Limb Joint Angles Research Articles

Experimental evaluation of indoor magnetic distortion effects on gait analysis performed with wearable inertial sensors

Magnetic inertial measurement unit systems (MIMU) offer the potential to perform joint kinematics evaluation as an alternative to optoelectronic systems (OS). Several studies have reported the effect of indoor magnetic field disturbances on the MIMU's heading output, even though the overall effect on the evaluation of lower limb joint kinematics is not yet fully explored. The aim of the study is to assess the influence of indoor magnetic field distortion on gait analysis trials conducted with a commercial MIMU system. A healthy adult performed gait analysis sessions both indoors and outdoors. Data collected indoors were post-processed with and without a heading correction methodology performed with OS at the start of the gait trial. The performance of the MIMU system is characterized in terms of indices, based on the mean value of lower limb joint angles and the associated ROM, quantifying the system repeatability. We find that the effects of magnetic field distortion, such as the one we experienced in our lab, were limited to the transverse plane of each joint and to the frontal plane of the ankle. Sagittal plane values, instead, showed sufficient repeatability moving from outdoors to indoors. Our findings provide indications to clinicians on MIMU performance in the measurement of lower limb kinematics.

Dynamic Optimization of FES and Orthosis-Based Walking Using Simple Models

Computation of an analytical control solution for functional electrical stimulation (FES) and orthosis-based walking is a daunting task due to the inherent nonlinear structure of the human muscle and walking dynamics. Furthermore, since muscle fatigue and available muscle force are major limiting issues, we explored the domains of numerical optimal control methods to address these issues. We first focused on the development of simple models to represent walking movement. These models account for walking produced via a limited number of activated muscles using FES along with a novel orthosis, and an assistive device such as a walker. Using dynamic optimization, the lower limb joint angle trajectories and control inputs were computed by minimizing the cost function comprising muscle stimulation variables and forces required to push a walker. Computer simulations for optimizations were performed across a range of step lengths to find the optimal step length (minimum cost per distance). Then, the optimal steady-state initial angular velocity (for optimal step length) was computed from a range of angular velocities of the lower-limb segments. We found considerable differences between able-bodied walking trajectories and the optimal walking trajectories for FES and orthosis-based walking. Based on this computer simulation study, we recommend that instead of arbitrary selection of stimulation profiles or gait parameters, dynamic optimization can be utilized to compute gait parameters such as step length, steady state velocity, and joint angle trajectories in future clinical implementation of FES and orthosis-based walking.

성인의 하향계단 보행 시 중량에 따른 하지의 운동역학적 변인 분석

The purpose of this study was to analyze the effect that various loads have on the lower limb biomechanics. The following variables were measured and analyzed; performance time for each phase, lower limb moments and joint angles, and ground reaction forces. The kinematic and kinetic data was recorded by 2 force platforms and a motion capture system while 12 healthy adults in their twenties stepped down three steps under loads of 0%, 10%, 20% BW. Results are as follows. First, the different loading conditions did not seem to significantly affect the performance times and the joint angles. Second, the largest ground reaction forces were observed at the 1 step at the 10% BW condition. Finally, at the 0% BW loading condition the right hip extension moment was the smallest and the left hip flexion moment was the largest. The results show that there are not any significant changes in the biomechanics of the lower limbs under loading conditions up to 20% BW. Further investigations including more loading conditions with more weights and more additional steps analyzed are needed.

An individual-specific gait pattern prediction model based on generalized regression neural networks

Robotics is gaining its popularity in gait rehabilitation. Gait pattern planning is important to ensure that the gait patterns induced by robotic systems are tailored to each individual and varying walking speed. Most research groups planned gait patterns for their robotics systems based on Clinical Gait Analysis (CGA) data. The major problem with the method using the CGA data is that it cannot accommodate inter-subject differences. In addition, CGA data is limited to only one walking speed as per the published data. The objective of this work was to develop an individual-specific gait pattern prediction model for gait pattern planning in the robotic gait rehabilitation systems. The waveforms of lower limb joint angles in the sagittal plane during walking were obtained with a motion capture system. Each waveform was represented and reconstructed by a Fourier coefficient vector which consisted of eleven elements. Generalized regression neural networks (GRNNs) were designed to predict Fourier coefficient vectors from given gait parameters and lower limb anthropometric data. The generated waveforms from the predicted Fourier coefficient vectors were compared to the actual waveforms and CGA waveforms by using the assessment parameters of correlation coefficients, mean absolute deviation (MAD) and threshold absolute deviation (TAD). The results showed that lower limb joint angle waveforms generated by the gait pattern prediction model were closer to the actual waveforms compared to the CGA waveforms.

