Application Of Inertial Measurement Units Research Articles

Air Handwriting by Using CNN Model

Gesture recognition has been a popular research field under the trend of IoT and intelligent devices. Air-writing is the most challenging and crucial topic in the gesture recognition field. In this paper, we propose a wearable airwriting system that makes users can write the English alphabet in the three-dimensional space without any write rules. The proposed system is based on the Inertial Measurement Unit (IMU), and it uses dynamic time warping (DTW) as the main recognition algorithm. In addition, to improve the recognition accuracy and take a better advantage of the DTW algorithm, we present an adjustment system that gives some new optimization methods to the application of IMU and DTW. In the experiment, the accuracy of recognition is 84.6% for the uppercase alphabet (from „A‟ to „Z‟) in user-dependent case. And we also confirmed that the recognition method only based on the DTW algorithm is one kind of user-dependent methods, which means this method is heavily dependent on personalization.

VR interactive input system based on INS and binocular vision fusion

In virtual reality interactive input systems, real-time and accurate spatial position and pose measurement is key to achieving a natural user interface. This study explores the application of inertial measurement units and binocular vision fusion technology in virtual reality interactive input systems, with the aim of improving the tracking accuracy of the system through optimized pose models and visual algorithms. A virtual reality measurement technology that integrates inertial measurement units and binocular vision is proposed by using an improved Kalman filtering algorithm to process inertial measurement units data, and combining it with the SURF algorithm optimized binocular vision system. Experimental results showed that fusion technology could reduce sensor noise and bias, improve the accuracy of pose estimation, and promote stable and robust motion tracking in virtual reality systems. In the angle range of -90 ° to 90 °, the average absolute error of the fusion system compared to the simple inertial measurement units pose calculation decreased from 3.371 ° to 1.369 ° In the experiment of measuring distance with a binocular vision system, the average absolute error value decreased to 1.532 mm, and the error range of the marker within the distance range of 500 mm to 650 mm was controlled within -1.3 mm to 1.2 mm. This study provides an effective solution for achieving high-precision virtual reality interactive input systems and is meaningful for the advancement of virtual reality technology.

RESPONSE TIME IN INERTIAL MEASUREMENT UNIT CONTROL ALGORITHMS

The Inertial Measurement Unit (IMU) [1] is a cornerstone technology in various fields, ranging from aerospace to consumer electronics, where accurate motion tracking is paramount. Central to the effectiveness of an IMU is the quality of data processing, particularly in the context of filtering techniques. This study compares two filtering methods: Complementary Filters and Kalman Filters, in their application to IMU data processing. Complementary Filters, known for their simplicity and efficiency, contrast with the more complex but potentially more accurate Kalman Filters. Our investigation delves into the underpinnings of each filter, followed by a practical analysis of their performance in real-world IMU applications. We comprehensively compare these filters in terms of accuracy, computational efficiency, and ease of implementation. This research offers valuable insights for practitioners and researchers in selecting the most suitable filtering approach for specific IMU-based applications, enhancing the overall quality of motion sensing and analysis.

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation.

Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.

Accelerations Recorded by Simple Inertial Measurement Units with Low Sampling Frequency Can Differentiate between Individuals with and without Knee Osteoarthritis: Implications for Remote Health Care.

Determining the presence and severity of knee osteoarthritis (OA) is a valuable application of inertial measurement units (IMUs) in the remote monitoring of patients. This study aimed to employ the Fourier representation of IMU signals to differentiate between individuals with and without knee OA. We included 27 patients with unilateral knee osteoarthritis (15 females) and 18 healthy controls (11 females). Gait acceleration signals were recorded during overground walking. We obtained the frequency features of the signals using the Fourier transform. The logistic LASSO regression was employed on the frequency domain features as well as the participant's age, sex, and BMI to distinguish between the acceleration data from individuals with and without knee OA. The model's accuracy was estimated by 10-fold cross-validation. The frequency contents of the signals were different between the two groups. The average accuracy of the classification model using the frequency features was 0.91 ± 0.01. The distribution of the selected features in the final model differed between patients with different severity of knee OA. In this study, we demonstrated that using logistic LASSO regression on the Fourier representation of acceleration signals can accurately determine the presence of knee OA.

