Accuracy assessment of Tri-plane B-mode ultrasound for non-invasive 3D kinematic analysis of knee joints

BACKGROUND Currently the clinical standard for measuring the motion of the bones in knee joints with sufficient precision involves implanting tantalum beads into the bones. These beads appear as high intensity features in radiographs and can be used for precise kinematic measurements. This procedure imposes a strong coupling between accuracy and invasiveness. In this paper, a tri-plane B-mode ultrasound (US) based non-invasive approach is proposed for use in kinematic analysis of knee joints in 3D space. METHODS The 3D analysis is performed using image processing procedures on the 2D US slices. The novelty of the proposed procedure and its applicability to the unconstrained 3D kinematic analysis of knee joints is outlined. An error analysis for establishing the method's feasibility is included for different artificial compositions of a knee joint phantom. Some in-vivo and in-vitro scans are presented to demonstrate that US scans reveal enough anatomical details, which further supports the experimental setup used using knee bone phantoms. RESULTS The error between the displacements measured by the registration of the US image slices and the true displacements of the respective slices measured using the precision mechanical stages on the experimental apparatus is evaluated for translation and rotation in two simulated environments. The mean and standard deviation of errors are shown in tabular form. This method provides an average measurement precision of less than 0.1 mm and 0.1 degrees, respectively. CONCLUSION In this paper, we have presented a novel non-invasive approach to measuring the motion of the bones in a knee using tri-plane B-mode ultrasound and image registration. In our study, the image registration method determines the position of bony landmarks relative to a B-mode ultrasound sensor array with sub-pixel accuracy. The advantages of our proposed system over previous techniques are that it is non-invasive, does not require the use of ionizing radiation and can be used conveniently if miniaturized.This work has been supported by School of Engineering & IT, UNSW Canberra, under Research Publication Fellowship

Use of MMG signals for the control of powered orthotic devices: Development of a rectus femoris measurement protocol

Copyright © 2009 Rehabilitation Engineering and Assistive Technology Society (RESNA). This is an Author's Accepted Manuscript of an article published in Assistive Technology, 21(1), 1 - 12, 2009, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/10400430902945678.A test protocol is defined for the purpose of measuring rectus femoris mechanomyographic (MMG) signals. The protocol is specified in terms of the following: measurement equipment, signal processing requirements, human postural requirements, test rig, sensor placement, sensor dermal fixation, and test procedure. Preliminary tests of the statistical nature of rectus femoris MMG signals were performed, and Gaussianity was evaluated by means of a two-sided Kolmogorov-Smirnov test. For all 100 MMG data sets obtained from the testing of two volunteers, the null hypothesis of Gaussianity was rejected at the 1%, 5%, and 10% significance levels. Most skewness values were found to be greater than 0.0, while all kurtosis values were found to be greater than 3.0. A statistical convergence analysis also performed on the same 100 MMG data sets suggested that 25 MMG acquisitions should prove sufficient to statistically characterize rectus femoris MMG. This conclusion is supported by the qualitative characteristics of the mean rectus femoris MMG power spectral densities obtained using 25 averages

Real-time gait assessment utilizing a new way of accelerometry

Real-time registration of body segment angles is essential in artificial body position control. A new method is presented for the real-time calculation of the lower extremity angles using data obtained from pairs of two one-dimensional accelerometers. It is shown that, assuming rigid-body dynamics and simple hinge joints, relative angles (i.e. angles between segments) can be calculated without integration, thereby solving the problem of integration drift normally associated with accelerometry. During the stance phase of walking, the relative angles can be transformed to absolute angles (i.e. relative to the gravitational field direction) for the different leg segments. The feasibility of relative angle calculation is demonstrated by calculation of the knee angle of a healthy subject. Stability and resolution were demonstrated with measurements during standing. Measurements during standing up, sitting down and walking showed that shock (heel-strike) and skin movements, due to movements of the underlying muscle tissue, are the main error sources. Additional signal processing, e.g. low-pass filtering, can be used to diminish this error. The accuracy of the knee angle found is shown to be high enough to be used in a feedback controller for functional electrostimulation of the lower extremities