Effect of a Pessoa training aid on temporal, linear and angular variables of the working trot

Development of core muscle strength is important for training and during rehabilitation following injury. A Pessoa training aid (PTA) is a system of ropes and pulleys which is commonly used during equine training and rehabilitation, but there is limited information on its effectiveness. The objective of the study was to determine the effect of the PTA on the temporal, linear and angular kinematics of the working trot. Influence of testing order on effect of a PTA was assessed in four horses. Twelve riding horses were lunged at working trot on a 16m diameter circle without (WO) and with a PTA (WP) that was set level with the shoulder. Objective measurements were carried out using high-speed motion capture (125Hz) and inertial measurement units. Subjective video assessment was also undertaken.When a PTA was applied there was a significant reduction in speed, stride length, head angle (P<0.0001 for all) and lumbosacral angle at maximal hindlimb retraction (P=0.0028), but no effect on limb joint angles. The highest point of the horse was significantly different between conditions (WO, poll; WP, crest) (P=0.0010). Dorsoventral displacement of the middle of the back (P⩽0.0001) and overall impression grade (P=0.0002) were significantly greater WP compared with WO. These findings indicated that a PTA may be beneficial for general training and rehabilitation as a method of improving posture, stimulating core muscle activation and improving overall way of going, without increasing the loading of forelimb and hindlimb structures. Further work is warranted to understand the mechanism which induces these changes.

Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics

This study aims to validate a commercially available inertial sensor based motion capture system, Xsens MVN BIOMECH using its native protocols, against a camera-based motion capture system for the measurement of joint angular kinematics. Performance was evaluated by comparing waveform similarity using range of motion, mean error and a new formulation of the coefficient of multiple correlation (CMC). Three dimensional joint angles of the lower limbs were determined for ten healthy subjects while they performed three daily activities: level walking, stair ascent, and stair descent. Under all three walking conditions, the Xsens system most accurately determined the flexion/extension joint angle (CMC > 0.96) for all joints. The joint angle measurements associated with the other two joint axes had lower correlation including complex CMC values. The poor correlation in the other two joint axes is most likely due to differences in the anatomical frame definition of limb segments used by the Xsens and Optotrak systems. Implementation of a protocol to align these two systems is necessary when comparing joint angle waveforms measured by the Xsens and other motion capture systems.

Model-Based Human Gait Recognition Via Deterministic Learning

In this paper, we present a new model-based approach for human gait recognition in the sagittal plane via deterministic learning (DL) theory. Side silhouette lower limb joint angles characterize the gait system dynamics and are selected as the gait feature. Locally accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks through DL. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern can be rapidly recognized according to the smallest error principle. In contrast to other existing approaches, the main focus of this paper is on obtaining and reusing the knowledge of the gait system dynamics. Finally, experiments are carried out on the CASIA-A and CASIA-B gait databases to benchmark the effectiveness of the proposed approach.

Human motion tracking and recognition using HMM by a mobile robot

PurposeThe aim of this paper is to develop autonomous mobile home healthcare robots, which are capable of observing patients’ motions, recognizing the patients’ behaviours based on observation data, and providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost‐effective, safe and easier at‐home rehabilitation to most motor‐function impaired patients (MIPs).Design/methodology/approachThe paper has developed following programs/control algorithms: control algorithms for a mobile robot to track and follow human motions, to measure human joint trajectories, and to calculate angles of lower limb joints; and algorithms for recognizing human gait behaviours based on the calculated joints angle data.FindingsA Hidden Markov Model (HMM) based human gait behaviour recognition taking lower limb joint angles and body angle as input was proposed. The proposed HMM based gait behaviour recognition is compared with the Nearest Neighbour (NN) classification methods. Experimental results showed that a human gait behaviour recognition using HMM can be achieved from the lower limb joint trajectory with higher accuracy than compared classification methods.Originality/valueThe research addresses human motion tracking and recognition by a mobile robot. Human gait behaviour recognition is HMM based lower limb joints and body angle data from extracted from kinect sensor at the mobile robot.

