Single Gait Cycle Research Articles

Vision-based estimation method of crowd-induced vertical dynamic loading and vibration analysis

Most existing studies concentrating on human-induced loads on structures often rely on approximate formulas based on the loading from a single gait cycle, which inherently possess certain limitations. In this paper, a novel approach utilizing a multi-person 2D posture estimation model rooted in computer vision technology to real-time identify the posture of moving human bodies on construction sites is proposed. This approach works to deduce the reaction forces of moving bodies upon the floor by analyzing the motion characteristics of different body parts during their movement, ensuring an accurate estimation of structural loading. To validate the model’s accuracy, a series of tests were conducted, leveraging force measurement equipment to scrutinize the precision of load estimation during single-person motion. This examination seeks to ascertain the potential of the load prediction model in forecasting multi-person loads within actual project environments.

Interactive Locomotion Style Control for a Human Character based on Gait Cycle Features

AbstractThis article introduces a data‐driven locomotion style controller for full‐body human characters using gait cycle features. Based on gait analysis, we define a set of gait features that can represent various locomotion styles as spatio‐temporal patterns within a single gait cycle. We compute the gait features for every single gait cycle in motion capture data and use them to search for the desired motion. Our real‐time style controller provides users with visual feedback for the changing inputs, exploiting the Motion Matching algorithm. We also provide a graphical controller interface that visualizes our style representation to enable intuitive control for users. We show that the proposed method is capable of retrieving appropriate locomotions for various gait cycle features, from simple walking motions to single‐foot motions such as hopping and dragging. To validate the effectiveness of our method, we conducted a user study that compares the usability and performance of our system with those of an existing footstep animation tool. The results show that our method is preferred over the baseline method for intuitive control and fast visual feedback.

Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data

Clinical datasets often comprise multiple data points or trials sampled from a single participant. When these datasets are used to train machine learning models, the method used to extract train and test sets must be carefully chosen. Using the standard machine learning approach (random-wise split), different trials from the same participant may appear in both training and test sets. This has led to schemes capable of segregating data points from a same participant into a single set (subject-wise split). Past investigations have demonstrated that models trained in this manner underperform compared to those trained using random-split schemes. Additional training of models via a small subset of trials, known as calibration, bridges the gap in performance across split schemes; however, the amount of calibration trials required to achieve strong model performance is unclear. Thus, this study aims to investigate the relationship between calibration training set size and prediction accuracy on the calibration test set. A database of 30 young, healthy adults performing multiple walking trials across nine different surfaces while fit with inertial measurement unit sensors on the lower limbs was used to develop a deep-learning classifier. For subject-wise trained models, calibration on a single gait cycle per surface yielded a 70% increase in F1-score, the harmonic mean of precision and recall, while 10 gait cycles per surface were sufficient to match the performance of a random-wise trained model. Code to generate calibration curves may be found at (https://github.com/GuillaumeLam/PaCalC).

Estimation of Knee Assistive Moment in a Gait Cycle Using Knee Angle and Knee Angular Velocity through Machine Learning and Artificial Stiffness Control Strategy (MLASCS)

Nowadays, many people around the world cannot walk perfectly because of their knee problems. A knee-assistive device is one option to support walking for those with low or not enough knee muscle forces. Many research studies have created knee devices with control systems implementing different techniques and sensors. This study proposes an alternative version of the knee device control system without using too many actuators and sensors. It applies the machine learning and artificial stiffness control strategy (MLASCS) that uses one actuator combined with an encoder for estimating the amount of assistive support in a walking gait from the recorded gait data. The study recorded several gait data and analyzed knee moments, and then trained a k-nearest neighbor model using the knee angle and the angular velocity to classify a state in a gait cycle. This control strategy also implements instantaneous artificial stiffness (IAS), a control system that requires only knee angle in each state to determine the amount of supporting moment. After validating the model via simulation, the accuracy of the machine learning model is around 99.9% with the speed of 165 observers/s, and the walking effort is reduced by up to 60% in a single gait cycle.