TRANSMISSION OF WHOLE-BODY VIBRATION FROM EXERCISE PLATFORMS

Introduction: The transmission of mechanical vibration to the axial skeleton from a whole body vibration platform was measured across different degrees of knee flexion. Methods: Male subjects (N=12) stood with varying knee flexion angles of 0, 20, and 40 degrees on a whole body vibration platform. Platform frequencies between 20-50 Hz at two amplitude settings (1 and 2 mm) were tested. Transmissibility was measured with triaxial accelerometers at the platform surface, hip, back and head. Results: Changes in peak to peak platform amplitude across vibration frequency and knee angle did not affect transmissibility measurements (p \u3e 0.05). Transmissibility measures varied depending on platform frequency and knee angle (p \u3c 0.05). A significant effect of knee angle on both hip and head transmissibility was found (p \u3c 0.05). Conclusions: The transmissibility of vibration from platform to head during whole body vibration training is reduced with 40 degrees of knee flexion. However, to effectively dampen mechanical energy at the head larger knee angles (\u3e20 degrees) are needed

DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning

Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning-based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root-mean-squared-error (RMSE) of estimated walking speed is 0.16m/s which is comparable to the results obtained by state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE of 0.11m/s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.Comment: 6 pages, 9 figures, published in IEEE Global Communications Conference (GLOBECOM

Measuring skeletal kinematics with accelerometers on the skin surface

The most common motion analysis method uses cameras to track the position of markers on bodily surfaces over time. Although each species has a common skeletal frame to reference recorded motions, the soft tissue covering each is not rigid. Markers, therefore, experience motion relative to the bone and do not accurately portray underlying bone activity. This limits clinical use of motion studies and the understanding of joint motion. Use of MEMS accelerometers for removing soft tissue artifact, motion relative to the bone, from surface measurements and determining the position of the underlying bone was investigated. An animal limb was modeled experimentally as a double pendulum with soft tissue as sprung masses with motions perpendicular to the pendulums. Horizontal motion was cycled at the top joint with a 25 cm stroke. Position data obtained from the mass with a Codamotion™ system and integrated accelerometer data were combined in a Kalman filter to determine global position. Acceleration data in the sensor coordinate system determined tissue artifact and were compared to measurements using CODA markers on the mass and pendulum. Removing artifact from mass position estimated pendulum position over time. In determining mass position, integrated accelerometer data experienced drift, deviating from reasonable values and were determined impractical for Kalman filter input. This led to using only the CODA-determined position as the true position. Accelerometer artifacts resulted in mean differences with the CODA markers of less than 1 mm over 3 cm displacements excluding a mass with mechanical difficulties. The largest mean difference across four tests was 0.66 mm, which is 96.17 percent accurate. Mean differences between base positions collected from accelerometers and CODA markers were found for the global x and y directions. Maximum deviations were 1.64 mm and 4.45 mm, respectively, which are 99.56 and 99.63 percent accurate. Results show the effectiveness of this procedure in calculating the location of the bases of sprung masses in two dimensions. The basis of this research contributes to the determination of bone position over time that will increase the potential of understanding fundamental, rigid body and joint motions in a clinical setting using noninvasive methods

Accuracy assessment of Tri-plane B-mode ultrasound for non-invasive 3D kinematic analysis of knee joints

Background: Currently the clinical standard for measuring the motion of the bones in knee joints with sufficient precision involves implanting tantalum beads into the bones. These beads appear as high intensity features in radiographs and can be used for precise kinematic measurements. This procedure imposes a strong coupling between accuracy and invasiveness. In this paper, a tri-plane B-mode ultrasound (US) based non-invasive approach is proposed for use in kinematic analysis of knee joints in 3D space.Methods: The 3D analysis is performed using image processing procedures on the 2D US slices. The novelty of the proposed procedure and its applicability to the unconstrained 3D kinematic analysis of knee joints is outlined. An error analysis for establishing the method's feasibility is included for different artificial compositions of a knee joint phantom. Some in-vivo and in-vitro scans are presented to demonstrate that US scans reveal enough anatomical details, which further supports the experimental setup used using knee bone phantoms.Results: The error between the displacements measured by the registration of the US image slices and the true displacements of the respective slices measured using the precision mechanical stages on the experimental apparatus is evaluated for translation and rotation in two simulated environments. The mean and standard deviation of errors are shown in tabular form. This method provides an average measurement precision of less than 0.1 mm and 0.1 degrees, respectively.Conclusion: In this paper, we have presented a novel non-invasive approach to measuring the motion of the bones in a knee using tri-plane B-mode ultrasound and image registration. In our study, the image registration method determines the position of bony landmarks relative to a B-mode ultrasound sensor array with sub-pixel accuracy. The advantages of our proposed system over previous techniques are that it is non-invasive, does not require the use of ionizing radiation and can be used conveniently if miniaturized

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions