Three-axis Accelerometer Research Articles

A Novel Adaptive Kalman Filtering Approach to Human Motion Tracking With Magnetic-Inertial Sensors

This article presents an adaptive Kalman filtering approach to estimate the orientation of human body segments by using magnetic-inertial measurement units (MIMUs) with three-axis gyroscope, accelerometer, and magnetometer. In order to mitigate the negative impact of the external accelerations and ferromagnetic disturbances, the disturbances are modeled by first-order Gauss–Markov processes, and two parallel adaptive Kalman filters containing disturbance models are implemented to eliminate the disturbances. An adaptive factor is introduced to adjust the process noise covariance matrix to compensate for modeling errors induced by unmodeled gyroscope biases and the erroneous process noise covariance matrices. Furthermore, the outliers are checked and discarded by hypothesis tests on the innovation, and the measurement is rejected when the innovation exceeds a given confidence interval. Then, the estimated gravity acceleration and geomagnetic field are used to calculate the orientation quaternion using triaxial attitude determination algorithm. Finally, experiments under different motion scenarios are carried out to evaluate the effectiveness of the proposed method, and the performance of the proposed method is discussed in comparison with existing ones. A forward kinematics three-dimensional (3-D) human motion reconstruction method is proposed to drive the human skeleton model to reproduce human motion with a visual interface based on ROS platform.

Joint angle measurement of manipulator and error compensation based on an IMU sensor

To detect the joint angle of the manipulator accurately, a measuring method based on an IMU sensor is proposed. The sensor's attitude angles and corresponding rotation matrix are obtained according to the data of three-axis gyroscope and three-axis accelerometer. During the installation, the sensor's Z-axis direction is kept along with the motor's rotation axis direction, so that the angle of the sensor rotating around its Z-axis, which is the rotation angle of the motor, can be calculated by comparing the rotation relationship between the sensor's initial position and the sensor's position after the motor rotating. The largest source of result error derives from the inconsistent between the sensor's Z-axis direction and the motor's rotation axis direction. Consequently, an installation's error compensation method is designed to correct the sensor's Z-axis to the motor's rotation axis by rotating the related motor. The experiments show that the method can measure the joint angle of the manipulator accurately and calibrate the installation error effectively. The measurement result errors are confined <0.25°.

Wireless detector for translational and rotational motion of spherical-particle flow

To measure the translational and rotational motion of spherical particles in a three-dimensional granular flow, a wireless spherical detector with a built-in wireless module and a three-axis gyroscope and three-axis accelerometer was developed. The three-axis acceleration and three-axis angular velocity can be measured and stored by the detector in real time. The attitude angle, the spatial rotation of the detector coordinate system relative to the laboratory coordinate system, described via the Euler angle is obtained with a Quaternion algorithm and Kalman filter. The gravity acceleration component in the acceleration data can be removed with the attitude angle. The test results show that the acceleration error is ±0.02G, and the average cumulative angle error is 0.02°/s. Finally, the detector was tested as a tracer particle in hopper flow. According to the results, the variation value of the rotation angles of the spherical detector obeys an exponential distribution.

The First 3D-Printed and Wet-Metallized Three-Axis Accelerometer With Differential Capacitive Sensing

In this paper, the innovative fabrication process for electro-mechanical sensors, born from the smart combination of 3D-printing and wet-metallization and previously demonstrated in a $z$ -axis accelerometer, is employed to fabricate a fully 3D three-axis accelerometer. Thanks to the three-dimensionality of the printing process, a central symmetry along the three axes can be obtained by design, unlike in common 2D-like microfabrication processes. Electrostatic differential sensing is obtained through the metallization procedure and the subsequent mounting of fixed electrodes on an external frame of the mechanical structure. Experimental tests under accelerations show good agreement with theoretical predictions. With relatively small dimensions (features as low as $450~\mu \text{m}$ ), very high capacitive sensitivity (> 200 fF/g) and low theoretical thermo-mechanical noise, the proposed three-axis accelerometer represents a further step towards the development of an innovative fabrication process for high-performance, customizable, and low-cost electro-mechanical devices.

Self-Tracking Technology as an Extension of Man

Self-Tracking Technology as an Extension of Man

A Unique Signature of Cardiac-Induced Cranial Forces During Acute Large Vessel Stroke and Development of a Predictive Model.