Framework of sampling the subject-specific static loads from the gait cycle of interindividual variation

Background and ObjectiveNumerous techniques for bone remodeling simulation have been developed based on Wolff's law. However, most studies have been conducted with empirically determined static loads, which cannot reflect subject-specific characteristics. We recently proposed a new concept of representative static loads (RSLs) to efficiently consider the effect of cyclically repeated dynamic loads on bone remodeling simulation. Based on this concept, the goal of this study is to sample the subject-specific static loads (SSL) from a general gait cycle of interindividual variation. MethodsA total of 15 gait cycles (ten normal and five abnormal cycles) obtained from the public database were used in this study. Each gait cycle was applied to a femur FE model constructed from the clinical CT scan data to evaluate the strain energy distribution as a reference. Then, a natural coordinate was introduced to maintain the predefined locations of extreme points (i.e., two peaks and one valley) for both normal and abnormal gait cycles. To determine the RSLs in the natural coordinate, five out of ten normal gait cycles were used. Through an inverse transformation for each gait cycle, the RSLs in the natural coordinate were converted to the SSLs in the original coordinate. Topology optimization results with the proposed SSLs were compared with those with a single full gait cycle (reference). For comparison, topology optimization was also conducted with empirically determined loads (EDLs) which have been widely used in the literature. ResultsFor normal gait cycles, the proposed SSLs reduced the average computing cost by 95.86% while suppressing the errors of bone mass distribution and apparent stiffness below maximum 4.24% and 1.72%, respectively. Even for abnormal gait cycles, the errors of bone mass distribution and apparent stiffness were suppressed below maximum 9.49% and 2.12%, respectively. Conversely, the conventional EDLs (peak loads selected in this study) showed significantly larger errors of maximum 47.28% and 30.31% in bone mass distribution and apparent stiffness for normal gait cycles. ConclusionBy virtue of using the coordinate transformation for each gait cycle, the proposed SSLs achieved a higher accuracy in the bone mass distribution and apparent stiffness than the previous RSLs and EDLs. Furthermore, this approach can be used for abnormal gait cycles which have higher interindividual variation.

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes.

The study of plantar pressure has become a research consensus in the field of biomechanics. The purpose of this paper is to study some lower limb movements in the daily activities of ice and snow athletes to obtain relevant data so as to carry out gait recognition analysis research. This paper selects the average foot pressure, forefoot foot pressure, front and rear foot pressure, foot pressure, toe pressure, 2–5 toe pressure, standing with eyes closed, x- and y-axes speed, foot length, foot width, and other actions of ice and snow athletes. Therefore, correlation analysis, work analysis, and curve fitting analysis were carried out on the joint motion in a single gait cycle. The collection and application of foot pressure and foot posture information are also analyzed. According to the plantar structure, the sole is divided into four parts. The maximum pressure point and coordinates of each part, the pressure center point, the ratio of the width and height of the sole of the foot, and so on are extracted as the haptic features of the gait. The experimental data shows that it can be seen that if the plantar area is divided in advance and the weight of each area is marked, whether standing, walking, or standing with one leg closed eyes can achieve better recognition results, and the accuracy rate is all more than 90 percent. The average recognition accuracy rate using the method of dividing four regions is only about 80%, and the accuracy rate of recognition using the method of dividing eight regions is 82%. It can be seen that the features extracted by the FCM model proposed in this paper contain more information of the plantar pressure image, and the accuracy rate is higher in the classification and recognition.

