects arising from different arm positions and angles of the movement could be mitigated using accelerometer and gyroscope data, and their effects on classifier performance were discussed. As classifiers, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were used. SVM classifiers achieved an average success rate of 83% in the five different categories of analysis, while ANN classifiers achieved an average success rate of 82%. It was found that accelerometer and gyroscope data in various positions contributed very little to the performance of movement classification. As a result of the evaluation, it was discovered that collecting training data in all positions and angles of the forearm improved classification results for a sEMG-based systems. [ABSTRACT FROM AUTHOR]

MEMS (micro-electro-mechanical system) gyroscopes and accelerometers are used in several applications. They are very popular due to their small size, low price, and accessibility. The design of MEMS accelerometers enables the measurement of vibrations, with frequencies from tenths of hertz to even 1 kHz. MEMS gyroscopes can be applied to measure angular rates, and indirectly also angular oscillations with frequencies similar to accelerometers. Despite significant stochastic errors, MEMS sensors are used not only in popular domestic appliances (e.g., smartphones) but also in safety-critical units, such as aeronautical attitude and heading reference systems (AHRSs). In engineering, methods of stochastic properties analysis are important tools for sensor selection, verification, and the design of measurement algorithms. In this article, three methods used for the analysis of stochastic properties of sensors are presented and comparative analyses are shown. The applied measurement frequencies (1 kHz) were much higher than those typically found in MEMS sensor applications. Additionally, an exemplary analysis of temperature drift frequency, as well as the possibility for the synthesis of complementary filter parameters with the use of the described methods, is shown. Assessment of the stochastic properties of MEMS accelerometers and gyroscopes was performed under both constant and variable temperature conditions (during warm-up after switching on) with the use of classic methods, such as power spectral density (PSD) and Allan variance (AV), as well as the less known but very promising generalized method of wavelet moments (GMWM).

Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU’s location has on running data, a reference IMU was ‘correctly’ placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was ‘misplaced’ 0.05 m away, simulating a ‘worst-case’ misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to −10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and −24.3 ms; LOAs −96.8 to +131.6 and −137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs −143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases −30.4 and +29.0 ms; LOAs −149.2 to +88.5 and −83.3 to +141.3 ms) and the Auvinet method for TC (bias −2.8 ms; LOAs −152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy). [ABSTRACT FROM AUTHOR]

One of the main issues in inertial navigation systems is attitude determination, which means estimating the level angles (i.e., roll and pitch). This paper investigates the attitude estimation problem for an accelerated rigid body using three gyros and three accelerometers. The most critical challenges in attitude determination systems are external accelerations and gyroscope drift errors. Thus, a novel method based on the adaptive filter-Kalman algorithm is proposed to estimate and compensate for these errors. Linearization was performed around a general work point, and the covariance matrix's adaptive values were obtained so that leveling angles were accurately determined despite external accelerations. The simulation results, along with the car test, which was performed in different dynamic conditions with external accelerations, showed that the introduced algorithm has a high capability in accurately estimating leveling angles. This approach can be used for GPS-less navigation Algorithms. [ABSTRACT FROM AUTHOR]

High-sensitivity uniaxial opto-mechanical accelerometers provide very accurate linear acceleration measurements. In addition, an array of at least six accelerometers allows the estimation of linear and angular accelerations and becomes a gyro-free inertial navigation system. In this paper, we analyze the performance of such systems considering opto-mechanical accelerometers with different sensitivities and bandwidths. In the six-accelerometer configuration adopted here, the angular acceleration is estimated using a linear combination of accelerometers' read-outs. The linear acceleration is estimated similarly but requires a correcting term that includes angular velocities. Accelerometers' colored noise from experimental data is used to derive, analytically and through simulations, the performance of the inertial sensor. Results for six accelerometers, separated by 0.5 m in a cube configuration show noise levels of 10 − 7 m s − 2 and 10 − 5 m s − 2 (in Allan deviation) for time scales of one second for the low-frequency (Hz) and high-frequency (kHz) opto-mechanical accelerometers, respectively. The Allan deviation for the angular velocity at one second is 10 − 5 rad s − 1 and 5 × 10 − 4 rad s − 1 . Compared to other technologies such as MEMS-based inertial sensors and optical gyroscopes, the high-frequency opto-mechanical accelerometer exhibits better performance than tactical-grade MEMS for time scales shorter than 10 s. For angular velocity, it is only superior for time scales less than a few seconds. The linear acceleration of the low-frequency accelerometer outperforms the MEMS for time scales up to 300 s and for angular velocity only for a few seconds. Fiber optical gyroscopes are orders of magnitude better than the high- and low-frequency accelerometers in gyro-free configurations. However, when considering the theoretical thermal noise limit of the low-frequency opto-mechanical accelerometer, 5 × 10 − 11 m s − 2 , linear acceleration noise is orders of magnitude lower than MEMS navigation systems. Angular velocity precision is around 10 − 10 rad s − 1 at one second and 5 × 10 − 7 rad s − 1 at one hour, which is comparable to fiber optical gyroscopes. While experimental validation is yet not available, the results shown here indicate the potential of opto-mechanical accelerometers as gyro-free inertial navigation sensors, provided the fundamental noise limit of the accelerometer is reached, and technical limitations such as misalignments and initial conditions errors are well controlled. [ABSTRACT FROM AUTHOR]