Cranial accelerometry is used to detect cerebral vasospasm and concussion. We explored this technique in a cohort of code stroke patients to see whether a signature could be identified to aid in the diagnosis of large vessel occlusion (LVO) stroke. A military-grade three-axis accelerometer was affixed to a headset. Accelerometer and electrocardiogram (ECG) outputs were digitized at 1.6kHz. We call the resulting digitized signals the "headpulse." Three-minute recordings were performed immediately after computed tomography (CT) angiography (CTA) and/or immediately before and after attempted mechanical thrombectomy in patents with suspected stroke. The resulting waveforms were inspected by eye and then subjected to supervised machine learning (MATLAB Classification Learner R2018a) to train a model using fivefold cross-validation. Of 42 code stroke subjects with recordings, 19 (45%) had LVO and 23 (55%) had normal CTAs. In patients without LVO, ECG-triggered waveforms followed a self-similar time course revealing that the headpulse is highly coupled to the cardiac contraction. However, in most patients with LVO, headpulses showed little cardiac contraction correlation. We term this abnormality "chaos" and parameterized it with 156 measures of trace-by-trace variation from the ECG-signal-averaged mean for machine learning model training. Selecting the best model, using biometric data only, we properly classified 15/19 LVOs and 20/23 non-LVO patients, with receiver operating characteristic curve area = 0.79, sensitivity of 73%, and specificity of 87%, P < 0.0001. Headpulse waveforms following thrombectomy showed return of cardiac contraction correlation. Headpulse recordings performed on patients with suspected acute stroke significantly identify those with LVO. The lack of temporal correlation of the headpulse with cardiac contraction and resolution to normal may reflect changes in cerebral blood flow and may provide a useful technique to triage stroke patients for thrombectomy using a noninvasive device.

Horsing Around—A Dataset Comprising Horse Movement

Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz.

Human Activities and Postures Recognition: From Inertial Measurements to Quaternion-Based Approaches.

This paper presents two approaches to assess the effect of the number of inertial sensors and their location placements on recognition of human postures and activities. Inertial and Magnetic Measurement Units (IMMUs)—which consist of a triad of three-axis accelerometer, three-axis gyroscope, and three-axis magnetometer sensors—are used in this work. Five IMMUs are initially used and attached to different body segments. Placements of up to three IMMUs are then considered: back, left foot, and left thigh. The subspace k-nearest neighbors (KNN) classifier is used to achieve the supervised learning process and the recognition task. In a first approach, we feed raw data from three-axis accelerometer and three-axis gyroscope into the classifier without any filtering or pre-processing, unlike what is usually reported in the state-of-the-art where statistical features were computed instead. Results show the efficiency of this method for the recognition of the studied activities and postures. With the proposed algorithm, more than 80% of the activities and postures are correctly classified using one IMMU, placed on the lower back, left thigh, or left foot location, and more than 90% when combining all three placements. In a second approach, we extract attitude, in term of quaternion, from IMMUs in order to more precisely achieve the recognition process. The obtained accuracy results are compared to those obtained when only raw data is exploited. Results show that the use of attitude significantly improves the performance of the classifier, especially for certain specific activities. In that case, it was further shown that using a smaller number of features, with quaternion, in the recognition process leads to a lower computation time and better accuracy.

Application Research of Physical Activity Measurement Tool Compared with Traditional Observation Method in Exercise Density of Physical Education Class

This paper takes the cricket course of Tsinghua University as an example to study how to quantitatively monitor the exercise density of physical education class through the three-axis accelerometer physical activity measurement tool, and obtain the calculation formula of effective exercise density in physical education class. Compare physical activity objective measurement method with physical education teachers’ traditional subjective observation method to find out the differences between these two measurement methods of exercise density in physical education classes. It will provide a reference for the exercise density evaluation in physical education classes and the physical activity monitoring of the students.

MAG.I.C.AL.–A Unified Methodology for Magnetic and Inertial Sensors Calibration and Alignment

This paper introduces the MAGnetometer-Inertial sensors Calibration and ALignment (MAG.I.C.AL.) methodology for unified calibration and joint axes alignment of three-axis magnetometer, three-axis accelerometer, and three-axis gyroscope. MAG.I.C.AL. compensates for all linear time-invariant distortions such as scale-factor, cross-coupling, and offset, including the soft-iron and hard-iron distortions of the magnetometer. It introduces a new, computationally efficient, least-squares-based iterative algorithm for the calibration of the magnetometer and the accelerometer. It aligns their axes and introduces a new way to calibrate the gyroscope based on their joint data. MAG.I.C.AL. is implemented in a 15-step sequence achieving fast convergence and high accuracy without using any external piece of equipment and without requiring external attitude references. Simulation and experimental results using low-cost sensors are presented to support the accuracy, efficiency, and the applications of the algorithm.