IMU-based full-body pose estimation for construction machines using kinematics modeling

Monitoring unsafe operations of construction machines is important for reducing accidents on sites. To automate safety monitoring, this paper studies pose estimation of construction machines, which describes their motions in 3D space. Inertial Measurement Unit (IMU) is considered accurate, cost-saving and user-friendly for estimating the pose of machines. Nevertheless, the complexity and instability of construction sites hinder the application of IMU to construction machines. Hence, this study first defines the information requirements of IMU-based full-body pose estimation for construction machines. Then, an efficient method is developed to generate the trajectories of a machine based on kinematics. Moreover, an optimal installation scheme for IMU sensors is investigated systematically to optimize the location and number of IMUs to install. Overall, this study contributes an IMU-based method to provide full-body poses of machines without environmental constraints on real construction sites, and a theoretical basis of optimal IMU installation strategy for reliable motion-related analyses.

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial.

BackgroundCerebral palsy (CP) is a physical disability that affects movement and posture. Approximately 17 million people worldwide and 34,000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and positions in children with CP.ObjectiveThis paper presents collaborative research between the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University and a team of clinicians in a multicenter randomized controlled trial involving children with CP. This study aims to develop a digital solution for mass data collection using inertial measurement units (IMUs) and the application of machine learning (ML) to classify the movement features associated with CP to determine the effectiveness of therapy. The results were calculated without the need to measure Euler, quaternion, and joint measurement calculation, reducing the time required to classify the data.MethodsCustom IMUs were developed to record the usual wrist movements of participants in 2 age groups. The first age group consisted of participants approaching 3 years of age, and the second age group consisted of participants approaching 15 years of age. Both groups consisted of participants with and without CP. The IMU data were used to calculate the joint angle of the wrist movement and determine the range of motion. A total of 9 different ML algorithms were used to classify the movement features associated with CP. This classification can also confirm if the current treatment (in this case, the use of wrist extension) is effective.ResultsUpon completion of the project, the wrist joint angle was successfully calculated and validated against Vicon motion capture. In addition, the CP movement was classified as a feature using ML on raw IMU data. The Random Forrest algorithm achieved the highest accuracy of 87.75% for the age range approaching 15 years, and C4.5 decision tree achieved the highest accuracy of 89.39% for the age range approaching 3 years.ConclusionsAnecdotal feedback from Minimising Impairment Trial researchers was positive about the potential for IMUs to contribute accurate data about active range of motion, especially in children, for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movements throughout the day.Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12614001276640, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367398; ANZCTR ACTRN12614001275651, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367422

Prediction of slaughterhouse workers’ RULA scores and knife edge using low-cost inertial measurement sensor units and machine learning algorithms

The high prevalence of work-related musculoskeletal disorders (WRMSDs) has been a concern in the meat-processing industry, owing to the manual nature of the work and the high upper-limb and neck exposure to movements that can lead to WRMSD. The ability to perform an accurate and fast assessment of WRMSDs remains a challenge in industrial environments. Most assessment methodologies rely on standard survey-based methods, which are time- and labor-intensive. In this paper, we present an application of inertial measurement units (IMUs) to measure human activity, and the use of artificial intelligence and machine learning techniques to perform task classification and ergonomic assessments in workplace settings. We present the results obtained by using simple low-cost IMUs worn on slaughterhouse worker wrists to capture information on their movements. We describe the use of this information to detect the risk factors of the wrists/hands that can lead to WRMSDs. The results indicate that by using low-cost IMU-based sensors on the wrists of slaughterhouse workers, we can accurately classify the sharpness of the knife and predict the worker RULA score.

Use of IMU in Differential Analysis of the Reverse Punch Temporal Structure in Relation to the Achieved Maximal Hand Velocity.

To achieve good performance, athletes need to synchronize a series of movements in an optimal manner. One of the indicators used to monitor this is the order of occurrence of relevant events in the movement timeline. However, monitoring of this characteristic of rapid movement is practically limited to the laboratory settings, in which motion tracking systems can be used to acquire relevant data. Our motivation is to implement a simple-to-use and robust IMU-based solution suitable for everyday praxis. In this way, repetitive execution of technique can be constantly monitored. This provides augmented feedback to coaches and athletes and is relevant in the context of prevention of stabilization of errors, as well as monitoring for the effects of fatigue. In this research, acceleration and rotational speed signal acquired from a pair of IMUs (Inertial Measurement Unit) is used for detection of the time of occurrence of events. The research included 165 individual strikes performed by 14 elite and national-level karate competitors. All strikes were classified as slow, average, or fast based on the achieved maximal velocity of the hand. A Kruskal–Wallis test revealed significant general differences in the order of occurrence of hand acceleration start, maximal hand velocity, maximal body velocity, maximal hand acceleration, maximal body acceleration, and vertical movement onset between the groups. Partial differences were determined using a Mann–Whitney test. This paper determines the differences in the temporal structure of the reverse punch in relation to the achieved maximal velocity of the hand as a performance indicator. Detecting the time of occurrence of events using IMUs is a new method for measuring motion synchronization that provides a new insight into the coordination of articulated human movements. Such application of IMU can provide additional information about the studied structure of rapid discrete movements in various sporting activities that are otherwise imperceptible to human senses.

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.

Auto-Correction of 3D-Orientation of IMUs on Electric Bicycles †.

The application of inertial measurement units (IMU) in electronically power-assisted cycles (EPACs) has become increasingly important for improving their functionalities. One central issue of such an application is to calibrate the orientation of the IMU on the EPAC. The approach presented in this paper utilizes common bicycling motions to calibrate the 2D- and 3D-mounting orientation of a micro-electro-mechanical system (MEMS) IMU on an electric bicycle. The method is independent of sensor biases and requires only a very low computation expense and, thus, the estimation can be realized in real-time. In addition, the acceleration biases are estimated using a barometric pressure sensor. The experimental results show high accuracy of the calibrated orientation and estimated sensor biases.

The application of inertial measurement units and functional principal component analysis to evaluate movement in the forward 3½ pike somersault springboard dive

ABSTRACT Based on technological and analytical advances, the capability to more accurately and finitely examine biomechanical and skill characteristics of movement has improved. The purpose of this study was to use Inertial Measurement Units (IMUs) and Functional Principal Components Analysis (fPCA) to examine the role of movement variability (assessed via angular velocity), on 2 divers (1 international level; 1 national) performing the forward 3½ pike somersault dive. Analysis of angular velocity curves during ive-flight identified 5 fPCs, accounting for 96.5% of movement variability. The national diver’s scatter plots and standard deviation of fPC scores illustrated larger magnitudes of angular velocity variability across dive flight. For fPC1 and fPC3, magnitudes of SD variability were 282.6 and 201.5, respectively. The international diver illustrated more consistent angular velocity profiles, with clustering of fPCs scores (e.g., fPC1 & 3 = SD’s of 75.2 & 68.0). To account for lower variability in the international diver, the ability to better coordinate movement sequences and functionally utilise feedback in response to initiation of the somersault position is highlighted. Overall, findings highlight how both IMUs and fPCA can more holistically and finitely examine the biomechanical and skill characteristics of movement sequences with the capability to inform athlete development.

Effect of Strapdown Integration Order and Sampling Rate on IMU-Based Attitude Estimation Accuracy.

This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).

Quantitative Analysis of Parkinsonian Tremor in a Clinical Setting Using Inertial Measurement Units.

Background Parkinson's disease (PD) is a neurodegenerative disorder that affects human voluntary movements. Tremor is one of the most common symptoms of PD and is expressed as involuntary oscillation of the body. Tremors can be analysed in the frequency domain. Objective The aim of the current study was to examine selected tremor parameters (frequency, root mean square, and approximated entropy) in order to quantify the characteristics of patients diagnosed with PD, compared to a healthy control group, and to compare the parameters by dividing the subjects according to UPDRS assessment. Methods The subjects were divided into two groups: a group of people diagnosed with PD (n = 19) and a control group consisting of healthy volunteers (CO = 12). Each subject performed motor tasks specific to certain tremors: the finger-to-nose test. Each subject performed a motor task three times. A nine degree of freedom (DOF) wireless inertial measurement unit was used for the measurement of upper limb motor tasks. For the quantitative estimation of kinetic and postural tremors, dominant frequency, root means square, and approximation entropy were selected and calculated from the measured angular velocity and linear acceleration signals. A one-way ANOVA with a significance level of α = 0.05 was used to test the null hypothesis that the means of the tremor metrics were the same between the PD and CO groups. Results Statistically significant differences between PD patients and control groups were observed in ApEn acceleration signal of kinetic tremor, ApEn angular velocity signal of kinetic tremor, ApEn angular velocity of postural tremor, frequency acceleration signal of postural tremor, and RMS angular speed kinetic tremor. Conclusion Application of inertial measurement units for clinical research of patients and PD tremor evaluation allows providing quantitative information for diagnostic purposes, during screening in a clinical setting that differentiates between PD patients and controls.

The Application of Inertial Measurement Units and Wearable Sensors to Measure Selected Physiological Indicators in Archery

The requirement for objective techniques to observe physical action in its distinctive measurements has prompted the improvement and broad utilisation of motion sensors called Inertial Measurement Units (IMUs), which measures bodily movements. However, although these sensors have been utilised to measure postural balance in both clinical and some specific sports, little or no effort have been made to apply these sensors to the measurement of other physiological indicators in the sport of archery. This study aims to ascertain the postural balance, hand movement, muscular activation as well as heart rate of an archer. An archer was instructed to perform two balance standings, two hand movements and his muscular activations of flexor and extensor digitorum, as well as heart rate, were recorded using Shimmer sensors. The mean movement of x and y-axis of the archer was used to correlate with the Pearson correlation for testing the validity of the sensors. Kolmogorov/Smirnov test was utilised to measure the reliability of the sensors over test re-test in two different tests. The coefficient of determination indicates some positive and negative significant relationships between some indicators. The Kolmogorov/Smirnov test re-test reveals a significant difference between all the indicators in both tests A and B, p < 0.001. The archer was able to present two types of postural standings and exhibited two hands movement while holding the bow. However, his heart rate demonstrated some variability during the executions of the movement in both tests. Thus, it could be concluded that the fusion sensors are reliable in measuring the aforementioned physiological indicators.

The validation and application of Inertial Measurement Units to springboard diving

Inertial Measurement Units (IMUs) may offer an ecologically valid, reliable, and practical method for biomechanical performance analysis. With such potential in mind, Part 1 of this study examined the accuracy of IMUs gyroscopes with an optical system (Cortex 3.3). A calibration formula standardised the IMUs angular velocity output with the optical system. The percentage differences between the two measures = 0.5% (p < 0.05), suggest IMU’s efficacy for application. In Part 2, the aim was to examine and understand how dive flight angular velocity time series plots change and increase according to dive degree of difficulty. With IMUs attached to three competitive divers performing forward somersault dives, dive flight kinematics were assessed. Biomechanically, a 4½ tuck somersault dive differed to lower degree of difficulty dives in terms of: (1) a rotational delay immediately after takeoff (to gain greater vertical translation); (2) increased total time of flight; (3) greater muscle effort to resist increased centrifugal forces produced by the increased angular velocity (1,090 °/s); and (4) greater eccentric control during deceleration allow a safe and vertical entry into the water. IMUs can be effectively utilised and integrated into contexts such as springboard diving for performance analysis and optimisation purposes.

Application of Inertial Measurement Units to Assess Vehicle Ingress and Egress Characteristics