Changes in hip joint contact stress during a gait cycle based on the individualized modeling method of “gait-musculoskeletal system-finite element”

ObjectiveTo construct a comprehensive simulation method of “gait-musculoskeletal system (MS)-finite element (FE)” for analysis of hip joint dynamics characteristics and the changes in the contact stress in the hip throughout a gait cycle.MethodsTwo healthy volunteers (male and female) were recruited. The 3D gait trajectories during normal walking and the CT images including the hip and femur of the volunteers were obtained. CT imaging data in the DICOM format were extracted for subjected 3D hip joint reconstruction. The reconstructed 3D model files were used to realize the subject-specific registration of the pelvis and thigh segment of general musculoskeletal model. The captured marker trajectory data were used to drive subject-specific musculoskeletal model to complete inverse dynamic analysis. Results of inverse dynamic analysis were exported and applied as boundary and load settings of the hip joint finite element in ABAQUS. Finally, the finite element analysis (FEA) was performed to analyze contact stress of hip joint during a gait cycle of left foot.ResultsIn the inverse dynamic analysis, the dynamic changes of the main hip-femoral muscle force with respect to each phase of a single gait cycle were plotted. The hip joint reaction force reached a maximum value of 2.9%BW (body weight) and appeared at the end of the terminal stance phase. Twin peaks appeared at the initial contact phase and the end of the terminal stance phase, respectively. FEA showed the temporal changes in contact stress in the acetabulum. In the visual stress cloud chart, the acetabular contact stress was mainly distributed in the dome of the acetabulum and in the anterolateral area at the top of the femoral head during a single gait cycle. The acetabular contact area was between 293.8 and 998.4 mm2, and the maximum contact area appear at the mid-stance phase or the loading response phase of gait. The maximum contact stress of the acetabulum reached 6.91 MPa for the model 1 and 6.92 MPa for the model 2 at the terminal stance phase.ConclusionsThe “Gait-MS-FE” technology is integrated to construct a comprehensive simulation framework. Based on human gait trajectories and their CT images, individualized simulation modeling can be achieved. Subject-specific gait in combination with an inverse dynamic analysis of the MS provides pre-processing parameters for FE simulation for more accurate biomechanical analysis of hip joint.Graphical abstract

Effects of the Anterolateral Ligament and Anterior Cruciate Ligament on Knee Joint Mechanics: A Biomechanical Study Using Computational Modeling.

Background:Recent studies on lateral knee anatomy have reported the presence of a true ligament structure, the anterolateral ligament (ALL), in the anterolateral region of the knee joint. However, its biomechanical effects have not been fully elucidated.Purpose:To investigate, by using computer simulation, the association between the ALL and anterior cruciate ligament (ACL) under dynamic loading conditions.Study Design:Descriptive laboratory study; Level of evidence, 5.Methods:The authors combined medical imaging from 5 healthy participants with motion capture to create participant-specific knee models that simulated the entire 12 degrees of freedom of tibiofemoral (TF) and patellofemoral (PF) joint behaviors. These dynamic computational models were validated using electromyographic data, muscle activation data, and data from previous experimental studies. Forces exerted on the ALL with ACL deficiency and on the ACL with ALL deficiency, as well as TF and PF contact forces with deficiencies of the ACL, ALL, and the entire ligament structure, were evaluated under gait and squat loading. A single gait cycle and squat cycle were divided into 11 time points (periods 0.0-1.0). Simulated ligament forces and contact forces were compared using nonparametric repeated-measures Friedman tests.Results:Force exerted on the ALL significantly increased with ACL deficiency under both gait- and squat-loading conditions. With ACL deficiency, the mean force on the ALL increased by 129.7% under gait loading in the 0.4 period (P < .05) and increased by 189% under high flexion during the entire cycle of squat loading (P < .05). A similar trend of significantly increased force on the ACL was observed with ALL deficiency. Contact forces on the TF and PF joints with deficiencies of the ACL, ALL, and entire ligament structure showed a complicated pattern. However, contact force exerted on TF and PF joints with respect to deficiencies of ACL and ALL significantly increased under both gait- and squat-loading conditions.Conclusion:The results of this computer simulation study indicate that the ACL and the ALL of the lateral knee joint act as secondary stabilizers to each other under dynamic load conditions.

