Inertial Measurement Unit System Research Articles

Center of mass velocity comparison using a whole body magnetic inertial measurement unit system and force platforms in well trained sprinters in straight-line and curve sprinting

BackgroundSprint performance can be characterized through the centre of mass (COM) velocity over time. In-field computation of the COM is key in sprint training. Research questionTo compare the stance-averaged COM velocity computation from a Magneto-Inertial Measurement Units (MIMU) to a reference system: force platforms (FP), over the early acceleration phase in both straight and curve sprinting. MethodsNineteen experienced-to-elite track sprinters performed 1 maximal sprint on both the straight and the curve (radius = 41.58 m) in a randomized order. Utilizing a MIMU-based system (Xsens MVN Link) and compared to FP (Kistler), COM velocity was computed with both systems. Averaged stance-by-stance COM velocity over straight-line and curve sprinting following the vertical axis (respectively VzMIMU and VzFP) and the norm of the two axes lying on the horizontal plane: x and y, approximately anteroposterior and mediolateral (respectively VxyMIMU and VxyFP) over the starting-blocks (SB) and initial acceleration (IA – composed out of the first four stances following the SB) were compared using mean bias, 95 % limits of agreements and Pearson’s correlation coefficients. Results148 stances were analyzed. VxyMIMU mean bias was comprised between 0.26 % and 2.03 % (expressed in % with respect to the FP) for SB, 5.63 % and 7.29 % over IA respectively on the straight and the curve. Pearson’s correlation coefficients ranged between 0.943 and 0.990 for Vxy, 0.423 and 0.938 for Vz. On the other hand, VzMIMU mean bias ranged between 2.33 % and 4.69 % for SB, between 1.44 % and 19.95 % over IA respectively on the straight and the curve SignificanceThe present findings suggest that the MIMU-based system tested slightly underestimated VxyMIMU, though within narrow limits which supports its utilization. On the other hand, VzMIMU computation in sprint running is not fully mature yet. Therefore, this MIMU-based system represents an interesting device for in-fieldVxyMIMU computation either for straight-line and curve sprinting.

Experimental Study on the Potential of Vehicle’s Attitude Response to Railway Track Irregularity in Precise Train Localization

Railway track is never perfect, as rail distortions, namely, geometric irregularities, exist at all locations along the track. However, these distortions can be regarded as valuable indicators for train localization, since track irregularities present location-dependent characteristics, the measurements of which using onboard sensors are repeatable for the same track. In this research, we study the possibility of determining a train’s position by matching the track irregularity measurements to a predefined map. A train-borne experiment on a real track is used to preliminarily demonstrate the feasibility, evaluate the performance and determine the key parameters for practical implementation. The results show that a submeter longitudinal localization accuracy can be achieved even when using a low-cost cabin-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU), which measures the train’s responses to track irregularities. The proposed method can enhance the positioning accuracy and improve the robustness of multisensory train localization systems.

Improving Vehicle Positioning Performance in Urban Environment with Tight Integration of Multi-GNSS PPP-RTK/INS

Global navigation satellite system (GNSS) signals are easily blocked by urban canyons, tree-lined roads, and overpasses in urban environments, making it impossible to ensure continuous and reliable positioning using only GNSS, even with the widely used precise point positioning and real-time kinematic (PPP-RTK). Since the inertial navigation system (INS) and GNSS are complementary, a tightly coupled PPP-RTK/INS model is developed to improve the positioning performance in these GNSS-challenged scenarios, in which the atmospheric corrections are used to achieve a rapid ambiguity resolution and the mechanization results from INS are utilized to assist GNSS preprocessing, re-fixing, and reconvergence. The experiment was conducted using three sets of vehicle-mounted data, and the performance of low-cost receiver and microelectromechanical system (MEMS) inertial measurement unit (IMU) was compared. The result shows that the positioning accuracy of PPP-RTK/INS can reach 2 cm in the horizontal component and 5 cm in the vertical component in the open environment. In the complex urban environment, continuous and reliable positioning can be ensured during GNSS short interruption, ambiguity can be instantaneously re-fixed with the assistance of INS, and decimeter-level positioning accuracy can be achieved. As a result, the horizontal positioning errors of more than 95% of the total epochs were within 20 cm. In addition, average positioning accuracy better than 15 cm and 30 cm in the horizontal and vertical components, respectively, can be obtained using the low-cost receiver and MEMS IMU. Compared with tactical IMU, the improvements in positioning accuracy and the ambiguity fixing rate using the geodetic receiver were more significant.