Online mobile C-arm calibration using inertial sensors: a preliminary study in order to achieve CBCT.

Cone beam computed tomography (CBCT) became increasingly popular over the last years. It allows more accurate diagnosis and treatment planning with a lower effective radiation dose. However, volume reconstruction algorithms require a very precise knowledge of the imaging geometry. Due to mechanical instabilities, mobile C-arms are incompatible with existing tomography algorithms. Therefore, C-arm online calibration is essential in order to achieve an accurate volume reconstruction. We present an online calibration method for mobile C-arms. It is based on tracking the detector and the X-ray source of the C-arm using three-axis gyroscopes and accelerometers. It aims to be precise and noninvasive. The performance of the calibration algorithm is evaluated in regard to the precision of the sensors and to whether or not dynamic models are considered. In addition, we present an algorithm which propagate the errors from the positions and orientations estimates to the 2D projections on the detector plane. Thus, we can evaluate the impact of the estimation errors on the acquired images. The experiments are conducted on an experimental C-arm. The reached accuracy is [Formula: see text] for orientation and [Formula: see text] for position. These errors propagate as an error of [Formula: see text] for the 2D projections on the detector plane. The proposed calibration algorithm achieves an accuracy comparable to the precision of existing calibration methods. The required angle accuracy by CBCT algorithms is reached. However, improvements are needed to achieve the required position precision. The in-plane translations of the X-ray source and the detector are the most crucial parameters to estimate in order to conduct CBCT on mobile C-arms.

Daily Motion Recognition System by a Triaxial Accelerometer Usable in Different Positions

Human–computer interaction has been a more popular research field in recent years with the escalating interest in wearable systems. In this context, evaluating the details of the physical movement of the human body has become a prominent subject for researchers. The systems, which are yielded from this subject, are utilized in such areas as health, public safety, access control, and smart home systems. This kind of system is expected to give fast and reliable results and can be used comfortably for the user. In this paper, a fast and accurate system design about recognition of daily human activities, including falls in the elderly, is proposed. In addition, another main objective of the study is to examine whether an accelerometer can be set in different positions during person’s daily activities. Motion data have been acquired from the waist, wrist, and knee positions of 5 female and 15 male volunteers using a three-axis accelerometer. In this way, walking, sitting, standing up, jumping, and falling motions were collected from volunteers. The principal component analysis method was used as a feature reduction method to reduce the process time and get quick results. Furthermore, any feature extraction method was not used. The support vector machine, multi-layer perceptron, radial basis function, and Naive Bayes classifiers were employed to perform classification success. The proposed technique achieved a maximum of 100%, an average of 96.54% accuracy on the three defined positions. As a result, it has been determined that a single accelerometer can be set in different positions. The findings also suggest that this method can be used in home care and follow-up procedures and fall detection for elderly people.

Calibration, Alignment, and Dynamic Tilt Maintenance Method Based on Vehicular Hybrid Measurement Unit

Aiming at the urgently required for miniaturization, low cost and high reliability in north seeking, and attitude maintenance, a six-axis MEMS inertial measurement unit (IMU) and single-axis fiber optic gyro (FOG) composite measuring device is proposed, in which the six-axis MEMS IMU is installed in a conventional orthogonal configuration; while the single-axis FOG is mounted in parallel with the MEMS horizontal X-axis approximately. Based on this new combinational inertial measurement device, there are two functions can be achieved: North-seeking function, three-axis MEMS accelerometers, and single-axis FOG are used to complete four-position north seeking and initial horizontal attitude calculation. Once the alignment is completed, the novel equipment constructs an attitude maintaining device and transfer the calculated attitude and orientation results to the six-axis MEMS-IMU. Concerning the low-speed working environment and changeable motion state of the equipment, the static state and uniform motion of the instrument can be automatically detected. The MEMS gyro error and attitude error are corrected by filter while in static and uniform-speed state, and maintain attitude tracking in other condition. A detailed calibration, alignment, and attitude maintaining method of the new measuring device is introduced in this paper. The calibration of the turntable, the multi-position alignment of the turntable, and the dynamic performance experimental results demonstrate that the novel equipment and method can effectively and feasibility achieve the user index requirements. The three-axis alignment accuracy is better than 1.5 degree ( $3 \sigma $ ), and the 15-minute tilt maintaining accuracy is better than 2 degree ( $3 \sigma $ ).