Feature selection to classify lameness using a smartphone-based inertial measurement unit.

Background and objectivesGait can be severely affected by pain, muscle weakness, and aging resulting in lameness. Despite the high incidence of lameness, there are no studies on the features that are useful for classifying lameness patterns. Therefore, we aimed to identify features of high importance for classifying population differences in lameness patterns using an inertial measurement unit mounted above the sacral region.MethodsFeatures computed exhaustively for multidimensional time series consisting of three-axis angular velocities and three-axis acceleration were carefully selected using the Benjamini–Yekutieli procedure, and multiclass classification was performed using LightGBM (Microsoft Corp., Redmond, WA, USA). We calculated the relative importance of the features that contributed to the classification task in machine learning.ResultsThe most important feature was found to be the absolute value of the Fourier coefficients of the second frequency calculated by the one-dimensional discrete Fourier transform for real input. This was determined by the fast Fourier transformation algorithm using data of a single gait cycle of the yaw angular velocity of the pelvic region.ConclusionsUsing an inertial measurement unit worn over the sacral region, we determined a set of features of high importance for classifying differences in lameness patterns based on different factors. This completely new set of indicators can be used to advance the understanding of lameness.

A four-bar knee joint measurement walking system for prosthesis design.

Gait analysis is important for the lower limb prosthesis design. Simulating the natural motion of the human knee in different terrains is useful for the design and performance assessment of the prosthetic knee. This study aimed to propose a four-bar knee joint measurement system which can simulate the natural knee motions to collect the kinetic parameters precisely and analyze the walking characteristics under different terrain conditions. A low-cost four-bar knee joint mechanism was proposed and gait characteristics were assessed on level ground, ascending and descending stairs, and ascending and descending ramp. The initial knee flexion angle during stair ascent at heel strike is obviously larger than in other walking scenes. The stance phase accounts for 53% of a single gait cycle during stair descent, which is slightly lower than other walking scenarios. The period that both the hindfoot and forefoot contact the ground in ramp descent accounts for 18%, which is less than for the others. While the forefoot contacts the ground in ramp ascent, the maximum vertical ground reaction force of the forefoot occurs when the hindfoot and forefoot simultaneously contact the ground, whereas in other scenarios the forefoot contacts the ground solely. The four-bar knee joint can simulate the natural motion of the human knee accurately. The gait characteristics analysis of different walking scenarios indicated that the low-cost four-bar knee joint exoskeleton was suitable for human knee joint simulation.

In silico total hip replacement wear testing in the framework of ISO 14242-3 accounting for mixed elasto-hydrodynamic lubrication effects

Currently, preclinical wear tests for total hip replacements is carried out by using mechanical simulators under a simplified normal gait cycle as recommended by the international guidelines ISO-14242. Nevertheless, the wear tests have a long duration and are expensive so that the aim of this paper was to propose a novel in silico approach for wear calculation in lubricated total hip replacements.The proposed in silico simulation is based on the accurate modelling of the tribological phenomena acting in the joint during certain kinematical and dynamical conditions under non-Newtonian unsteady mixed elasto-hydrodynamic lubrication conditions (MEHL). The analyzed hip joint is modeled as soft polymeric acetabular cup, PE GUR1020 (EtO), against hard metallic CoCrMo femoral head. The calculation of the fluid film pressure field within the joint gap is achieved during the in silico simulations by imposing the ISO 14242-3 kinematics and dynamics, to evaluate the cumulated wear volume over a single gait cycle by using a modified Archard’s model.The main purpose of this study is to propose an in-silico wear calculation approach reproducing the classical in vitro wear testing of total hip replacements accounting for unsteady synovial lubrication effects between the femoral head and the acetabular cup. The predicted wear, extrapolated from a novel proposed approach based on the wear slopes, was compared with the value from in vitro simulations showing a satisfactory agreement, allowing the authors to conclude that the proposed model is suitable to be used as in silico hip joint wear simulator.