Estimating Relative Angles Using Two Inertial Measurement Units Without Magnetometers

Inertial measurement units (IMUs) are used in biomechanical and clinical applications for quantifying joint kinematics. This study aimed to assist researchers new to IMUs and wanting to develop an inexpensive IMU system to estimate the relative angle between IMUs, while understanding the different algorithms for estimating angular kinematics. Thus, there were three subgoals: 1) to present a low-cost and convenient IMU system utilizing two 6-axis IMUs for computing the relative angle between the IMUs; 2) to examine seven methods for estimating the angular kinematics of an IMU; and 3) to provide an open-source code and working principles of these methods. The raw gyroscopic and accelerometer data were preprocessed. The seven methods included gyroscopic integration (GI), accelerometer inclination (AC), basic complementary filter (BCF), Kalman filter (KF), digital motion processor (DMP, a proprietary algorithm), Madgwick filter (MW), and Mahony filter (MH). An apparatus was designed to test nine conditions that computed angles for rotation about three axes (roll, pitch, yaw) and three movement speeds (50°/s, 150°/s, 300°/s). Each trial lasted 25 min. The root-mean-squared error (RMSE) between the gold-standard value measured from the apparatus’ encoder and the value calculated from each of the seven methods was determined. For roll and pitch, all methods accurately quantified angles (RMSE < 6°) at all speeds. For yaw, all methods except AC and DMP displayed RMSE < 6° at all speeds. AC could not be used for yaw angle computation, and DMP displayed RMSE >6°. Researchers can utilize appropriate methods based on their study’s application.

Assessment of shoulder range of motion using a commercially available wearable sensor-a validation study.

Our study aims to validate a commercially available inertial measurement unit (IMU) system against a standard laboratory-based optical motion capture (OMC) system for shoulder measurements in a clinical context. The validation analyses were conducted on 19 healthy male volunteers. Twelve reflective markers were placed on each participant's trunk, scapula and across the arm and one IMU was attached via a self-adhesive strap on the forearm. A single tester simultaneously collected shoulder kinematic data for four shoulder movements: flexion, extension, external rotation, and abduction. Agreement between OMC system and IMU measurements was assessed with Bland-Altman analyses. Secondary analysis included mean biases, root mean square error (RMSE) analysis and Welch's t-test. Bland-Altman limits of agreement (LoA) exceeded the acceptable range of mean difference for 95% of the population (-22.27°, 11.31°). The mean bias showed high levels of agreement within 8° for all four movements. More than 60% of participants demonstrated mean bias less than 10° between methods. Statistically significant differences were found between measurements for abduction (P<0.001) and flexion (P=0.027) but not for extension and external rotation (P≥0.05). Our study shows preliminary evidence for acceptable accuracy of a commercially available IMU against an OMC system for assessment of shoulder movements by a single tester. The IMU also exhibits similar whole degree of error compared to a standard goniometer with potential for application in remote rehabilitation.

Observability Analysis and Keyframe-Based Filtering for Visual Inertial Odometry With Full Self-Calibration

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.

Laterality assessment in martial artists through kinematic analysis of striking techniques

The purpose of this study was to objectively measure and highlight laterality differences in striking techniques between elite martial artists and to compare the results achieved by each athlete. The research hypothesis is that the kinematic analysis of both fighting stances (dominant and non-dominant sides) can reveal laterality differences for each athlete. We used an inertial measurement unit system based on 17 motion trackers consisting of 3D gyroscopes, 3D accelerometers and 3D magnetometers, namely the Xsens MVN Awinda system. We collected data on the striking techniques of 8 elite martial artists. The kinematic parameters were calculated and analysed for some of the most commonly used techniques in martial arts, more specifically, straight and roundhouse punches. In order to obtain information about the laterality levels of the monitored athletes, we compared the values of these parameters for technical execution on the dominant and non-dominant sides and highlighted significant differences through the non-parametric Wilcoxon Signed-Ranks Test of significance. To facilitate comparisons between athletes in terms of laterality, we designed an original rating scale and created diagrams based on their scores. The assessments carried out will contribute to both the management and individualisation of training and the choice of combat tactics in a much more informed way.

