Inertial Measurement Unit-Assisted Implantation of Pedicle Screws in Combination With an Intraoperative 3-Dimensional/2-Dimensional Visualization of the Spine

2018 ◽  
Vol 16 (3) ◽  
pp. 326-334
Author(s):  
Gregory F Jost ◽  
Jonas Walti ◽  
Luigi Mariani ◽  
Stefan Schaeren ◽  
Philippe Cattin
Keyword(s):  
Microelectromechanical Systems ◽  
Sagittal Plane ◽  
Pedicle Screws ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Software Application ◽  
3 Dimensional ◽  
Human Cadavers ◽  
Measurement Units ◽  
Pedicle Wall

Abstract BACKGROUND Inertial measurement units (IMUs) are microelectromechanical systems used to track orientation and motion. OBJECTIVE To use instruments mounted with IMUs in combination with a 3- and 2-dimensional (3D/2D) rendering of the computed-tomography scan (CT) to guide implantation of pedicle screws. METHODS Pedicle screws were implanted from T1 to S1 in 2 human cadavers. A software application enabled the surgeon to select the starting points and trajectories on a 3D/2D image of the spine, then locate these starting points on the exposed spine and apply the IMU-mounted instruments to reproduce the trajectories. The position of the screws was evaluated on the postoperative CT scan. RESULTS A total of 72 pedicle screws were implanted. Thirty-seven (77%) of the thoracic screws were within the pedicle (Heary I), 7 (15%) showed a lateral breach of the pedicle, and 4 (8%) violated the anterior or lateral vertebral body (Heary III). In the lumbar spine and S1, 21 screws (88%) were within the pedicle (Gertzbein 0), 2 (8%) screws had a pedicle wall breach < 2 mm (Gertzbein 1), and 1 > 2 to < 4 mm (Gertzbein 2). In the second cadaver, the position was compared to the intraoperatively shown virtual position. The median offset was 3°(mean 3° ± 2°, variance 5, range 0°–9°) in the sagittal plane and 3° (mean 4° ± 3°, variance 9, range 0°–12°) in the axial plane. CONCLUSION IMU-assisted implantation of pedicle screws combined with an intraoperative 3D/2D visualization of the spine enabled the surgeon to precisely implant pedicle screws on the exposed spine.

scholarly journals Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

2021 ◽  
Vol 10 (9) ◽  
pp. 1804
Author(s):  
Jorge Posada-Ordax ◽  
Julia Cosin-Matamoros ◽  
Marta Elena Losa-Iglesias ◽  
Ricardo Becerro-de-Bengoa-Vallejo ◽  
Laura Esteban-Gonzalo ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
High Speeds ◽  
Controlled Conditions ◽  
Gait Parameters ◽  
Measurement Units ◽  
Accuracy And Repeatability

In recent years, interest in finding alternatives for the evaluation of mobility has increased. Inertial measurement units (IMUs) stand out for their portability, size, and low price. The objective of this study was to examine the accuracy and repeatability of a commercially available IMU under controlled conditions in healthy subjects. A total of 36 subjects, including 17 males and 19 females were analyzed with a Wiva Science IMU in a corridor test while walking for 10 m and in a threadmill at 1.6 km/h, 2.4 km/h, 3.2 km/h, 4 km/h, and 4.8 km/h for one minute. We found no difference when we compared the variables at 4 km/h and 4.8 km/h. However, we found greater differences and errors at 1.6 km/h, 2.4 km/h and 3.2 km/h, and the latter one (1.6 km/h) generated more error. The main conclusion is that the Wiva Science IMU is reliable at high speeds but loses reliability at low speeds.

Systematic Calibration Method for FOG Inertial Measurement Units

2013 ◽  
Vol 662 ◽  
pp. 717-720 ◽  
Author(s):  
Zhen Yu Zheng ◽  
Yan Bin Gao ◽  
Kun Peng He
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Calibration Method ◽  
State Equation ◽  
Measurement Unit ◽  
Observation System ◽  
Body Frame ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Error Coefficients

As an inertial sensors assembly, the FOG inertial measurement unit (FIMU) must be calibrated before being used. The paper presents a one-time systematic IMU calibration method only using two-axis low precision turntable. First, the detail error model of inertial sensors using defined body frame is established. Then, only velocity taken as observation, system 33 state equation is established including the lever arm effects and nonlinear terms of scale factor error. The turntable experiments verify that the method can identify all the error coefficients of FIMU on low-precision two-axis turntable, after calibration the accuracy of navigation is improved.