Erkennung und Klassifikation von Haltungs- und Gangmustern am Rollator durch Abstandsmessungen – ein Vergleich zwischen klinischer Beurteilung und maschineller Klassifikation

This article describes the development of an add-on module for wheeled walkers dedicated to sensor-based posture and gait pattern recognition with the goal to develop an everyday aid for fall prevention. The core contribution is aclinical study that compared single gait parameter assessments coming from medical staff to those obtained from an automatic classification algorithm, i. e. the Mahalanobis distance over time series of sensor measurements. The walker-module described here extends an off-the-shelf wheeled walker by two depth cameras that observe the torso, pelvic, region and legs of the user. From the stream of depth images, distance measurements to eight relevant feature points on the body surface (shoulders, iliac crests, upper and lower legs) are combined to time series that describe the individual gait cycles. For automatic classification of gait cycle descriptions 14 safety-relevant gait parameters (gait width, height, length, symmetry, variability; flection of torso, knees (l/r), hips (l/r); position, distance to walker; 2‑value, 5‑value gait patterns [While the two-value gait pattern differentiates agait cycle into physiological and pathological, the five-value gait pattern distinguishes between antalgic, atactic, paretic, protective, and physiological gait]), single classifier algorithms were trained using machine learning techniques based on the mathematical concept of the Mahalanobis distance (distance of individual gait cycles to class averages and corresponding covariance matrices). For this purpose, training and test datasets were gathered in aclinical setting from 29subjects. Here, the assessment of gait properties given by medical experts served for the labelling of sensorial gait cycle descriptions of the training and test datasets. In order to evaluate the quality of the automated classification in the add-on module afinal comparison between human and automatic gait parameter assessment is given. The gait assessment conducted by trained medical staff served as acomparator for the machine learning gait assessment and showed arelatively uniform class distribution of gait parameters over the group of probands, e. g. 57% showed an increased and 43% anormal distance to the walker. Of the subjects 51% positioned themselves central to the walker, while 41% took aleft deviating, and 8% aright deviating position. A further 12 gait parameters were differentiated and evaluated in 2-5 classes. In the following, single gait cycle descriptions of each subject were assessed by trained classification algorithms. The best automatic classification rates over all subjects were given by the distance to walker (99.4%), and the 2-value gait pattern (99.2%). Gait variability (94.6%) and position to walker (94.2%) showed the poorest classification rates. Over all gait parameters and subjects, 96.9% of all gait cycle descriptions were correctly classified. With an average classification rate of 96.9%, the described gait classification approach is well suited for apatient-oriented training correction system that informs the user about false posture during every day walker use. Asecond application scenario is the use in aclinical setting for objectifying the gait assessment of patients. To reach these ambitious goals requires more future research. It includes the replacement of depth cameras by small size distance sensors (1D Lidar), the design and implementation of asuitable walker-user interface, and the evaluation of the proposed classification algorithm by contrasting it to results of modern deep convolutional neural network output.

Effects of passive and active joint compliance in quadrupedal locomotion

ABSTRACTCompliance of the body has a crucial role on locomotion performance. The levels and the distribution of compliance should be well tuned to obtain efficient gait. The leg stiffness changes significantly even during different phases of a single gait cycle. This paper presents an experimental study on different passive and active limb compliance configurations. Each configuration is tested on flat, rough and inclined-rough surfaces, to analyze locomotion performance in diverse conditions. As the active compliance mechanism, Tegotae-based control is selected. Even though active compliance is not its primary use, we show that the Tegotae rule presents intriguing features that have potential to boost gait performance in various scenarios.

Load exposure of osseointegrated implants for transfemoral limb prosthesis during running.