Copyright of Diversitas Journal is the property of Diversitas Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are many positioning systems available today. The most prominent of these systems is the Inertial Navigation System, which is increasingly preferred because it works with its own internal system independent of external stimuli. This system detects position, orientation and velocity information by means of accelerometer and gyroscope sensors. Using this information, it is possible to make predictions for the next position, orientation and speed with various algorithms. In the studies conducted so far, Kalman Filter (KF) algorithms have been predominantly used for prediction. In this study, Long Term-Short Memory (LSTM) neural network architecture, Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Unit (GRU) and Kalman Filtering methods, which are among the deep learning algorithms that have proven themselves as prediction algorithms, are examined in detail and a comparative study is presented. Here, LSTM, BLSTM and GRU deep learning networks were first trained with IMU sensor data and speed estimation was performed. Root Mean Squared Error (RMSE) values were obtained as 2.5547, 2.7592 and 2.5414, respectively. Furthermore, the same deep learning network methods were trained with GPS data. The prediction data obtained through LSTM, BLSTM and GRU provided RMSE values of 0.42542, 1.91122 and 0.32274, respectively. We see that the prediction with GPS data have higher accuracy since deep learning networks trained with GPS were less affected by noise during the training phase. [ABSTRACT FROM AUTHOR]

The inertial navigation system (INS) is widely used in spacecraft, missiles, and airplanes for its full autonomy and complete information. Skewed inertial measurement unit (IMU) is a typical device for INS. This IMU is of great significance for key projects such as manned spacecraft or heavy-lift rockets. Calibration of the IMU parameters has always been the key technology for INS. However, the calibration for skewed IMU is difficult to conduct for the skewed placement of the sensors and the complicated model. An all-parameter calibration for six-axis skewed IMU is proposed in this article. The error model is established. The measurement equations for Kalman filter are deduced. The calculation can be accomplished in two steps: The calibration for three gyros+ three accelerometers and the parameter compensation and iteration calculation. Simulations and experiments results indicate that: The complexity of calibration data and procedure is the same level as that of traditional IMU; all the parameters can be precisely estimated with the designed 12 positions sequence. [ABSTRACT FROM AUTHOR]

Deep learning methods are widely used in sensor-based activity recognition, contributing to improved recognition accuracy. Accelerometer and gyroscope data are mainly used as input to the models. Accelerometer data are sometimes converted to a frequency spectrum. However, data augmentation based on frequency characteristics has not been thoroughly investigated. This study proposes an activity recognition method that uses ensemble learning and filters that emphasize the frequency that is important for recognizing a certain activity. To realize the proposed method, we experimentally identified the important frequency of various activities by masking some frequency bands in the accelerometer data and comparing the accuracy using the masked data. To demonstrate the effectiveness of the proposed method, we compared its accuracy with and without enhancement filters during training and testing and with and without ensemble learning. The results showed that applying a frequency band enhancement filter during training and testing and ensemble learning achieved the highest recognition accuracy. In order to demonstrate the robustness of the proposed method, we used four different datasets and compared the recognition accuracy between a single model and a model using ensemble learning. As a result, in three of the four datasets, the proposed method showed the highest recognition accuracy, indicating the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The polarization-based attitude and heading reference system (PAHRS) provides an effective solution for attitude and heading information acquisition. Its practical performance, however, will be degraded due to partial loss and/or occlusion of the optical signal. To improve the adaptivity of PAHRS, in this article, we establish a polarization-based tightly coupled model (PTCM) and propose a reliable fusion strategy for information extraction from the polarization sensor (PS) and inertial navigation system (INS). As compared to the existing PS/INS fusion model, the proposed PTCM directly adopt PS raw observations (polarized skylight intensity) to compensate for the accumulation errors of INS, thereby removing the constraints on the least number of PS observation channels and avoiding nonlinear transformation of PS noises. Moreover, the reliable fusion strategy consists of a reliable observation channel selection step followed by a nonlinear filtering step, which can reduce the effect of unreliable polarized skylight intensity measurement. Finally, the simulation, static and semi-physical vehicle-mounted tests confirm the effectiveness of the proposed PTCM and fusion strategy. [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]