scholarly journals Reliability and Agreement of 3D Trunk and Lower Extremity Movement Analysis by Means of Inertial Sensor Technology for Unipodal and Bipodal Tasks

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 141 ◽  
Author(s):  
Rob Van der Straaten ◽  
Amber K. B. D. Bruijnes ◽  
Benedicte Vanwanseele ◽  
Ilse Jonkers ◽  
Liesbet De Baets ◽  
...  
Keyword(s):  
Lower Limb ◽  
Sagittal Plane ◽  
Inertial Sensor ◽  
Movement Analysis ◽  
Sensor Technology ◽  
Inertial Measurement ◽  
Sit To Stand ◽  
Movement Strategies ◽  
Measurement Units ◽  
Healthy Participants

This study evaluates the reliability and agreement of the 3D range of motion (ROM) of trunk and lower limb joints, measured by inertial measurement units (MVN BIOMECH Awinda, Xsens Technologies), during a single leg squat (SLS) and sit to stand (STS) task. Furthermore, distinction was made between movement phases, to discuss the reliability and agreement for different phases of both movement tasks. Twenty healthy participants were measured on two testing days. On day one, measurements were conducted by two operators to determine the within-session and between-operator reliability and agreement. On day two, measurements were conducted by the same operator, to determine the between-session reliability and agreement. The SLS task had lower within-session reliability and agreement compared with between-session and between-operator reliability and agreement. The reliability and agreement of the hip, knee, and ankle ROM in the sagittal plane were good for both phases of the SLS task. For both phases of STS task, within-session reliability and agreement were good, and between-session and between-operator reliability and agreement were lower in all planes. As both tasks are physically demanding, differences may be explained by inconsistent movement strategies. These results show that inertial sensor systems show promise for use in further research to investigate (mal)adaptive movement strategies.

scholarly journals Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 109
Author(s):  
Binbin Su ◽  
Christian Smith ◽  
Elena Gutierrez Farewik
Keyword(s):  
Wearable Sensors ◽  
Sensor Data ◽  
Measurement Unit ◽  
Deep Convolutional Neural Networks ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Gait Phase ◽  
Robotic Devices ◽  
Phase Recognition ◽  
Five Phases

Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance.

scholarly journals Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5283 ◽  
Author(s):  
Gianmarco Baldini ◽  
Filip Geib ◽  
Raimondo Giuliani
Keyword(s):  
Wearable Sensors ◽  
Measurement Unit ◽  
Daily Routine ◽  
Inertial Measurement Units ◽  
Digital Output ◽  
Inertial Measurement ◽  
Continuous Authentication ◽  
Measurement Units ◽  
Analytical Approaches ◽  
Road Segments

The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the basis of intrinsic features extracted from the analysis of the digital output generated by wearable sensors worn by the subjects during their daily routine. This paper investigates the application of this concept to the continuous authentication of automotive vehicles, which is a novel concept in the literature and which could be used where conventional solutions based on cryptographic means could not be used. In this case, the Continuous Authentication concept is implemented using the digital output from Inertial Measurement Units (IMUs) mounted on the vehicle, while it is driving on a specific road path. Different analytical approaches based on the extraction of statistical features from the time domain representation or the use of frequency domain coefficients are compared and the results are presented for various conditions and road segments. The results show that it is possible to authenticate vehicles from the Inertial Measurement Unit (IMU) recordings with great accuracy for different road segments.

scholarly journals A novel approach to navigated implantation of S-2 alar iliac screws using inertial measurement units

2016 ◽  
Vol 24 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Gregory F. Jost ◽  
Jonas Walti ◽  
Luigi Mariani ◽  
Philippe Cattin
Keyword(s):  
Sagittal Plane ◽  
Intraoperative Imaging ◽  
Intraoperative Navigation ◽  
Anatomical Landmarks ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Sacropelvic Fixation ◽  
Iliac Screws ◽  
Entry Points ◽  
Measurement Units

OBJECT The authors report on a novel method of intraoperative navigation with inertial measurement units (IMUs) for implantation of S-2 alar iliac (S2AI) screws in sacropelvic fixation of the human spine and its application in cadaveric specimens. METHODS Screw trajectories were planned on a multiplanar reconstruction of the preoperative CT scan. The pedicle finder and screwdriver were equipped with IMUs to guide the axial and sagittal tilt angles of the planned trajectory, and navigation software was developed. The entry points were chosen according to anatomical landmarks on the exposed spine. After referencing, the sagittal and axial orientation of the pedicle finder and screwdriver were wirelessly monitored on a computer screen and aligned with the preoperatively planned tilt angles to implant the S2AI screws. The technique was performed without any intraoperative imaging. Screw positions were analyzed on postoperative CT scans. RESULTS Seventeen of 18 screws showed a good S2AI screw trajectory. Compared with the postoperatively measured tilt angles of the S2AI screws, the IMU readings on the screwdriver were within an axial plane deviation of 0° to 5° in 15 (83%) and 6° to 10° in 2 (11%) of the screws and within a sagittal plane deviation of 0° to 5° in 15 (83%) and 6° to 10° in 3 (17%) of the screws. CONCLUSIONS IMU–based intraoperative navigation may facilitate accurate placement of S2AI screws.