Multi-GNSS PPP/INS/Vision/LiDAR tightly integrated system for precise navigation in urban environments

Nowadays, high-precision navigation is a fundamental prerequisite for autonomous driving technology. However, it is often difficult for a stand-alone sensor to meet the needs of high-precision navigation in complex scenarios. To address this problem, we propose a tightly coupled precise point positioning (PPP)/inertial navigation system (INS)/Vision/LiDAR integration method to achieve high-precision, continuous and reliable navigation in urban environments. The multi-GNSS carrier phase and pseudorange measurements, low-cost microelectromechanical system (MEMS) inertial measurement units (IMU) records, sparse visual landmarks and extracted LiDAR edge/planar features are directly fused at the observation level through a centralized Extended Kalman Filter (EKF). The vehicle experiments in different GNSS availability conditions were conducted to evaluate the proposed method. Results indicate that our proposed PPP/INS/Vision/LiDAR integration can maintain sub-meter level positioning in both GNSS half-open-sky and difficult environments, with improvements of (50.7%, 58.6%, 54.3%) and (46.2%, 55.0%, 58.8%) relative to PPP/INS/Vision and PPP/INS/LiDAR, respectively. Moreover, both visual and LiDAR information can significantly improve the velocity and attitude estimation performance, especially for the heading.

Association between heart failure severity and mobility in geriatric patients: an in-clinic study with wearable sensors.

Individuals with heart failure (HF) frequently experience limitations in mobility, but specific aspects of these limitations are not well understood. This study investigated the association of HF severity, based on the New York Heart Association (NYHA) classes, with digital mobility outcomes (DMOs) and handgrip strength in older inpatients with HF. For this explorative analysis, hospital admission and discharge data from an ongoing, prospective cohort study were used. The sample included older participants with HF and a sub-sample of heart-healthy individuals. Participants were equipped with a wearable inertial measurement unit (IMU) system during mobility performance (balancing, sit-to-stand transfer, walking). We analyzed the association between 17 DMOs and HF severity with multiple linear regression models. The total sample included 61 older participants (65-97 years of age, 55.7% female). Of all DMOs, only sway path in a semi-tandem stance position (m/s²) showed a relevant association with NYHA classes (admission: β = -0.28, P = 0.09; discharge: β = -0.39, P = 0.02). Handgrip strength showed a trend towards a significant association (admission: β = -0.15, P = 0.10; discharge: β = -0.15, P = 0.19). This is to our best knowledge the first analysis on the association of HF severity and IMU-based DMOs. Sway path and handgrip strength may be the most promising parameters for monitoring mobility aspects in treatment of HF.

Quantitative downhill skiing technique analysis according to ski instruction curricula: A proof-of-concept study applying principal component analysis on wearable sensor data.

Downhill skiing technique represents the complex coordinative movement patterns needed to control skiing motion. While scientific understanding of skiing technique is still incomplete, not least due to challenges in objectively measuring it, practitioners such as ski instructors have developed sophisticated and comprehensive descriptions of skiing technique. The current paper describes a 3-step proof-of-concept study introducing a technology platform for quantifying skiing technique that utilizes the practitioners’ expert knowledge. The approach utilizes an inertial measurement unit system (Xsens™) and presents a motion analysis algorithm based on the Principal Movement (PM) concept. In step 1, certified ski instructors skied specified technique elements according to technique variations described in ski instruction curricula. The obtained data was used to establish a PM-coordinate system for skiing movements. In step 2, the techniques parallel and carving turns were compared. Step 3 presents a case study where the technique analysis methodology is applied to advise an individual skier on potential technique improvements. All objectives of the study were met, proving the suitability of the proposed technology for scientific and applied technique evaluations of downhill skiing. The underlying conceptual approach - utilizing expert knowledge and skills to generate tailored variability in motion data (step 1) that then dominate the orientation of the PMs, which, in turn, can serve as measures for technique elements of interest - could be applied in many other sports or for other applications in human movement analyses.

Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment.

Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.

Positioning Algorithm of MEMS Pipeline Inertial Locator Based on Dead Reckoning and Information Multiplexing

High-precision mapping and positioning of urban underground pipelines are the basic requirements of urban digital construction. Aiming at the above problems, a dead reckoning algorithm based on the starting point and ending point correction and forward and reverse solution information reuse is proposed. This paper firstly establishes a dead reckoning system model consisting of a microelectromechanical system (MEMS) inertial measurement unit (IMU) and an odometer and analyzes the propagation mechanism of dead reckoning errors. The algorithm constructs the trajectory correction matrix by using the position information of the starting point and the ending point of the short-distance underground pipeline and then uses the trajectory correction matrix to correct the trajectory position information obtained by forward and reverse dead reckoning. Finally, the corrected forward and reverse trajectory position information is fused and averaged to achieve high-precision mapping and positioning of underground pipelines. The simulation results of the 100 m pipeline show that the maximum positioning error of the proposed algorithm for straight pipelines is within 5 cm, and the maximum positioning error for 90° curved pipelines is within 20 cm. The algorithm effectively solves the problem of a rapid accumulation of errors over time in the process of dead reckoning, which greatly improves the positioning accuracy.

