Inertial Measurement Unit Device Research Articles

Development of gravitationally aligned pendant IMU frailty identifier

This study focused on the practical development of a device for the early detection of frailty by leveraging the advanced Inertial Measurement Unit (IMU) technique. A five-time sit-to-stand test (FTSST) was conducted with people simply wearing a devised IMU pendant to evaluate the degree of frailty. As anticipated, the pendant sensor was recommended to be loosely strapped on older people's bodies for a comfortable outfit. Hence, the measurements from the IMU device would vary with the strap angle of the pedant. A calibration of gravitational alignment, which aligns the measurements to the gravity direction exactly with the FTSST motion signals consistently with an identical principal axis, would benefit the straightforward successive signal analysis and assessment. A prototype was thus developed with the designated calibration by using an open and systematic principle. This study analyzed the signals of accelerometer and gyroscope from IMU, and the subject's demographic profiles comprised a spectrum of features for identification analysis. Pearson correlation and principal component analysis for relevance ranking were used to reduce the feature dimensionality. Classifiers, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), were introduced to infer the frailty level of the subject. An innovative signal calibration and alignment process with solid mathematical support was imposed in the scheme. As deployed, the designated frailty identifier arrived at its generalized accuracy of 95.45 % with real clinic subjects in prediction. The alignment calibration process is the key for not only resolving the loose-attachment problem tolerantly but also enhancing the discriminability of the features. The prototype revealed the better integrity in developing a cheap, tiny, wearable device to identify the frailty.

Validity of Inertial Measurement Unit (IMU Sensor) for Measurement of Cervical Spine Motion, Compared with Eight Optoelectronic 3D Cameras Under Spinal Immobilization Devices.

The assessment of cervical spine motion is critical for out-of-hospital patients who suffer traumatic spinal cord injuries, given the profound implications such injuries have on individual well-being and broader public health concerns. 3D Optoelectronic systems (BTS SmartDX) are standard devices for motion measurement, but their price, complexity, and size prevent them from being used outside of designated laboratories. This study was designed to evaluate the accuracy and reliability of an inertial measurement unit (IMU) in gauging cervical spine motion among healthy volunteers, using a 3D optoelectronic motion capture system as a reference. Twelve healthy volunteers participated in the study. They underwent lifting, transferring, and tilting simulations using a long spinal board, a Sked stretcher, and a vacuum mattress. During these simulations, cervical spine angular movements-including flexion-extension, axial rotation, and lateral flexion-were concurrently measured using the IMU and an optoelectronic device. We employed the Wilcoxon signed-rank test and the Bland-Altman plot to assess reliability and validity. A single statistically significant difference was observed between the two devices in the flexion-extension plane. The mean differences across all angular planes ranged from -1.129° to 1.053°, with the most pronounced difference noted in the lateral flexion plane. Ninety-five percent of the angular motion disparities ascertained by the SmartDX and IMU were less than 7.873° for the lateral flexion plane, 11.143° for the flexion-extension plane, and 25.382° for the axial rotation plane. The IMU device exhibited robust validity when assessing the angular motion of the cervical spine in the axial rotation plane and demonstrated commendable validity in both the lateral flexion and flexion-extension planes.

Digital twin perception and modeling method for feeding behavior of dairy cows

The digital twin of cows holds significant promise for advancing animal welfare and production efficiency. This paper aims to propose an architecture for digital twins of cows that covers their entire lifecycle. A digital twin solution has been developed that utilizes indoor positioning data and inertial measurement unit (IMU) data to construct a cow’s digital shadow. As an example, the paper utilizes the classification of cow feeding and non-feeding behaviors to study the digital twin perception and modeling methods. A custom-made collar integrated with utilized ultra-wideband (UWB) chips and inertial measurement units (IMUs) was utilized to collect real-time location and neck movement data from five healthy non-lactating Holstein cows. The collected data was transmitted via UWB signals to the positioning anchor and subsequently forwarded to a local server. To classify the feeding and non-feeding behaviors of the cows, three methods were employed: Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM). According to the experimental results, all three classification methods were effective, however, LSTM outperformed the others. Employing solely IMU data and implementing the LSTM, the precision of identifying bovine foraging behavior reached 91.05%, with concomitant precision and recall rates of 92.23 and 91.35%, respectively. Through an integration of the data from indoor position detection and IMU devices and the employment of LSTM, the accuracy of identification increased to 94.97%, with a precision rate of 99.99% and a recall rate of 93.86%. The trial of the digital twin solution demonstrated the rationality and technical feasibility of the digital twin architecture, which holds significant reference value for the development of animal digital twins in the animal husbandry industry.

Introducing a time-efficient workflow for processing IMU data to identify sport-specific movement patterns

Introduction
 The use of inertial measurement unit (IMU) has become popular in sports assessment. New IMU devices may make the monitoring process easier; however, their validity and reliability should be established prior to widespread use. IMU devices use a combination of gyroscopic and accelerometer data which allow the derivation of velocity and position vectors by integrating the data over time. Because the process of time integration suffers from time varying biases and noise, the resulting velocity and position vectors are prone to drift after a few seconds. This must be accounted for when processing data from IMUs.
 Aim
 Motivated by the variety of approaches to IMU-based human motion tracking, the aim of this paper is to deliver a report of the author’s experience in processing and handling acceleration data from a wearable IMU sensor recorded during resistance training and present a workflow to identify specific movement patterns across different sports.
 Methods
 Given acceleration data from a wearable sensor during sports practice, the workflow to derive velocity and position measures of specific movement patterns is divided into the following seven steps: 1) Rough cropping of region of interest (ROI). 2) Application of low pass filter to remove jittering upon visual inspection. Depending on ROI length, a detrend filter should be applied on the integrated position and maybe on the velocity data to correct for drift. 3) Visual analysis of characteristics of at least one movement pattern (more if the pattern shows a high inter-repetition variability) to identify key events (e.g. maximal velocity). 4) Automatically find and count key events along ROI. 5) Reassess characteristics of movement pattern to determine other relevant events. 6) Next, segmentation of ROI based on selected events and integration of individual sections to avoid drift. The aim is to integrate the smallest pieces possible. 7) Finally, check to make sure that segmentation worked correctly (e.g. correct number of repetitions, resulting values in a possible range).
 Results
 Acceleration data was captured with an Apple Watch 7 (Apple Inc. California) using the SensorLog app streaming to a customized node.js server application. For the processing and visualization of the data, the programming language Python with usage of the Pandas and SciPy libraries were utilized. The velocity and position data were determined by finding the integral of the acceleration and velocity respectively. Using the previous mentioned workflow 306 repetitions of the back squat executed by 11 recreational athletes (w: 5/m: 6, age: 22-37, weight:58-90kg) were successful segmented.
 Discussion
 The technology underlying commercial IMU sensors are often not communicated transparently. Thus, it is important to properly study the task of calibration the IMU and calculating the vertical component before using it for sport science measurements. Furthermore, it is rarely the case that the movement pattern remains the same over each training session. Therefore, characteristics of the movement pattern must be studied thoroughly to create a robust identification criterium.
 By applying the presented workflow researcher have a structured, easy to apply and time efficient approach to analyze recorded acceleration data on different sport-specific movement patterns.

Nine-axis inertial measurement unit output discriminates activities of varying intensity in the dog

To explore relationships between 9-axis inertial measurement unit (IMU) output and activities of varying intensity in dogs of various sizes. 20 healthy, agility course-trained dogs of various ages and sizes. Height, weight, body condition score, age, length from IMU to the ischium, and height of IMU to the floor were recorded. Dogs performed a series of activities (rest, walk, trot, and agility course) while wearing the IMU device. IMU and video output were reviewed by independent investigators. Correlations and multiple regression models were used to explore relationships between independent variables and IMU output. Calibration demonstrated excellent correlation and concordance between IMUs (intraclass correlation > 0.9) and that the IMUs reliably measured a known acceleration (gravity at rest). Resultant vector magnitude {sqrt[(x^2) + (y^2) + (z^2)]} normalized to body size was calculated from the data. IMU output clearly discriminates between activities of varying intensity in the dog. The inability to accurately measure chronic pain is a barrier to the development of new, or critical evaluation of, therapeutics. Activity monitors (AM) may be the ideal diagnostic target since they are small and provide objective data that can be collected while the pet remains in its natural environment. These results demonstrate the concurrent and predictive validity of the IMU tested. Our long-range goal is to validate an open-source algorithm for the IMU so activity in a pet's natural environment can be used as an outcome measure in future studies.

Validity and reliability of the Output sport device for assessing drop jump performance.

The devices for measuring plyometric exercise in field conditions are becoming increasingly prevalent in applied research and practice. However, before the use of a device in an applied setting, the validity and reliability of such an instrument must be determined. The study aimed to assess the validity and reliability of the Output Sport, an inertial measurement unit (IMU), through comparisons with a force plate for research purposes. A repeated measure test-retest study was performed. Reliability was assessed during single-session trials (i.e., intrasession reliability). A total of 34 national/university level athletes (13 females, 21 males) performed three drop jumps with a fall from 30cm while both devices recorded ground contact time (GCT), flight time (FT), jump height (HJ), and reactive strength index (RSI). T-tests demonstrated that data collected from the IMU device were significantly different to the force platform for all reported variables (all p < 0.01). The intraclass correlation coefficients (ICC) demonstrated good-to-excellent reliability, but with a large range of confidence intervals (CI 95%) for GCT (0.825, 0.291-0.930), FT (0.928, 0.756-0.958), HJ (0.921, 0.773-0.964), and RSI (0.772, 0.151-0.907). The Bland-Altman test showed that the device overestimated contact times and underestimated the other variables. Upon landing, greater ground contact times (i.e., ≥0.355ms) were associated with higher reliability. These results suggest that a single IMU can be used to track changes somewhat accurately and reliably in jump metrics, especially when the GCT is greater than 0.355ms. It is recommended that before practitioners and trainers use the device as a cost-effective solution in the field, further research should be carried out to evaluate a range of data on the type of exercise to be performed.

The effects of different small-sided games configurations on heart rate, rating of perceived exertion, and running demands in professional soccer players

We aimed to compare the effects of different small-sided games (SSG) configurations on heart rate (HR), rating of perceived exertion (RPE), and running demands in soccer players. Twelve outfield male soccer players (U18) participated in this randomized cross-over design study. Players performed three SSG in different “fixed” and “dynamic” pitch sizes (3v3 in large (SSGL) and small (SSGS) fixed area; or 3v3 in dynamic dimensions [SSGD]), with 4x4 minutes interspersed by 2 minutes of rest. HR measures (maximum [HRmax], average [HRavg], and percentage of maximum [%HRmax]), RPE, and running demands were collected across the SGG sessions. The following running activities - total distance covered (TD), distance covered (DC) at 6-12 km·h-1, ≥ 12-18 km·h-1, and >18 km·h-1, peak running speed, acceleration (ACC) and deceleration (DEC) at 1-2 m·s-2 and 2-3 m·s-2, player load, and high metabolic load distance (HMLD) - were recorded by an inertial measurement unit devices. SSGL displayed higher HRavg, %HRmax, and RPE values than SSGS (p < 0.05). SSGL resulted in higher TD, DC at 6-12 km·h-1, ≥ 12-18 km·h-1, > 18 km·h-1, and higher peak speed than SSGS and SSGD (p < 0.05). Moreover, SSGD presented higher TD and DC at 6-12 km·h-1 and ≥ 12-18 km·h-1 than SSGS (p < 0.05). In contrast, SSGD and SSGS showed higher number of ACC and DEC at 1-2 m·s-2 than SSGL (p < 0.05). In conclusion, the pitch size and playing area of the SSG can be manipulated to promote desired physiological and physical demands in young soccer players.

Test – re-test reliability and concurrent validity of cervical active range of motion in young asymptomatic adults using a new inertial measurement unit device

ABSTRACT Objectives Cervical range of motion (CROM) is one of the first things evaluated in cervical disorders. DyCare-Lynx is an inertial measurement unit device that was recently designed to measure CROM. Therefore, the objectives of the present study were to test the reliability and validity of the DyCare-Lynx device for active CROM. Materials and Methods This study included 36 healthy individuals for the reliability study and 31 individuals for the validity study. Test–retest reliability was examined in three different days, by the same examiner with a 4 ± 1-day interval between them in all cervical movements in random order. For validity, the CROM was tested with the Zebris Motion Analysis system and DyCare-Lynx simultaneously. Results The interclass correlation coefficient (ICC) of the DyCare-Lynx ranged from 0.54 to 0.90. The standard error of measurement (SEM) ranged from 2.12°-7.65°. The smallest detectable change (SDD) ranged from 11.25% to 29.75%. The Pearson’s r correlation of DyCare-Lynx with Zebris ranged from 0.655 to 0.957. Conclusion DyCare-Lynx showed moderate to excellent reliability and moderate-to-high validity. Moreover, SEM was low with acceptable SDD values for all movements. Overall, it can be suggested that DyCare-Lynx is a reliable and valid tool to evaluate active CROM.

