Comparative study of inertial measurement unit and optical tracking systems for respiratory motion management in radiotherapy.

Human-Robot Interaction (HRI) plays an important role in a wide range of applications in healthcare, entertainment, and industries. Many ways are being devised to improve the interaction between humans and machines in the field of robotics. For instance, the existing approaches claim precise control over the robotic arm using many different controllers like the joystick, keypad control, or electroMyographic (EMG) sensors attached to the human arm as separate units. However, it requires a non-practical amount of calibration time and high operational expertise. Therefore, our goal is to mimic the human arm movements on the 6-degree of freedom (DOF) robotic arm in real-time, providing minimum latency. We practically show the interfacing of Myo armband with robots that provide tremendously low calibration time without compromising efficiency. We also allow a 6-DOF using EMG and inertial measurement unit (IMU) signals for Myo armband and extract the values of EMG and IMU sensors. The extracted raw EMG and IMU values are calibrated using Arduino as a standard micro-controller. Due to the extraction of raw values, Myo armband can also be integrated with other users' peripheral, which in our case is a 6-DOF robotic arm.

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the IMU-2-segment (I2S) pairing work properly only for a limited range of gait speeds or require a similar sensor configuration. Our goal was to propose an algorithm that works over a wide range of gait speeds with different sensor configurations while being robust to footwear type and generalizable to pathologic gait patterns. Eight IMU sensors were attached to both feet, shanks, thighs, sacrum, and trunk, and 12 healthy subjects (training dataset) and 22 patients (test dataset) with medial compartment knee osteoarthritis walked at different speeds with/without insole. First, the mean stride time was estimated and IMU signals were scaled. Using a decision tree, the body segment was recognized, followed by the side of the lower limb sensor. The accuracy and precision of the whole algorithm were 99.7% and 99.0%, respectively, for gait speeds ranging from 0.5 to 2.2 m/s. In conclusion, the proposed algorithm was robust to gait speed and footwear type and can be widely used for different sensor configurations.

Background:Spinal mobility is an important assessment outcome in axial spondyloarthritis (axSpA). Until now, conventional metrology (Schober test, lateral flexion, BASMI, …) has been used to assess spinal mobility, however, new technologies have been developed that provide better accuracy, reliability and responsiveness. Motion capture has been validated and Inertial Measurement Unit (IMU) sensors, appears to be a promising alternative. To use this IMU sensors in axSpA patients, wireless systems must be developed and validated allowing to doctors and patients to use them in hospitals and at home.Objectives:To develop an easy to use mobile app and IMU sensors system for analyse mobility for axSpA patients.Methods:A mobile app has been developed (iUCOTrack) that communicates with two IMU sensors (Shimmer 3©, Fig-a). These sensors are attached in different locations: at forehead and T12 for cervical mobility (Fig-c) and T12 and Sacrum for thoracolumbar mobility (Fig-b). The app provides mobility results for the different tests (Fig-d) and store results in the cloud. Validation tests of these sensors, using Matlab©, were done previously [1]. Our study test the validity of this app against a motion capture system, the UCOTrack®, and its metrology index, the UCOASMI [2], and conventional metrology as reference standards. Patiens with axSpA were recruited consecutively from the COSPAR cohort. Conventional metrology, PRO questionnaires and mobility (Cervical and thoracolumbar - flexion, lateral bending, rotation) using the iUCOTrack app and the UCOTrack were registered. Intraclasss Correlation Coefficients (ICC 3,1) between systems and correlations (spearman) with other axSpA outcome measures were performed for testing validity.Results:15 axSpA patients (47% female, age 52±12 years, disease duration 21±16 years) were included. Table shows ROM (SD) in degrees obtained for cervical and thoracolumbar spine measured by motion capture (UCOTrack) and the app (iUCOTrack). In the last column appears the UCOASMI (SD) calculated using angles obtained by each system. All ICC were good (ICC>0.8), and correlations were significant (p<0.05, r>0.8) specially the UCOASMI. Cervical rotation using a goniometer was 106.2±36°, with a significant correlation with both systems (p<0.05; r>0.8). Schober correlation with lumbar flexion was poor (NS;r>0.5) but a good correlation appeared with lateral flexion (p<0.01;r>0.9). Mean BASMI was 4.0± 1.8 with an excellent correlation with UCOASMI measured by Mocap (p<0.01;r=0.93) and by IMU (p<0.001;r=0.98).CervicalThoracolumbarFlexRotLatFlexRotLatUCOASMIUCOTrack79.5(24.7)109.8(29.6)62.5(25.1)100.7(21.6)61.8(25.3)54.7(22.9)6.07(1.66)iUCOTrack83.0(33.6)112.6(44.3)73.9(29.7)114.4(28.1)51.4(16.1)59.4(15.4)6.15(1.65)ICC0.8640.9030.8120.9360.7980.9010.970Corr0.89*0.96**0.82*0.97**0.88*0.97***0.97**Conclusion:New metrology tools are needed to improve features of convencional metrology. Motion Capture has proved to be valid but has feasibility problems. IMU sensor based systems provide similar results to motion capture but it can be faster and cheaper. A system based on mobile app connected to wireless IMU sensors could be a solution to improve metrology in axSpA. Further studies and developments are needed to introduce these technologies in research and clinical daily practice.