Rotating Shaft Fault Prediction Using Convolutional Neural Network: A Preliminary Study

Abstract One of the most important subsystems of the vehicles and machines operating currently in industry and transportation are the rotating subsystems. During the operation, due to the forcing factors influence, the technical state of them is changing and the failure can occur. Fault diagnosis is maintenance task considered as an essential in such subsystems, since possibility of an early detection and diagnosis of the faulty condition can save both time and money. To do this the analysis of the subsystems vibrations is performed. The identified technical state should be considered in a context of the ability and different inability states. Therefore, the first step of the diagnostic procedure is the ability and different inability states identification. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for both expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This article presents authors initial research in deep learning-based data-driven fault diagnosis of rotating subsystems. The proposed technique input raw three-axis accelerometer signal as high-definition image into deep learning layers, which automatically extract signal features, enabling high classification accuracy.

Implementation of a real-time fall detection system for elderly Korean farmers using an insole-integrated sensing device

Korea’s rural communities suffer from serious health issues from a rise in fall accidents due to an aging population. This study proposes a real-time fall detection system using an insole-integrated sensor device that detects fall accidents during farming activities and enables rapid responses to these emergencies. This study developed an insole-integrated fall detection device comprising a three-axial accelerometer and force sensitive resistor pressure sensors. Threshold values were set using data captured by the sensors to distinguish fall and emergency situations from other normal activities. In addition, a real-time monitoring system was implemented so that accident-related information may be automatically communicated to a third party or the monitoring center when a fall or emergency is recognized. A reliability test of the system showed 94.8% accuracy, 98.9% sensitivity, and 91.3% specificity. Furthermore, the detection rate for emergency conditions showed a high accuracy of 100%. Based on the results of this study, the fall detection system based on the insole-integrated sensor device may be implemented as a useful tool to address the issue of fall accidents involving elderly farmers in agricultural activities.

Research on a physical activity tracking system based upon three-axis accelerometer for patients with leg ulcers.

Venous leg ulcerations are a common problem, with high prevalence in the middle-aged and elderly population, and more attention on research of their physical activities has been paid, as they have great effects on the blood circulation of the lower limb. With enough, appropriate training, the chronic venous ulcerations in the lower limb can be avoided and alleviated, and venous hypertension can be reduced effectively. The study deals with a physical activity tracking system for the patients based on a three-axis accelerometer. The system uses a three-axis accelerometer, a microcontroller, and a wireless Bluetooth module to form a data acquisition platform to acquire accelerations of the lower limb movement, and sends it to a smart mobile phone via the wireless Bluetooth module. The system takes advantages of the smart mobile phone to guide the chronic venous leg ulcers to do prescribed rehabilitation exercises for the lower limb muscles, perform acceleration data preprocessing, wavelet transform and reconstruction, denoising and feature extraction, obtain the results of the rehabilitation exercises, and then give reasonable evaluation and judgment. It is helpful to treat underlying venous reflux, create such an environment that allows skin to grow across an ulcer, and accelerate ulcer healing process consequently.

Implementation of real-time seismic diagnostic system on emergency management center buildings: system introduction and operational status on municipal government office buildings

In preparation for a major earthquake in the Nankai Trough, the authors plan to introduce a real-time seismic diagnostic system for emergency management center building clusters in the Aichi prefecture eastern region in Japan. The system involves installing seismometers (three-axis accelerometers) in the buildings and storing observation records in a cloud-based internet system (the Cloud) in real time. Immediately after an earthquake, a simple diagnosis of the building’s residual seismic performance is performed using a lumped-mass model and emergency management officials are notified of the results by email. Also, a detailed analysis of the damaged condition using a three-dimensional frame model is performed for several hours after the event to try to assess the specific building parts that are damaged. This paper describes the proposed system and then presents the results of setting up the system and conducting trial operations in municipal government office buildings as a test case prior to actual system implementation.

Development of a low-cost quadrotor UAV based on ADRC for agricultural remote sensing.