Direct skeletal attachment of lower limb prostheses ensures direct load transfer between the prosthetic leg and the skeleton. Knowledge of the load characteristics at the boneimplant interface during high-loading activities is needed to understand the limitations of current implant systems, as well as to inform their future development. The present study estimates the load scenario at the bone-implant interface of a transfemoral amputee while running with kinematic symmetry between the prosthetic and the intact limbs corresponding to that of an ablebodied subject. Kinematic symmetry was used as this represents the ultimate aim of advanced bionic legs. Kinematic data and ground reaction forces from a running trial of an able-bodied subject were matched to a musculoskeletal model of a transfemoral amputee. The joint reaction forces at the boneimplant interface were calculated using inverse dynamics. The normalized peak forces and moments during a single gait cycle were calculated to 153 % BW (body weight) / -14.8 % BWm, 186 % BW / 16.2 % BWm and 56.8 % BW / -18.7 % BWm for the x- (anterior), y- (longitudinal), and z-axis (lateral-medial), respectively. These findings can potentially be used as design input for future implant systems and external safety devices.

Quadruped Robot Leg Optimization Based on a Multi-Objective Genetic Algorithm

To improve the speed of a quadruped robot, a novel electric-drive robot leg based on a double four-bar mechanism is proposed. This scheme avoids the need for constant alternation between the acceleration and deceleration of the motors and may thus achieve a higher rotation speed. However, the dimensional parameters of the double four-bar mechanism have a significant impact on the dynamic performance of the robot. To determine the optimal dimensions, a kinematic and dynamic analysis of the proposed robot leg is carried out, and the functional relations between the joint motors’ peak torque, peak angular velocity and total energy consumption in a single gait cycle and the dimensional parameters are determined. In this way, a multi-objective optimization model for the leg design is constructed, and the gamultiobj algorithm is applied to obtain the Pareto optimal solution set of the optimization model. By determining the weight of each objective function based on a combination weighting method, the optimal dimensions of the leg are obtained from the Pareto optimal solution. Finally, a virtual prototype of the robot leg is built via ADAMS to conduct a walking simulation, and the physical prototype is installed on a self-developed test bench of one-legged movement to conduct a no-load walking experiment. The results of the theoretical calculations and simulation are well coincident with those of the tests, which verify the correctness and validity of the proposed algorithm. DOI: http://dx.doi.org/10.5755/j01.mech.23.6.19854

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults.

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques have been employed, each with positives and negatives. The aim was to determine the effect of NW on older adult gait and posture and to determine optimal use of different data collection systems in both short and long duration analysis. Gait and posture during NW and normal walking were assessed in 17 healthy older adults (age: 69 ± 7.3). Participants performed two trials of 6 Minute Walk Tests (6MWT) (1 with poles (WP) and 1 without poles (NP)) and 6 trials of a 5m walk (3 WP and 3 NP). Motion was recorded using two systems, a 6-sensor accelerometry system and an 8-camera 3-dimensional motion capture system, in order to quantify spatial-temporal, kinematic, and kinetic parameters. With both systems, participants demonstrated increased stride length and double support and decreased gait speed and cadence WP compared to NP (p <0.05). Also, with motion capture, larger single support time was found WP (p <0.05). With 3-D capture, smaller hip power generation and moments of force were found at heel contact and pre-swing as well as smaller knee power absorption at heel contact, pre-swing, and terminal swing WP compared to NP, when assessed over one cycle (p <0.05). Also, WP yielded smaller moments of force at heel contact and terminal swing along with larger moments at mid-stance of a gait cycle (p <0.05). No changes were found for posture. NW seems appropriate for promoting a normal gait pattern in older adults. Three-dimensional motion capture should primarily be used during short duration gait analysis (i.e. single gait cycle), while accelerometry systems should be primarily employed in instances requiring longer duration analysis such as during the 6MWT.