The first objective was to test the validity, reliability and accuracy of paired inertial measurement units (IMUs) to assess absolute angles relative to Vicon and OptiTrack systems. The potential impacts of slow vs. rapid and intermittent vs. continuous movements were tested during 2D laboratory analyses and 3D ecological context analysis. The second objective was to test the IMUs alone in an ecological activity (i.e., front crawl) that encompassed the previous independent variables to quantify inter-cyclic variability. Slow and intermittent motion ensured high to reasonable validity, reliability and accuracy. Rapid motion revealed an out-of-phase pattern for temporal reliability and lower validity, which was also visible in 3D. Also, spatial reliability and accuracy decreased in 3D, mainly due to discrepancies in local maximums, whereas temporal reliability remained in-phase. For the second objective, inter-cyclic variability did not exceed 12° based on root mean square error (RMSE). Therefore, IMUs should be considered valuable supplements to optoelectronic systems if users carefully position the sensors in rigid clusters and calibrate them to integrate potential offsets. Drift correction by spline interpolation or normalisation of the absolute data should also be considered as additional techniques that increase IMU performance in ecological contexts of performance.

Inertial Measurement Unit (IMU) sensors are used in many applications that include aviation, vehicle systems, unmanned aircraft, indoor navigation, health, and robotic systems. An IMU consists of accelerometers and gyroscope sensors combined in a single module. However, the accelerometer or gyroscope alone cannot produce reliable data, and so the outputs are combined to determine accurate data for measurements such as direction, velocity, angular velocity and position. The data collected from IMU sensors may differ due to measurement errors, calibration issues, and errors due to ambient noise. Small errors in IMU sensors can cause large deviations in applications. There is no clear distinction between the performance and area of use of commercially available sensors. Therefore, when selecting a sensor, the requirements for performance should be determined for the area of use and choosen accordingly. This study investigates the performance of three IMU sensors that have no specific application area and are in common use. An experimental setup was designed and implemented to test the accuracy of the acceleration and gyroscopic information obtained from the IMU sensors. The test apparatus consists of IMU sensor, encoder, stepper motor and Raspberry Pi. The stepper motor and encoder are connected to a shaft, and the IMU sensor is mounted on a rotating moving mechanism. The apparatus is controlled by a Raspberry Pi. The Python programming language has been used for the control software. The apparatus provides rotation of a desired angle and velocity. Acceleration and gyroscopic data received from the IMU sensor are drawn in real time. All sensors were first calibrated and then data were taken. The performance of the sensors was compared using the angular values around the x-axis. The test setup was rotated at a certain angle in the x-axis using a stepper motor. The gyroscopic data on the x-axis for each IMU sensor were then read and processed through a Kalman filter. The accuracy of the IMU sensors was determined with reference to the encoder data.