Evaluation of inertial measurement units as a novel method for kinematic gait evaluation in dogs

2016 ◽  
Vol 29 (06) ◽  
pp. 475-483 ◽  
Author(s):  
Alexandra Pauls ◽  
Chris Kawcak ◽  
Kevin Haussler ◽  
Gina Bertocci ◽  
Valerie Moorman ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Sagittal Plane ◽  
Plane Motion ◽  
Inertial Measurement Units ◽  
Hip Joints ◽  
Kinematic Data ◽  
Inertial Measurement ◽  
Optical Motion ◽  
Measurement Units ◽  
Plane Joint

Summary Objective: To evaluate the use of inertial measurement units (IMU) for quantification of canine limb kinematics. Methods: Sixteen clinically healthy, medium-sized dogs were enrolled. Baseline kinematic data were acquired using an optical motion capture system. Following this baseline data acquisition, a harness system was used for attachment of IMU to the animals. Optical kinematic data of dogs with and without the harness were compared to evaluate the influence of the harness on gait parameters. Sagittal plane joint kinematics acquired simultaneously with IMU and the optical system were compared for the carpal, tarsal, stifle and hip joints. Comparisons of data were made using the concordance correlation coefficient (CCC) test and evaluation of root mean squared errors (RMSE). Results: No significant differences were demonstrated in stance duration, swing duration or stride length between dogs instrumented with or without the harness, however, mean RMSE values ranged from 4.90° to 14.10° across the various joints. When comparing simultaneously acquired optical and IMU kinematic data, strong correlations were found for all four joints evaluated (CCC: carpus = 0.98, hock = 0.95, stifle = 0.98, hip = 0.96) and median RMSE values were similar across the joints ranging from 2.51° to 3.52°. Conclusions and Clinical relevance: Canine sagittal plane motion data acquisition with IMU is feasible, and optically acquired and IMU acquired sagittal plane kinematics had good correlation. This technology allows data acquisition outside the gait laboratory and may provide an alternative to optical kinematic gait analysis for the carpal, tarsal, stifle, and hip joints in the dog. Further investigation into this technology is indicated.

scholarly journals In Vivo Measurement of Wrist Movements during the Dart-Throwing Motion Using Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5623
Author(s):  
Gabriella Fischer ◽  
Michael Alexander Wirth ◽  
Simone Balocco ◽  
Maurizio Calcagni
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Absolute Difference ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Mean Absolute Difference ◽  
Inertial Measurement ◽  
Dart Throwing ◽  
Measurement Units ◽  
Throwing Motion

Background: This study investigates the dart-throwing motion (DTM) by comparing an inertial measurement unit-based system previously validated for basic motion tasks with an optoelectronic motion capture system. The DTM is interesting as wrist movement during many activities of daily living occur in this movement plane, but the complex movement is difficult to assess clinically. Methods: Ten healthy subjects were recorded while performing the DTM with their right wrist using inertial sensors and skin markers. Maximum range of motion obtained by the different systems and the mean absolute difference were calculated. Results: In the flexion–extension plane, both systems calculated a range of motion of 100° with mean absolute differences of 8°, while in the radial–ulnar deviation plane, a mean absolute difference of 17° and range of motion values of 48° for the optoelectronic system and 59° for the inertial measurement units were found. Conclusions: This study shows the challenge of comparing results of different kinematic motion capture systems for complex movements while also highlighting inertial measurement units as promising for future clinical application in dynamic and coupled wrist movements. Possible sources of error and solutions are discussed.

scholarly journals Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2983
Author(s):  
Marie Sapone ◽  
Pauline Martin ◽  
Khalil Ben Mansour ◽  
Henry Château ◽  
Frédéric Marin
Keyword(s):  
Motion Capture ◽  
Inertial Measurement Unit ◽  
Detection Methods ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
New Methods ◽  
Measurement Units ◽  
Ground Types ◽  
Stride Duration

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

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