The zero-velocity update (ZUPT) method is an effective technique, which has been widely used in low-cost foot-mounted inertial navigation systems. It can mitigate the accumulated error by using the fact that, when the foot is stationary, the velocity should be zero. However, it is hard to detect the zero-velocity phases (ZVPs) correctly in various motion modes. To deal with this problem, a novel adaptive zero velocity detection algorithm based on an improved general likelihood ratio test (IGLRT) detector is proposed. The prior knowledge of the proposed IGLRT detector is the minima output value of inertial sensors, which is determined by an improved peak-valley detection method, while the prior knowledge of the traditional general likelihood ratio test (GLRT) method is based on the hypothesis that, when the foot is stationary, the magnitude of angular rates is zero, and the magnitude of accelerometers is equal to the magnitude of gravitation. Also, an adaptive method is proposed to enhance the robustness of the IGLRT, where the thresholds of IGLRT can be adjusted adaptively according to the amplitude of accelerometers. Field tests show that the proposed algorithm performs better than conventional and state-of-the-art approaches in mixed motion modes. [ABSTRACT FROM AUTHOR]

A popular approach to track the position of a user is to use an inertial measurement unit (IMU), which allows tracking a user with a pedestrian dead reckoning (PDR) system by estimating the length and heading of each step a user takes. Furthermore, a user’s steps together with their length can be used to detect anomalies in the gait due to, e.g., a gait disorder. To determine the length and/or heading of a step, we first need an algorithm that estimates the boundaries of a step, i.e., the start and end, in the IMU signals. In this article, we propose a deep-learning-based step detection algorithm that detects the start and end of a step using only data from a handheld accelerometer. In this algorithm, also, the relationship between the start and end of a step is used to improve performance. To evaluate the performance of the detector, in contrast to most previous works that only look at the total number of detected steps, we determine if the start and end of each step are detected at the correct instant. This resulted in a step detector with an f-score of 99.2% and 99.0% for, respectively, detecting the start and end of a step. After detecting the start and end of the steps, we use a long short-term memory (LSTM)/convolutional neural network (CNN)-based step length estimator, which resulted in a mean absolute error (mae) on the step length of 3.21 cm. Finally, we also determine the combined performance of the step detector and step length estimator, i.e., where the proposed step detector is used to extract the accelerometer fragments of a step instead of assuming that the boundaries are known. This resulted in an mae of 6.56 cm using the deep-learning-based step length estimator. [ABSTRACT FROM AUTHOR]

An inertial measurement unit (IMU) architecture is proposed by combining a set of accelerometers with a three-axis gyroscope, in closed-loop operation, to achieve superior accuracy and low noise estimation of the angular velocity by compensating for accelerometers’ bias and wandering. It advances the concept of gyroscope-free IMUs, which derive the angular velocity using multiple accelerometers in an open-loop configuration, by eliminating their constant drift errors due to accelerometers’ bias, enabling real-world applications. The stability of the proposed closed-loop system is studied analytically with sufficient conditions provided, and a closed-form expression of the angular velocity noise is derived. Both experimental and simulation results indicate that the proposed architecture excels in terms of accuracy and noise performance. [ABSTRACT FROM AUTHOR]