Fetlock Joint Angle Pattern and Range of Motion Quantification Using Two Synchronized Wearable Inertial Sensors per Limb in Sound Horses and Horses with Single Limb Naturally Occurring Lameness.

Simple SummaryThe incidence of disease affecting fetlock joint and associated structures is high in the equine athlete due to the large loads acting on the joint during athletic activity. Therefore, study of the movement of this joint (flexion and extension) is a significant matter of interest. To date, fetlock joint angle pattern and range of motion in horses have been quantified using optical motion capture (OMC). These systems use a number of cameras to film the subject and reconstruct the movement trajectories into a computer model. They are very accurate and precise but require expensive equipment and a laboratory setting. The use of inertial measurement unit (IMU) systems in the field of motion analysis is now widespread. IMU systems use a number of sensors in order to derive the position of the body in the space; they have the advantage of being able to collect kinematic spatio-temporal data when the animal is moving overground with fewer constraints than in a gait lab. To the authors’ best knowledge there are no studies using IMUs sensors in order to record relative fetlock joint angles in horses moving overground. In this study, we wanted first to validate the use of IMUs for kinematic detection of fetlock joint movement on the sagittal axis comparing it to the OMC. Then, we intended to discuss fetlock joint range of motion variability quantified by the IMU system under investigation in lame and sound horses. The IMU system was able to record fetlock joint range of motion just as does the bi-dimensional OMC at walk and trot, in both sound and lame horses. IMU system quantification of fetlock joint range of motion confirmed that the variability was mainly due to lameness in our population of horses. Quantifying joint angle patterns with an IMU system instead of using OMC has the advantage of furnishing real-time bio-feedback of kinematic data. The system can handle various equine gaits and clinical and training conditions outside of expensive laboratory circumstances.Fetlock joint angle (FJA) pattern is a sensitive indicator of lameness. The first aim of this study is to describe a network of inertial measurement units system (IMUs) for quantifying FJA simultaneously in all limbs. The second aim is to evaluate the accuracy of IMUs for quantifying the sagittal plane FJA overground in comparison to bi-dimensional (2-D) optical motion capture (OMC). 14 horses (7 free from lameness and 7 lame) were enrolled and analyzed with both systems at walk and trot on a firm surface. All enrolled horses were instrumented with 8 IMUs (a pair for each limb) positioned at the dorsal aspect of the metacarpal/metatarsal bone and pastern and acquiring data at 200 Hz. Passive markers were glued on the center of rotation of carpus/tarsus, fetlock, and distal interphalangeal joint, and video footages were captured at 60 Hz and digitalized for OMC acquisition. The IMU system accuracy was reported as Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The Granger Causality Test (GCT) and the Bland–Altman analysis were computed between the IMUs and OMC patterns to determine the agreement between the two systems. The proposed IMU system was able to provide FJAs in all limbs using a patented method for sensor calibration and related algorithms. Fetlock joint range of motion (FJROM) variability of three consecutive strides was analyzed in the population through 3-way ANOVA. FJA patterns quantified by IMUs demonstrated high accuracy at the walk (RMSE 8.23° ± 3.74°; PCC 0.95 ± 0.03) and trot (RMSE 9.44° ± 3.96°; PCC 0.96 ± 0.02) on both sound (RMSE 7.91° ± 3.19°; PCC 0.97 ± 0.03) and lame horses (RMSE 9.78° ± 4.33°; PCC 0.95 ± 0.03). The two systems’ measurements agreed (mean bias around 0) and produced patterns that were in temporal agreement in 97.33% of the cases (p < 0.01). The main source of variability between left and right FJROM in the population was the presence of lameness (p < 0.0001) and accounted for 28.46% of this total variation. IMUs system accurately quantified sagittal plane FJA at walk and trot in both sound and lame horses.

Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector.