Biomechanical Analysis of the Tuck Jump Assessment.

Nascimento, N, Sideris, V, and Read, PJ. Biomechanical analysis of the tuck jump assessment. J Strength Cond Res 36(10): 2946-2949, 2022-The tuck jump assessment (TJA) examines plyometric technique flaws associated with the increased risk of anterior cruciate ligament injury. Biomechanical data to investigate the mechanics used during the TJA are limited and assessing each jump individually across the entire test period is time inefficient. This study examined performance characteristics and movement mechanics in 16 professional soccer players during a standardized TJA protocol (10 jumps) to determine if sensitive periods exist within the test period (first 5 vs. last 5). Jump height, average power (PW) reactive strength index (RSI), frontal plane projection angle (FPPA), knee angular velocity (AV), and peak knee flexion angle (PKA) were analyzed using an optoelectrical and inertial measurement unit device. Jump height and PW increased in the past 5 jumps displaying a medium effect size (d = > 0.5). A trend was observed of increased AV, FPPA, and PKA during the past 5 jumps; however, no meaningful differences were observed between respective jump phases, and effect sizes were small (d ≤ 0.2). This study indicates that performance metrics increase during the past 5 jumps of a standardized TJA 10-jump protocol; thus, analyzing the entire test period may not be required, providing a time-efficient approach. Caution should be applied if using this protocol as an injury risk screening tool or to identify fatigue related alterations in male professional soccer players as no obvious movement abnormalities were apparent and minimal differences were present between jump phases in a range of kinematic variables across the recorded test duration.

Validation of Inertial Sensor to Measure Barbell Kinematics across a Spectrum of Loading Conditions.

The aim of this study was to evaluate the level of agreement in measuring back squat kinematics between an inertial measurement unit (IMU) and a 3D motion capture system (3DMOCAP). Kinematic variables included concentric peak velocity (CPV), concentric mean velocity (CMV), eccentric peak velocity (EPV), eccentric mean velocity (EMV), mean propulsive velocity (MPV), and POP-100: a proprietary variable. Sixteen resistance-trained males performed an incrementally loaded one repetition maximum (1RM) squat protocol. A series of Pearson correlations, 2 × 4 RM ANOVA, Cohen’s d effect size differences, coefficient of variation (CV), and standard error of the estimate (SEE) were calculated. A large relationship existed for all variables between devices (r = 0.78–0.95). Between-device agreement for CPV worsened beyond 60% 1RM. The remaining variables were in agreement between devices with trivial effect size differences and similar CV magnitudes. These results support the use of the IMU, regardless of relative intensity, when measuring EMV, EPV, MPV, and POP-100. However, practitioners should carefully select kinematic variables of interest when using the present IMU device for velocity-based training (VBT), as certain measurements (e.g., CMV, CPV) do not possess practically acceptable reliability or accuracy. Finally, the IMU device exhibited considerable practical data collection concerns, as one participant was completely excluded and 13% of the remaining attempts displayed obvious internal error.

Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data.

The amount of home-based exercise prescribed by a physical therapist is difficult to monitor. However, the integration of wearable inertial measurement unit (IMU) devices can aid in monitoring home exercise by analyzing exercise biomechanics. The objective of this study is to evaluate machine learning models for classifying nine different upper extremity exercises, based upon kinematic data captured from an IMU-based device. Fifty participants performed one compound and eight isolation exercises with their right arm. Each exercise was performed ten times for a total of 4500 trials. Joint angles were calculated using IMUs that were placed on the hand, forearm, upper arm, and torso. Various machine learning models were developed with different algorithms and train-test splits. Random forest models with flattened kinematic data as a feature had the greatest accuracy (98.6%). Using triaxial joint range of motion as the feature set resulted in decreased accuracy (91.9%) with faster speeds. Accuracy did not decrease below 90% until training size was decreased to 5% from 50%. Accuracy decreased (88.7%) when splitting data by participant. Upper extremity exercises can be classified accurately using kinematic data from a wearable IMU device. A random forest classification model was developed that quickly and accurately classified exercises. Sampling frequency and lower training splits had a modest effect on performance. When the data were split by subject stratification, larger training sizes were required for acceptable algorithm performance. These findings set the basis for more objective and accurate measurements of home-based exercise using emerging healthcare technologies.