This study proposed a wearable inertial motion capture (Mocap) method for Parkinson’s lower limb motion reconstruction and analysis. To eliminate the error accumulation caused by sensor noise, the error state Kalman filter is used for attitude estimation. The inertial Mocap method proposed in this study is compared with the OptiTrack system to verify the motion tracking effect. The accuracy of different phase-detection methods is compared, and the highest recognition accuracy is 99.11% using the long short-term memory (LSTM) method. In addition, this study collected subjects in healthy control (HC), Parkinson’s disease (PD), and freezing of gait (FOG) groups for comparative analysis. The experimental results revealed differences in motion characteristics and kinematics between PD and healthy subjects. Thus, the quantitative analysis results can be used as an important reference for the clinical application of PD. [ABSTRACT FROM AUTHOR]

Optics and, more recently, coherent matter waves enabled inertial sensors, such as accelerometers and gyroscopes, to reach high levels of resolution and sensitivity. As these technologies rest on physical phenomena that require particular setups and working conditions, e.g., kilometers of optical fibers or ultralow temperatures, their application range is limited because of lack of portability. Here, we propose a path forward considering a superconducting quantum interference device (SQUID) to detect and measure acceleration by using electronic interferometry. The basic idea is not to use a SQUID as a magnetometer in acceleration measurement setups, but as an accelerometer. The operation of such an accelerometer rests on the ability of the Cooper pairs to record their wave function phase change, as the device is subjected either to a transverse acceleration or vibrations. We provide numerical evidence for the feasibility of SQUID-based accelerometers that can be used for transverse acceleration and oscillatory motion measurement. [ABSTRACT FROM AUTHOR]

Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether the intrinsic parameters of both the camera and the IMU are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera–IMU system, all the intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera are observable with an unknown landmark. To our knowledge, we are the first to present such a proof. Next, to validate this analysis and to solve the drift issue of a structureless filter during standstills, we develop a keyframe-based sliding window filter (KSWF) for odometry and self-calibration, which works with a monocular RS camera or stereo RS cameras. Though the keyframe concept is widely used in vision-based sensor fusion, to our knowledge, the KSWF is the first of its kind to support self-calibration. Our simulation and real data tests have validated that it is possible to fully calibrate the camera–IMU system using the observations of opportunistic landmarks under diverse motion. Real data tests confirmed previous allusions that keeping landmarks in the state vector can remedy the drift in standstill and showed that the keyframe-based scheme is an alternative solution. [ABSTRACT FROM AUTHOR]

Arm-and-hand tracking by technological means allows gathering data that can be elaborated for determining gesture meaning. To this aim, machine learning (ML) algorithms have been mostly investigated looking for a balance between the highest recognition rate and the lowest recognition time. However, this balance comes mainly from statistical models, which are challenging to interpret. In contrast, we present $\mu C^{1}$ and $\mu C^{2}$ , two geometric model-based approaches to gesture recognition which support the visualization and geometrical interpretation of the recognition process. We compare $\mu C^{1}$ and $\mu C^{2}$ with respect to two classical ML algorithms, k-nearest neighbor (k-NN) and support vector machine (SVM), and two state-of-the-art (SotA) deep learning (DL) models, bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU), on an experimental dataset of ten gesture classes from the Italian Sign Language (LIS), each repeated 100 times by five inexperienced non-native signers, and gathered with wearable technology (a sensory glove and inertial measurement units). As a result, we achieve a compromise between high recognition rates ($>90\%$) and low recognition times ($ < 0.1 {\mathrm{ s}}$) that is adequate for human–computer interaction. Moreover, we elaborate on the algorithms’ geometric interpretation based on geometric algebra, which supports some understanding of the recognition process. [ABSTRACT FROM AUTHOR]