The development of light detection and ranging (lidar) technology began in the 1960s, following the invention of the laser, which represents the central component of this system, integrating laser scanning with an inertial measurement unit (IMU) and Global Positioning System (GPS). Lidar technology is spreading to many different areas of application, from those in autonomous vehicles for road detection and object recognition, to those in the maritime sector, including object detection for autonomous navigation, monitoring ocean ecosystems, mapping coastal areas, and other diverse applications. This paper presents lidar system technology and reviews its application in the modern road transportation and maritime sector. Some of the better-known lidar systems for practical applications, on which current commercial models are based, are presented, and their advantages and disadvantages are described and analyzed. Moreover, current challenges and future trends of application are discussed. This paper also provides a systematic review of recent scientific research on the application of lidar system technology and the corresponding computational algorithms for data analysis, mainly focusing on deep learning algorithms, in the modern road transportation and maritime sector, based on an extensive analysis of the available scientific literature.

Inertial measurement unit-based motion capture to replace camera-based systems for assessing gait in healthy young adults: Proceed with caution

Gait analysis can identify injury-risk markers indiscernible to the naked eye. Inertial measurement unit (IMU)-based motion capture circumvents optokinetic motion capture (OMC) clinical implementation barriers with its portability, increased affordability, and decreased computational burden. We compared an IMU system to a robust OMC marker set for gait analysis. 10 healthy adults walked at self-selected speeds equipped with Noraxon MyoMotion IMUs and a 24-marker, 5-cluster marker-set in view of 14 OMC cameras. A single calibration was applied. IMU system and OMC calculated joint angles were compared. A single calibration performed similarly to previously reported repeated calibration. IMU and OMC agreement was best in the sagittal plane with IMU axis-mixing affecting off-sagittal plane agreement. System differences were greater than 5° for most motions. Measurement system bias showed at the ankle and knee, however differences varied across participants. IMU kinematics should be interpreted with caution; consistency and accuracy must improve before IMUs can replace OMC.

Centroidal Moment Pivot for ambulatory estimation of relative feet and CoM movement post stroke: Portable Gait Lab.

Measuring gait and balance recovery is necessary post stroke. In an earlier study, we developed a minimal three Inertial Measurement Units (IMUs) system called Portable Gait Lab (PGL). The PGL used the Centroidal Moment Pivot (CMP) assumption to estimate relative foot and centre of mass (CoM) positions, and thereby estimate gait parameters in healthy participants. In this study, we validate the feasibility of the PGL to track foot and CoM trajectory during gait in four persons with chronic stroke. Spatiotemporal gait and balance measures were estimated from the foot and CoM trajectories, and compared with the reference ForceShoes™. Each participant made at least 20 steps, and the PGL was able to track foot and CoM trajectories with a root mean square of the differences with the reference of 2.9 ± 0.2 cm and 4.6 ± 3.6 cm. The distances between either foot at the end of the walking task, and step lengths were estimated by PGL with an average error with the reference of 1.98 ± 2.2 cm and 7.8 ± 0.1 cm respectively across participants. We show that our approach was able to estimate spatiotemporal and balance parameters related to gait quality in a clinically useful manner. We recommend conducting further studies to study the feasibility of using the PGL system for variable gait patterns measured post stroke.

What are the kinematic characteristics of the world champion couple in competitive ballroom dance during the waltz's spin movement?

To evaluate competitive ballroom dancing, one of the official events of the World Games, spin movements used by advanced dancers who ranked high in actual competition may be employed. The purpose of this study was to investigate the kinematic characteristics of spin movements in competitive ballroom dancing performed by world champion couples, especially holding posture and lower limb movements. A champion couple and 13 national-level competitive ballroom dancers as the control group participated in this study. An inertial measurement unit (IMU) system (MVN Link; Xsens, Netherlands) consisting of 17 IMUs, attached to feet, shanks, thighs, pelvis, sternum, head, upper arms, forearms, and hands of the dancers, was used to obtain three-dimensional kinematic data at 240 Hz. The overall trend was that the actual and normalized stride lengths of the champion male and female dancers tended to be longer than those of the male and female dancers in the control group. Further, large differences were observed in the pelvic and rib cage segments movements, and the relative angle of the rib cage segment to the pelvic segment. The champion male dancer started to move from the pelvic segment, whereas the champion female dancer and the national-level top male and female dancers started to move from the rib cage segment. During the spin movement, the champion male dancer was in a position where the rib cage segment was rotated to the left with respect to the pelvic segment, whereas the other dancers were in a position where the rib cage segment was rotated to the right. Although limited to technical aspects, these dance kinematic characteristics of the world champion couples and their differences from those of the control group dancers, will be helpful to competitive ballroom dancers and coaches in their daily practice.