IMU (inertial measurement unit) device for internet of things based disaster early warning system: applications and innovation

The various natural disasters which happen in this world push us to develop an integrated and thorough natural disaster prevention system. In an era full of technologies, accessing data become much easier that it has become probable to decrease the loss caused by natural disaster. The internet of things has powerful role in increasing the prevention of natural disaster. Also, the application of inertial measurement unit has been promoted as universal component that can serve as a tool to monitor the parameters of a natural disaster. This is some examples of several applications of IMU device in general uses and in disaster related uses. This paper serve as a way to develop new ideas about IoT based IMU device, which can be a pillar in developing a disaster responsive society.

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

Radiation-free methods for navigated screw placement in slipped capital femoral epiphysis surgery.

For orthopedic procedures, surgeons utilize intra-operative medical images such as fluoroscopy to plan screw placement and accurately position the guide wire with the intended trajectory. The number of fluoroscopic images needed depends on the complexity of the case and skill of the surgeon. Since more fluoroscopic images lead to more exposure and higher radiation dose for both surgeon and patient, a solution that decreases the number of fluoroscopic images would be an improvement in clinical care. This article describes and compares three different novel navigation methods and techniques for screw placement using an attachable Inertial Measurement Unit device or a robotic arm. These methods provide projection and visualization of the surgical tool trajectory during the slipped capital femoral epiphysis procedure. These techniques resulted in faster and more efficient preoperative calibration and set up times compared to other intra-operative navigation systems in our phantom study. We conducted an experiment using 120 model bones to measure the accuracy of the methods. As conclusion, these approaches have the potential to improve accuracy of surgical tool navigation and decrease the number of required X-ray images without any change in the clinical workflow. The results also show 65% decrease in total error compared to the conventional manual approach.

Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders.

The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement.

Research into Kinect/Inertial Measurement Units Based on Indoor Robots.

As indoor mobile navigation suffers from low positioning accuracy and accumulation error, we carried out research into an integrated location system for a robot based on Kinect and an Inertial Measurement Unit (IMU). In this paper, the close-range stereo images are used to calculate the attitude information and the translation amount of the adjacent positions of the robot by means of the absolute orientation algorithm, for improving the calculation accuracy of the robot’s movement. Relying on the Kinect visual measurement and the strap-down IMU devices, we also use Kalman filtering to obtain the errors of the position and attitude outputs, in order to seek the optimal estimation and correct the errors. Experimental results show that the proposed method is able to improve the positioning accuracy and stability of the indoor mobile robot.

Attitude Fusion of Inertial and Magnetic Sensor under Different Magnetic Filed Distortions

By virtue of gravity measurement from a handheld inertial measurement unit (IMU) sensor, current indoor attitude estimation algorithms can provide accurate roll/pitch dimension angles. Acquisition of precise heading is limited by the absence of accurate magnetic reference. Consequently, initial stage magnetometer calibration is deployed to alleviate this bottleneck in attitude fusion. However, available algorithms tackle magnetic distortion based on time-invariant surroundings, casting the post-calibration magnetic data into unchanged ellipsoid centered in the calibration place. Consequently, inaccurate fusion results are formulated in a more common case of random walk in time-varying magnetic indoor environment. This article proposes a new fusion algorithm from various kinds of IMU sensors, namely gyroscope, accelerometer, and magnetometer. Compared to state-of-the-art attitude fusion approaches, this article addresses the indoor time-varying magnetic perturbation problem in a geometric view. We propose an extend Kalman filter--based algorithm based on this detailed geometric model to eliminate the position-dependent effect of a compass sensor. Experimental data demonstrate that, under different indoor magnetic distortion environments, our proposed attitude fusion algorithm has the maximum angle error of 2.02°, outperforming 7.17° of a gradient-declining-based algorithm. Additionally, this attitude fusion result is constructed in a low-cost handheld arduino core--based IMU device, which can be widely applied to embedded systems.