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques have been employed, each with positives and negatives. The aim was to determine the effect of NW on older adult gait and posture and to determine optimal use of different data collection systems in both short and long duration analysis. Gait and posture during NW and normal walking were assessed in 17 healthy older adults (age: 69 ± 7.3). Participants performed two trials of 6 Minute Walk Tests (6MWT) (1 with poles (WP) and 1 without poles (NP)) and 6 trials of a 5m walk (3 WP and 3 NP). Motion was recorded using two systems, a 6-sensor accelerometry system and an 8-camera 3-dimensional motion capture system, in order to quantify spatial-temporal, kinematic, and kinetic parameters. With both systems, participants demonstrated increased stride length and double support and decreased gait speed and cadence WP compared to NP (p <0.05). Also, with motion capture, larger single support time was found WP (p <0.05). With 3-D capture, smaller hip power generation and moments of force were found at heel contact and pre-swing as well as smaller knee power absorption at heel contact, pre-swing, and terminal swing WP compared to NP, when assessed over one cycle (p <0.05). Also, WP yielded smaller moments of force at heel contact and terminal swing along with larger moments at mid-stance of a gait cycle (p <0.05). No changes were found for posture. NW seems appropriate for promoting a normal gait pattern in older adults. Three-dimensional motion capture should primarily be used during short duration gait analysis (i.e. single gait cycle), while accelerometry systems should be primarily employed in instances requiring longer duration analysis such as during the 6MWT.

Effect of Human Movement on Galvanic Intra-Body Communication during Single Gait Cycle

Intra-body communication (IBC) is a communication system that uses human body as a signal transmission medium. From previous research, two coupling methods of IBC were concluded which are capacitive coupling and galvanic coupling. This paper investigates the effect of human movement on IBC using the galvanic coupling method. Because the human movement is control by the limb joint, the knee flexion angle during gait cycle was used to examine the influence of human movement on galvanic coupling IBC. The gait cycle is a cycle of people walking that start from one foot touch the ground till that foot touch the ground again. Frequency range from 300 kHz to 200MHz was swept in order to investigate the signal transmission loss and the result was focused on operating frequency 70MHz to 90MHz. Results show that the transmission loss varies when the knee flexion angle increased. The highest loss of signal at frequency range between 70MHz to 90 MHz was 69dB when the knee flexion angle is 50° and the minimum loss was 51dB during the flexion angle is 5°.

Comparison between an accelerometer and a three dimensional motion analysis system for estimating the vertical displacement of center of mass

Background: The vertical displacement of center of mass (CoM) is related to walking efficiency, and is an important parameter to assess gait disorder. There are several methods to estimate CoM, for example, by using a force plate or a three-dimensional motion analysis system. However, these equipment are time-consuming, costly, limited to a single gait cycle, and restricted to a laboratory environment. One possible alternative to laboratory equipment is gait analysis by means of an accelerometer. This method permits a natural walking pattern over many repeated cycles. There have been previous studies comparing between accelerometer method and gold standard (e.g., force plate method and body segmental analysis method) for estimating the vertical displacement CoM at a comfortable walking speed, which show excellent relationship between the two methods, but little is known about the influence of walking speed.

A Deterministic Model of Human Motion Based on Algebraic Techniques and a Sensor Network to Simulate Shoulder Kinematics.

Limiting the quantitative characterization of ambulatory mobility to only the two-dimensional sagittal plane through the investigation of key kinematic parameters, may still inform scientists and bioengineers of critical elements of joint locomotion. This paper presents the initial validation of a deterministic biomechanical gait model that was derived from an inverse kinematic analysis of three-dimensional upper extremity movement. Algebraic methods were applied to generate shoulder flexion and extension angles during a single gait cycle during normal walking. The direct kinematic measurements from a motion capture system were analyzed and compared to the predicted measurements from the algebraic model for eight healthy, human subjects. The predicted results over all subjects varied from the actual joint angle measurements with a nominal amount of mean error (23%), while correlations were quite strong (mean R2 = 0.97). These findings indicate the potential value of deterministic modeling with algebraic techniques as an alternative to existing methods.