Test-retest reliability of customised inertial measurement units (IMUs) in evaluating skateboarding related manoeuvres

Recent developments in sports technology have enabled sports engineers to utilise the availability of inertial measurement units (IMUs) to develop a customised system for data collection during sports performances. Ensuring the reliability of such a system is essential for objective data collection and transmission of information that coaches could rely upon for improving athletes’ techniques and overall performance. The aim of this study was to investigate the reliability of a custom-made IMU system (embedded with Arduino Pro Mini) in the evaluation of skateboarding related manoeuvres. A skilful male and experienced amateur skateboarder (23 years, with 5 years skateboarding experience) performed five skateboarding tricks (Ollie, Nollie, Pop Shuvit, Kickflip and Frontside 180) using a customised ORY skateboard (IMUs fused). The skateboarder was permitted to execute two separate tricks (Tests A and B); thereafter, the reliability of the IMUs in recognising the similarity of the tricks was evaluated using a test-retest approach. Six time-domain signals obtained from the IMU system of each trick’s execution were extracted. Statistical analyses, including the Kolmogorov-Smirnov test, intraclass correlation coefficients (ICC), Cronbach alpha and correlation coefficients, were utilised to measure the scale’s reliability of the system. The results revealed no significant difference between Tests A and B of each trick p &gt; 0.05, ICC &gt; 0.80, Cronbach alpha &gt; 0.80 and r &gt; 0.80; p-value &lt; 0.001. A relatively lower root means square error and mean absolute error were obtained, further suggesting the effectiveness of the system in detecting the similarities in the movement patterns of the skateboarder during the trick execution. The IMU system appeared to be reliable in measuring skateboarding tricks performances. The present findings could serve as guidelines for testing future, custom-made, IMU devices before the commencement of data collection.

Acquisition of Lower-Limb Motion Characteristics with a Single Inertial Measurement Unit-Validation for Use in Physiotherapy.

In physiotherapy, there is still a lack of practical measurement options to track the progress of therapy or rehabilitation following injuries to the lower limbs objectively and reproducibly yet simply and with minimal effort and time. We aim at filling this gap with the design of an IMU (inertial measurement unit) system with only one sensor placed on the tibia edge. In our study, the IMU system evaluated a set of 10 motion tests by a score value for each test and stored them in a database for a more reliable longitudinal assessment of the progress. The sensor analyzed the different motion patterns and obtained characteristic physiological parameters, such as angle ranges, and spatial and angular displacements, such as knee valgus under load. The scores represent the patient’s coordination, stability, strength and speed. To validate the IMU system, these scores were compared to corresponding values from a simultaneously recorded marker-based 3D video motion analysis of the measurements from five healthy volunteers. Score differences between the two systems were almost always within 1–3 degrees for angle measurements. Timing-related measurements were nearly completely identical. The tests on the valgus stability of the knee showed equally small deviations but should nevertheless be repeated with patients, because the healthy subjects showed no signs of instability.

Accuracy and Validity of a Single Inertial Measurement Unit-Based System to Determine Upper Limb Kinematics for Medically Underserved Populations.

Stroke is one of the leading causes of death and disability worldwide, with a disproportionate burden represented by low- and middle-income countries (LMICs). To improve post-stroke outcomes in LMICs, researchers have sought to leverage emerging technologies that overcome traditional barriers associated with stroke management. One such technology, inertial measurement units (IMUs), exhibit great potential as a low-cost, portable means to evaluate and monitor patient progress during decentralized rehabilitation protocols. As such, the aim of the present study was to determine the ability of a low-cost single IMU sensor-based wearable system (named the T’ena sensor) to reliably and accurately assess movement quality and efficiency in physically and neurologically healthy adults. Upper limb movement kinematics measured by the T’ena sensor were compared to the gold standard reference system during three functional tasks, and root mean square errors, Pearson’s correlation coefficients, intraclass correlation coefficients, and the Bland Altman method were used to compare kinematic variables of interest between the two systems for absolute accuracy and equivalency. The T’ena sensor and the gold standard reference system were significantly correlated for all tasks and measures (r range = 0.648—0.947), although less so for the Finger to Nose task (r range = 0.648—0.894). Results demonstrate that single IMU systems are a valid, reliable, and objective method by which to measure movement kinematics during functional tasks. Context-appropriate enabling technologies specifically designed to address barriers to quality health services in LMICs can accelerate progress towards the United Nations Sustainable Development Goal 3.