Human activity recognition is a very active research on pervasive computing and mobile health application. Many human activity systems based on inertial measurement unit (IMU) sensor data were proposed in the past few years. These systems mainly use IMU sensor placed on he torso and limbs to collect data and utilize supervised machine learning algorithms on sensor data. One main issue of these systems is that wearing multiple on-body IMU sensors may bring inconvenience to users' daily life. The other issue of these exiting methods is that an activity recognition model that is trained on a specific subject does not work well when being applied to predict another subject's activities since IMU activity data always carry information that is specific to the human subject who conducts the activities. In our work, inspired by the principle of domain adaption, we proposed a new deep-learning activity recognition model based on an adversarial network which can remove the subject-specific information within the IMU activity data and extract subject-independent features shared by the data collected on different subjects. We also for the first time use data collected from insole based IMU sensors on 8 participants for 5 common activities to build a new real world human activity dataset which can minimize the inconvenience for users to wear. We conducted experiments with our new real-world dataset. Results show that our subject independent activity recognition model outperforms state-of-art supervised learning techniques and eliminates the effects of individual differences between subjects successfully. The average recognition accuracy under the leave-one-out (L1O) condition achieves 99.0% which is higher than the performance of traditional human activity recognition system based on CNNs.

BackgroundPatient reported outcome measurement (PROMs) will not capture in detail the functional joint motion before and after total hip arthroplasty (THA). Therefore, methods more specifically aimed to analyse joint movements may be of interest. An analysis method that addresses these issues should be readily accessible and easy to use especially if applied to large groups of patients, who you want to study both before and after a surgical intervention such as THA. Our aim was to evaluate the accuracy of inertial measurement units (IMU) by comparison with an optical tracking system (OTS) to record pelvic tilt, hip and knee flexion in patients who had undergone THA.Methods49 subjects, 25 males 24 females, mean age of 73 years (range 51–80) with THA participated. All patients were measured with a portable IMU system, with sensors attached lateral to the pelvis, the thigh and the lower leg. For validation, a 12-camera motion capture system was used to determine the positions of 15 skin markers (Oqus 4, Qualisys AB, Sweden). Comparison of sagittal pelvic rotations, and hip and knee flexion-extension motions measured with the two systems was performed. The mean values of the IMU’s on the left and right sides were compared with OTS data.ResultsThe comparison between the two gait analysis methods showed no significant difference for mean pelvic tilt range (4.9–5.4 degrees) or mean knee flexion range (54.4–55.1 degrees) on either side (p > 0.7). The IMU system did however record slightly less hip flexion on both sides (36.7–37.7 degrees for the OTS compared to 34.0–34.4 degrees for the IMU, p < 0.001).ConclusionsWe found that inertial measurement units can produce valid kinematic data of pelvis- and knee flexion-extension range. Slightly less hip flexion was however recorded with the inertial measurement units which may be due to the difference in the modelling of the pelvis, soft tissue artefacts, and malalignment between the two methods or misplacement of the inertial measurement units.Trial registrationThe study has ethical approval from the ethical committee “Regionala etikprövningsnämnden i Göteborg” (Dnr: 611–15, 2015-08-27) and all study participants have submitted written approval for participation in the study.

Aiming at the problems of low accuracy and poor robustness in gait recognition of lower extremity exoskeleton robots in human-computer interaction, a depth residual contraction network recognition method based on the fusion of surface electrosemg (sEMG) and inertial measurement unit (IMU) signals was proposed. Firstly, a new energy kernel feature extraction method was used to extract sEMG signals. Based on the sEMG oscillator model, the sEMG energy kernel phase diagram was converted to gray level map by matrix counting method. Secondly, the IMU signal is denoised and processed graphically. Then, deep residual contraction network (DRSN) was used to recognize sEMG and IMU signals in lower limbs. Finally, experimental hardware was deployed in the wearer’s lower limbs, and the algorithm was used to conduct offline and online recognition experiments of three common gaits. Different comparative experiments show that the attention mechanism of DRSN network can significantly improve the classification effect, and the recognition accuracy is improved by 10%–20% compared with single source signal and other feature extraction methods, and finally the recognition accuracy reaches more than 90% through online experiments. The multi-feature fusion network based on energy kernel feature extraction is time-efficient, high-accuracy and robust, and has real-world application value in the field of exoskeleton robotics.

The use of unmanned aerial vehicles (UAV) systems has increased in recent years. Therefore,studies on UAVs have increased today. In this direction, the production of UAV systems with domestic resources has gained importance. In this study, it is desired to develop a domestic and national flight control card and software. In the flight control board designed for the UAV, it is aimed to keep the vehicle in balance in the air. Accurate measurement of platform orientation plays an important role in many applications such as aerospace, robotics, navigation, marine, machine interaction [1]. Inertial Measurement Unit (IMU) sensor was used to accurately measure the orientation of the UAV. IMU sensor is widely used in UAVs due to its light weight and low energy consumption. In this direction, the need for a filter has emerged in the IMU sensor, which is used to accurately measure the orientation of the unmanned aerial vehicle. In this study, a complementary filter was applied on the IMU sensor. Thanks to this filter, it has been observed that the accuracy of the data received from the IMU sensor has increased. Based on the data obtained, a Proportional Integral Derivative (PID) algorithm was developed, and the vehicle was kept in balance. In this study, ARMCortex-M4 based STM32F407VG microcontroller and MPU6050 as IMU sensor were used. Keil-uVision5 compiler is preferred for software. As a result, high accuracy in the orientation detection of unmanned aerial vehicles was obtained by applying a complementary filter on the IMU sensor.

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.

To evaluate the validity and reliability of inertial measurement unit (IMU) sensors in the assessment of spinal mobility in axial spondyloarthritis (axSpA). A repeated measures study design involving 40 participants with axSpA was used. Pairs of IMU sensors were used to measure the maximum range of movement at the cervical (Cx) and lumbar (Lu) spine. A composite IMU score was defined by combining the IMU measures. Conventional metrology and physical function assessment were performed. Validation was assessed considering the agreement of IMU measures with conventional metrology and correlation with physical function. Reliability was assessed using intra-class correlation coefficients (ICCs). The composite IMU score correlated closely (r = 0.88) with the BASMI. Conventional Cx rotation and lateral flexion tests correlated closely with IMU equivalents (r = 0.85, 0.84). All IMU movement tests correlated strongly with BASFI, while this was true for only some of the BASMI tests. The reliability of both conventional and IMU tests (except for chest expansion) ranged from good to excellent. Test-retest ICCs for individual conventional tests varied between 0.57 and 0.91, in comparison to a range from 0.74 to 0.98 for each of the IMU tests. Each of the composite regional IMU scores had excellent test-retest reliability (ICCs=0.94-0.97), comparable to the reliability of the BASMI (ICC=0.96). Cx and Lu spinal mobility measured using wearable IMU sensors is a valid and reliable assessment in multiple planes (including rotation), in patients with a wide range of axSpA severity.

Low-cost inertial measurement unit (IMU) is gradually applied for providing reliable positioning and navigation information in the area of Internet of Things (IoT) applications recently. However, the accuracy of IMU is highly influenced by inertial sensor errors in GNSS-denied and indoor navigation environment. In order to improve the accuracy and robustness of IMU, rotation modulation and cooperative navigation techniques can serve as effective ways for indoor and outdoor seamless navigation and positioning. In this paper, we propose a cooperative navigation system with a low-cost inertial sensor array composed of four IMUs. For optimizing the configuration and information processing of this system, observability analysis is carried out based on the concept of the degree of observability. Furthermore, a criterion for calculating the degree of observability is formulated to simplify the observability analysis of rotational IMUs. According to the simulation experiments of unmanned vehicle at low, medium and high driving speeds, the IMU rotation technique can improve the observability of inertial sensor errors and thus increase the accuracy of orientation, while the cooperative navigation technique can highly improve the accuracy of positioning.

The Inertial Measurement Unit (IMU) sensor is a tool used to measure the speed and acceleration of an object in 3 dimensions (x, y, z). IMU sensors are often used in robotics, drone control, autonomous vehicles, and augmented reality applications. Usually, the data obtained from the IMU sensor is contaminated by interference and noise, which can reduce measurement accuracy. Kalman Filter is a statistical method used to combine measurement data with a mathematical system model to produce better estimates. In the IMU context, the Kalman Filter removed interference and noise affecting acceleration and speed data so that IMU sensor data could be estimated more accurately. This algorithm predicts the next data state based on previous data and updates the prediction with new measurement data. The measurement implementation in this research is the IMU sensor on the GY-91 module to determine the object's tilt on the pitch, roll, and yaw axes during flight. The ARM STM32F407VGT6 microcontroller pin reads the sensor, and then the estimation and prediction process is carried out using the Kalman filter algorithm. With the parameters Kalman Measurement Error = 1, Estimation Error = 0.12, and Covariance Process = 0.4, it can predict the reading results from the IMU sensor well.

This paper intends to design a system which acquires the trainer's motion and force information in order to manipulate a robot arm applied for rehabilitations. Patients who suffering physical disability also can receive the professorial guiding and cheirapsis even excellent trainers are very busy and insufficient. The key point of this article is data acquisition and reconstruction of the movement of the upper limb by controlling the UR5 robot arm. Upper limb's postures are sensed by Inertial Measurement Unit (IMU) and transferred to STM32 microcontroller using I2C communication protocol. We employed the STM32 microcontroller to calculate attitude angles of both the upper arm and the forearm. And the method with using quaternions to calculate attitude angles is detailedly expounded in this paper. Besides, we employed the MYO armband to acquire upper limb's surface electromyography (sEMG) signals for estimating the muscle force of the upper limb. To verify the feasibility of the proposed system, we make three experiments including analyzing fluctuation range of the attitude angles from IMU signals, classifying muscle force using sEMG signals, and evaluating the effect of motion reconstruction. And the results show that the fluctuation range of acquired data are less than 1 degree, 4 typical motions of upper limb can be reconstructed. The proposed system can be used to reconstruct some upper limb's movement.

Four-dimensional cone-beam computed tomography (4D CBCT) is developed to reconstruct a sequence of phase-resolved images, which could assist in verifying the patient's position and offering information for cancer treatment planning. However, 4D CBCT images suffer from severe streaking artifacts and noise due to the extreme sparse-view CT reconstruction problem for each phase. As a result, it would cause inaccuracy of treatment estimation. The purpose of this paper was to develop a new 4D CBCT reconstruction method to generate a series of high spatiotemporal 4D CBCT images. Considering the advantage of (DL) on representing structural features and correlation between neighboring pixels effectively, we construct a novel DL-based method for the 4D CBCT reconstruction. In this study, both a motion-aware dictionary and a spatially structural 2D dictionary are trained for 4D CBCT by excavating the spatiotemporal correlation among ten phase-resolved images and the spatial information in each image, respectively. Specifically, two reconstruction models are produced in this study. The first one is the motion-aware dictionary learning-based 4D CBCT algorithm, called motion-aware DL based 4D CBCT (MaDL). The second one is the MaDL equipped with a prior knowledge constraint, called pMaDL. Qualitative and quantitative evaluations are performed using a 4D extended cardiac torso (XCAT) phantom, simulated patient data, and two sets of patient data sets. Several state-of-the-art 4D CBCT algorithms, such as the McKinnon-Bates (MKB) algorithm, prior image constrained compressed sensing (PICCS), and the high-quality initial image-guided 4D CBCT reconstruction method (HQI-4DCBCT) are applied for comparison to validate the performance of the proposed MaDL and prior constraint MaDL (pMaDL) pmadl reconstruction frameworks. Experimental results validate that the proposed MaDL can output the reconstructions with few streaking artifacts but some structural information such as tumors and blood vessels, may still be missed. Meanwhile, the results of the proposed pMaDL demonstrate an improved spatiotemporal resolution of the reconstructed 4D CBCT images. In these improved 4D CBCT reconstructions, streaking artifacts are suppressed primarily and detailed structures are also restored. Regarding the XCAT phantom, quantitative evaluations indicate that an average of 58.70%, 45.25%, and 40.10% decrease in terms of root-mean-square error (RMSE) and an average of 2.10, 1.37, and 1.37 times in terms of structural similarity index (SSIM) are achieved by the proposed pMaDL method when compared with piccs, PICCS, MaDL(2D), and MaDL(2D), respectively. Moreover the proposed pMaDL achieves a comparable performance with HQI-4DCBCT algorithm in terms of RMSE and SSIM metrics. However, pMaDL has a better ability to suppress streaking artifacts than HQI-4DCBCT. The proposed algorithm could reconstruct a set of 4D CBCT images with both high spatiotemporal resolution and detailed features preservation. Moreover the proposed pMaDL can effectively suppress the streaking artifacts in the resultant reconstructions, while achieving an overall improved spatiotemporal resolution by incorporating the motion-aware dictionary with a prior constraint into the proposed 4D CBCT iterative framework.