An All-Parameter System-Level Calibration for Stellar-Inertial Navigation System on Ground

The calibration, in particular about the installation errors of a star sensor, is a vital problem when improving the accuracy of stellar-inertial navigation system (INS). This paper proposed an all-parameter calibration method for a ground-based stellar-INS with 12-position rotations, using a Kalman filter that can simultaneously estimate bias, scale factor, misalignments of inertial measurement unit (IMU), and installation errors of star sensor. The difference between the star vector measured by the star sensor and the gold reference generated by the star simulator is used as an observation. On the basis of observability to all parameters, the accuracy is greatly enhanced through an iterative method. Better than previous separate calibration of INS and star sensor, the proposed method offers an advantage in that installation error is slightly influenced by IMU drift and device precision. The experimental results demonstrate that all estimated parameters have good stability and repeatability, with the maximum attitude error of integrated navigation less than ${6^{\prime \prime }}$ after compensation, compared with $2{0^{\prime \prime }}$ using the traditional method. It is shown that the proposed calibration method can efficiently improve the navigation performance of stellar-INS, which has been extensively used in shipborne systems, military aircraft, and missile systems.

Validation of an inertial measurement unit for the measurement of jump count and height

ObjectivesTo validate the use of an inertial measurement unit (IMU) for the collection of total jump count and assess the validity of an IMU for the measurement of jump height against 3-D motion analysis. DesignCross sectional validation study. Setting3D motion-capture laboratory and field based settings. ParticipantsThirteen elite adolescent volleyball players. Independent variablesParticipants performed structured drills, played a 4 set volleyball match and performed twelve counter movement jumps. Main outcome measuresJump counts from structured drills and match play were validated against visual count from recorded video. Jump height during the counter movement jumps was validated against concurrent 3-D motion-capture data. ResultsThe IMU device captured more total jumps (1032) than visual inspection (977) during match play. During structured practice, device jump count sensitivity was strong (96.8%) while specificity was perfect (100%). The IMU underestimated jump height compared to 3D motion-capture with mean differences for maximal and submaximal jumps of 2.5 cm (95%CI: 1.3 to 3.8) and 4.1 cm (3.1–5.1), respectively. ConclusionThe IMU offers a valid measuring tool for jump count. Although the IMU underestimates maximal and submaximal jump height, our findings demonstrate its practical utility for field-based measurement of jump load.

A Novel Calibration Method Research of the Scale Factor for the All-optical Atomic Spin Inertial Measurement Device

School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China(Received March 23, 2015 : revised June 9, 2015 : accepted July 6, 2015)A novel method to measure the scale factor for the all-optical atomic spin inertial measurement device (ASIMD) is demonstrated in this paper. The method can realize the calibration of the scale factor by a self-consistent method with small errors in the quiescent state. At first, the matured IMU (inertial measurement unit) device was fixed on an optical platform together with the ASIMD, and it has been used to calibrate the scale factor for the ASIMD. The results show that there were some errors causing the inaccuracy of the experiment. By the comparative analysis of theory and experiment, the ASIMD was unable to keep pace with the IMU. Considering the characteristics of the ASIMD, the mismatch between the driven frequency of the optical platform and the bandwidth of the ASIMD was the major reason. An all-optical atomic spin magnetometer was set up at first. The sensitivity of the magnetometer is ultra-high, and it can be used to detect the magnetization of spin-polarized noble gas. The gyromagnetic ratio of the noble gas is a physical constant, and it has already been measured accurately. So a novel calibration method for scale factor based on the gyromagnetic ratio has been presented. The relevant theoretical analysis and experiments have been implemented. The results showed that the scale factor of the device was 7.272 V/°/s by multi-group experiments with the maximum error value 0.49%.Keywords : Scale factor, All-optical, Atomic spin, Inertial measurement deviceOCIS codes : (230.1150) All-optical devices; (230.5160) Photo detectors; (280.4788) Optical sensing and sensors; (020.1335) Atom optics