The increasing number of elderly and overweight drivers in the United States has necessitated that vehicle manufacturers develop automobiles that accommodate these changing demographics. While digital human models have been successfully used to simulate human vehicle interaction (Chaffin, 2005; Ozsoy et al., 2015; Yang et al., 2007), the variability in ingress and egress procedures among drivers with different physical attributes poses a unique challenge to developing accurate models. The objective of this study was to apply inertial measurement units (IMUs) to compare ingress and egress characteristics of individuals of varying age and body type. Ninety-three participants, comprising a control group (aged 21–&lt;65 years with body mass index [BMI] &lt;30 kg/m2), a high-BMI group (aged 21–&lt;65 years with BMI ≥30 kg/m2), and an elderly group (aged ≥65 years), performed ingress and egress trials in three vehicles (compact, sedan, and sport utility vehicle [SUV]) while wearing a wireless IMU system (Xsens MVN BIOMECH Awinda system, Xsens Technologies BV, Enschede, Netherlands). Native protocols of the IMU system were used to estimate the joint centers and motion paths of several bilateral body joints including the ankle, knee, hip, wrist, elbow, and shoulder. The transition of each joint center across the edge of the seating area at the door sill as the subject got into and out of the vehicles was measured. The start of the ingress process was defined as the time when the first joint center passed through the door sill into the driver’s seating area. The end of ingress was considered to be the time when the final joint passed back through the edge of the seating area. Two-way analyses of variance (ANOVA) examining the effects of population (control vs. elderly vs. high-BMI), gender, vehicle model, and their interactions on ingress and egress times were conducted. In general, the right ankle was the first body segment to enter each of the three vehicles followed by the right knee, right hip, right shoulder, left hip, left shoulder, left knee and the left ankle regardless of population group. Ingress time was observed to be affected by population (F2, 28 = 17.97, p &lt; 0.001), and was characterized by a much slower ingress time among the elderly (mean = 4.33 secs), compared with controls (2.97 secs) and high-BMI participants (2.71 secs). Egress was also observed to be affected by population (F2, 28 = 8.85, p &lt; 0.001), but in a slightly different manner. The control group (mean = 2.95 secs) was the fastest to egress the car, followed by the high-BMI group (4.42 secs) and the elderly (5.26 secs). The results indicate that IMUs may be successfully applied to characterize ingress and egress motion paths of different population groups and may be useful when designing seated applications, particularly for elderly and high-BMI populations.

Multidimensional evaluation and analysis of motion segmentation for inertial measurement unit applications

Despite various Inertial Measurement Unit (IMU) applications and their technological advances, the process of motion segmentation performed by accelerometers continues to be essential for finding t...

Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units

Energy expenditure measurement is crucial to understand the biophysics of any kind of human locomotion. Despite the promising application of inertial measurement unit (IMU) for quantification of the energy expenditure during human on-land activities, it has never been deployed before to calculate the aquatic activities energy expenditure. Wearable IMUs were used in this paper to capture biomechanically interpretable descriptors of swimming. These descriptors were fed as inputs to a Bayesian linear model for estimation of the energy expenditure. To enhance generalization capacity of the estimator, a non-linear adjustment of the Bayesian model was devised using swimmer's anthropometric parameters. We used a set of four waterproofed IMUs worn on forearms, sacrum, and right shank of eighteen swimmers to extract the main spatiotemporal determinants of the front-crawl energy expenditure. The swimmers performed three 300-m trials at 70%, 80%, and 90% of their 400-m personal best time. At the end of each 300 m, the reference value of energy expenditure was measured based on indirect calorimetry and blood lactate concentration. The assessment of the proposed model on the test data shows a strong association between the estimated and reference energy expenditure (Spearman's rho=0.93, ) and a high relative precision of 9.4%. The backward elimination of model parameters with minimum rms error criterion shows that by excluding the features extracted from forearm sensors, i.e., using only two IMUs, we can still achieve an error of 0.9±11.3%.

A Novel Application of Inertial Measurement Units (IMUs) as Vehicular Technologies for Drowsy Driving Detection via Steering Wheel Movement

Introduction: Vehicular technologies intended for the improvement of driver safety are especially critical today in the view of the thousands of deaths that occur annually due to drowsy driving. Current technologies include physiological methods like electroencephalography (EEG), behavioral methods including driver video monitoring, and vehicle measures which include lane and steering wheel tracking. These current technologies are impractical in their current implementations as they cannot readily be used outside of laboratory settings due to their requirements for intrusive electrodes, expensive cameras, and complex equipment. An earlier article demonstrated an effective method for wheel tracking using only an accelerometer; however the introduction of integrated gyroscopes and accelerometers has afforded further opportunities. Objective: This paper introduces a novel, low-cost, and easy to implement an approach to address this unmet problem. Method: Through the use of an Inertial Measurement Unit (IMU) combining a gyroscope and an accelerometer, measurements of steering wheel behavior were recorded in both simulator and real world driving while compared against a standard potentiometer. Results: The excellent agreement between potentiometer recorded angles and IMU estimated angles (R2 = 0.98, P < 0.001) suggests that the complicated installation of potentiometers in vehicle steering columns is no longer a necessary step for steering wheel monitoring. Conclusion: This paper presents an IMU based method for drowsy steering-wheel behavioral tracking which is cost-effective, easy to implement, and accurately estimates steering behaviors. The results suggest that this novel vehicle technology offers hope for improving road safety.