Dog owners are extremely driven to comprehend the activity and health of their dogs and to keep tabs on their well-being. Dogs' health and well-being, whether as household pets or service animals, are critical issues that are addressed seriously for moral, psychological, and economical reasons. Evaluations of a dog's welfare depend on quantitative assessments of the frequency and variability of certain behavioral features, which are sometimes challenging to make in a dog's normal environment. While it is challenging to obtain dogs' behavioral patterns, it is nearly impossible to directly identify one distinct behavior when they are roaming around at will. Applications for automatic pet monitoring include real-time surveillance and monitoring systems that accurately identify pets using the most recent methods for the classification of pet activities. The suggested method makes use of a long short-term memory (LSTM)-based method to detect and classify the activities of dogs based on sensor data (i.e., accelerometer and gyroscope). The goal of this study is to use wearable sensor data and examine the activities of dogs using recurrent neural network (RNN) technology. We considered 10 pet behaviors, which include walking, sitting, down, staying, feeding, sideways, leaping, running, shaking, and nose work. As dog activity has a wider diversity, experimental work is performed on the multi-layer LSTM framework to have a positive influence on performance. In this study, data were collected from 10 dogs of various ages, sexes, breeds, and sizes in a safe setting. Data preprocessing and data synchronization were performed after the collection of data. The LSTM model was trained using the preprocessed data and the model's performance was evaluated by the test dataset. The model showed good accuracy and high performance for the detection of 10 activities of dogs. This model will be helpful for the real-time monitoring of dogs' activity, thus improving the well-being of dogs. [ABSTRACT FROM AUTHOR]

The demand for wearable devices to simultaneously monitor heart rate (HR) and respiratory rate (RR) values has grown due to the incidence increase in cardiovascular and respiratory diseases. The use of inertial measurement unit (IMU) sensors, embedding both accelerometers and gyroscopes, may ensure a non-intrusive and low-cost monitoring. While both accelerometers and gyroscopes have been assessed independently for both HR and RR monitoring, there lacks a comprehensive comparison between them when used simultaneously. In this study, we used both accelerometers and gyroscopes embedded in a single IMU sensor for the simultaneous monitoring of HR and RR. The following main findings emerged: (i) the accelerometer outperformed the gyroscope in terms of accuracy in both HR and RR estimation; (ii) the window length used to estimate HR and RR values influences the accuracy; and (iii) increasing the length over 25 s does not provide a relevant improvement, but accuracy improves when the subject is seated or lying down, and deteriorates in the standing posture. Our study provides a comprehensive comparison between two promising systems, highlighting their potentiality for real-time cardiorespiratory monitoring. Furthermore, we give new insights into the influence of window length and posture on the systems' performance, which can be useful to spread this approach in clinical settings. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity, using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset (H-Activity), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.

We review the field of atom interferometer inertial sensors. We begin by reviewing the path integral formulation of atom interferometers and then specialize the treatment to light-pulse atom interferometers and, in particular, gravimeters and gyroscopes. The bulk of the article focuses on the most common type of atom interferometer – the light-pulse interferometer, where the atom optics are composed of light pulses. Our article mainly focuses on a review of advances that aid in the practical implementation of atom interferometers toward gravimetry and inertial navigation. To that end, we develop a navigation model that aids in the connection of parameters and performance of atom interferometers to actual quantities of interest to the navigation community. Practical considerations of atomic inertial sensors, including dynamic range, bandwidth, dead time, and cross-coupling effects are discussed, before we review the field of accelerometer and gyroscope atom interferometers. Finally, we review advances in trapped-atom interferometers.

Different technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, fitting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant's legs to capture accelerometer and gyroscope data, and 42 reflexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre-processed in each of two sessions, performed on different days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specific for each system. Measurement(s) G Force • Spatial Orientation Technology Type(s) Accelerometer • Optical Instrument [ABSTRACT FROM AUTHOR]

Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0–6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3–6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP. [ABSTRACT FROM AUTHOR]

The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences in amplitude. The mean RMSE and centered RMSE were, respectively, 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs > 0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance in our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use. [ABSTRACT FROM AUTHOR]

Structural damage detection using inclinometers is getting wide attention from researchers. However, the high price of inclinometers limits this system to unique structures with a relatively high structural health monitoring (SHM) budget. This paper presents a novel low-cost inclinometer, the low-cost adaptable reliable angle-meter (LARA), which combines five gyroscopes and five accelerometers to measure inclination. LARA incorporates Internet of Things (IoT)-based microcontroller technology enabling wireless data streaming and free commercial software for data acquisition. This paper investigates the accuracy, resolution, Allan variance and standard deviation of LARA produced with a different number of combined circuits, including an accelerometer and a gyroscope. To validate the accuracy and resolution of the developed device, its results are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC) in laboratory conditions. The results of a load test experiment on a simple beam model show the high accuracy of LARA (0.003 degrees). The affordability and high accuracy of LARA make it applicable for structural damage detection on bridges using inclinometers. [ABSTRACT FROM AUTHOR]