Unmanned aerial vehicle (UAV) has the advantages of good repeatability and high remote sensing (RS) information acquisition efficiency, as an important supplement bridging the gap of high-altitude and ground RS platforms. A quadrotor UAV was developed for the agricultural RS application in this study. The control system consists of a main processor and a coprocessor, integrating a three-axis gyroscope, a three-axis accelerometer, an air pressure sensor and a global positioning system (GPS) module. Engineering trial method (ETM) was used to tune the parameters based on the active disturbance rejection control (ADRC) method. Also a ground control station (GCS) adapted to the quadrotor was developed realizing autonomously take-off and landing, flight route planning, data recording. To investigate the performances of the UAV, several flight tests were carried out. The test results showed that the pitch angle control accuracy error was less than 4°, the flight height control accuracy error was less than 0.86 m, the flight path control accuracy error was less than 1.5 m overall. Aerial multispectral images were acquired and processed. The reflected digital number (DN) values obtained from a height of 10-100 m with 10 m interval could be referenced to classify objects. The normalized-difference-vegetation index (NDVI) values obtained from the aerial multispectral images acquired at 15 m were compared with those obtained by the GreenSeeker (GS) and PSR-1100F. The maximum error was 20.37% while the minimum error was 1.99%, which demonstrated the developed quadrotor UAV’s satisfactions for low altitude remote sensing practice. This study provided a low-cost platform for agricultural remote sensing. Keywords: UAV, quadrotor, ADRC, agricultural remote sensing, NDVI, aerial spraying application DOI: 10.25165/j.ijabe.20191204.4641 Citation: Zhang S C, Xue X Y, Chen C, Sun Z, Sun T. Development of a low-cost quadrotor UAV based on ADRC for agricultural remote sensing. Int J Agric & Biol Eng, 2019; 12(4): 82–87.

Tremor Quantification and its Measurements Using Shimmer

This study aimed to provide a quantitative assessment of parkinsonian tremor through the precise measurement of tremor among Parkinson patients while taking into consideration specific parameters including acceleration, velocity, and displacement. The parameters were obtained by using transducers. For this assessment, three-axis accelerometer and three axis gyroscopic transducers embed in one device called shimmer. In this present paper, sensitivity assessment was used to measure the severity of hand tremors in two positions; resting and postural. Besides, the researchers obtained rotational movement along with tremors’ acceleration movements. The amplitude and frequency was taken from Parkinson patients’ hand. Shimmer sensor showed excellent correspondence of amplitude and frequency measurement with rotational transducer. The frequency of the patient’s hand tremor occurred between 1.594 Hz and 4.813 Hz. However, the accelerometer showed poorer correspondence compared to gyroscope measurement. At the end, gyroscope responses are more remarkable because of the low level of muscle activity required in the task for precise tremor measurements to appear in high sensitivity to this impairment tremor.

&lt;p&gt;Effect of tiotropium/olodaterol on sedentary and active time in patients with COPD: post hoc analysis of the VESUTO&lt;sup&gt;®&lt;/sup&gt; study&lt;/p&gt;

BackgroundPatients with COPD are less physically active. This post hoc analysis of a randomized, double-blind, active-controlled, crossover trial assessed the efficacy of once-daily tiotropium/olodaterol combination therapy versus tiotropium monotherapy in Japanese patients with COPD.Patients and methodsPatients were provided with a three-axis accelerometer to measure sedentary and active behavior defined as 1.0–1.5 metabolic equivalents (METs), ≥2.0 METs, and ≥3.0 METs, respectively. Of the 182 patients enrolled, 131 satisfied the conditions for the present analysis and were randomized to tiotropium monotherapy (n=62) or tiotropium/olodaterol combination therapy (n=69).ResultsTiotropium/olodaterol combination therapy significantly reduced the duration of 1.0–1.5 MET activity by 8.64 mins (p=0.040) and significantly increased the duration of ≥2.0 MET and ≥3.0 MET activity by 6.51 mins (p=0.017) and 2.60 mins (p=0.008), respectively, compared with tiotropium alone. Subgroup analyses showed that better lung function, milder dyspnea, and higher levels of physical activity at baseline were associated with reduced sedentary time and increased duration of physical activity.ConclusionTiotropium/olodaterol combination therapy significantly reduced sedentary time and improved physical activity compared with tiotropium monotherapy. This trial was registered in ClinicalTrials.gov (NCT02629965).