J151021 脚部関節運動に着目した水泳用大腿義足の研究([J151-02]ライフサポートとQOL(2))

The objective of this study was to develop a prototype of transfemoral prosthesis for swimming focused on the leg joint motion. This prosthesis has (i) an ankle joint and a knee joint which provide appropriate leg motion and (ii) a prosthesis whose appearance is sufficiently similar to an actual one. In order to confirm the validity of the developed prosthesis, a swimming experiment, in which a subject swimmer swam in a pool with the developed prosthesis attached, was conducted. The joint angles of the lower limbs were measured and an evaluation with questionnaires was performed. The validity of the proposed prosthesis was experimentally confirmed since the movable joints with appropriate spring yielded a better swimming performance and more comfort to the swimmer.

Estimation of Upper Limb Joint Angle Using Surface EMG Signal

In the development of robot-assisted rehabilitation systems for upper limb rehabilitation therapy, human electromyogram (EMG) is widely used due to its ability to detect the user intended motion. EMG is one kind of biological signal that can be recorded to evaluate the performance of skeletal muscles by means of a sensor electrode. Based on recorded EMG signals, user intended motion could be extracted via estimation of joint torque, force or angle. Therefore, this estimation becomes one of the most important factors to achieve accurate user intended motion. In this paper, an upper limb joint angle estimation methodology is proposed. A back propagation neural network (BPNN) is developed to estimate the shoulder and elbow joint angles from the recorded EMG signals. A Virtual Human Model (VHM) is also developed and integrated with BPNN to perform the simulation of the estimated angle. The relationships between sEMG signals and upper limb movements are observed in this paper. The effectiveness of our developments is evaluated with four healthy subjects and a VHM simulation. The results show that the methodology can be used in the estimation of joint angles based on EMG.

Biomechanical and Proprioceptive Differences during Drop Landings between Dancers and Non-dancers

The focus of this research was to determine if female dancers have differing kinematic and kinetic characteristics when landing from three heights (0.2, 0.5, and 0.8 m) both with and without vision compared to non-dancers. It was hypothesized that dancers would show differing kinematic and kinetic patterns of control due to their increased proprioceptive awareness. Eight collegiate dancers and seven collegiate controls who were neither dancers nor collegiate jumping athletes volunteered for this study. Sagittal plane lower limb joint angles were measured at 100 Hz prior to landing through stability with a high-speed camera, and peak vertical ground reaction forces relative to body weight were recorded with an indwelling force plate. Results indicated biomechanical differences across height and vision conditions, as well as between groups. Kinetic results showed a significant height effect with respect to vertical ground reaction forces. From the 0.8 m drop, both dancers and non-dancers produced significantly greater ground reaction forces when landing without vision compared to when they landed with vision. No significant kinetic differences were found between groups. Kinematic results revealed a significant height effect for the hip and knee angles across groups and vision conditions, meaning that as drop height increased, the participants demonstrated greater range of motion in their hip and knee joints. Dancers and non-dancers responded differently when dropping from 0.8 m without vision. Dancers significantly increased hip flexion compared to landing with vision, while non-dancers tended to stiffen up and reduced hip flexion. These findings suggest that dancers utilize proprioceptive input more effectively as they adopted a hip strategy (flexion of the hips) to maintain stability. Training dancers without vision may impact dance instruction and reduce the risk of injuries when landing.

The influences of limitation of the ankle dorsiflexion and gait speed to lower limb joint angles and muscle activities during gait

<b>The influences of limitation of the ankle dorsiflexion and gait speed to lower limb joint angles and muscle activities during gait </b>

A Validation Test of Measurement Method of Lower Limb Angles Based on Kalman Filter on Different Type of Inertial Sensors

Lower limb joint angle measurement method based on Kalman filter was tested using commercially available inertial sensors in our previous study for a rehabilitation use. Although the angle measurement method was effective, measurement with the wireless sensors were affected by wireless communication environment. Wired and wireless inertial sensor systems were developed in our laboratory for solving the wireless communication problem. This paper aimed to examine the 2 developed sensors, since those were different from the previously used sensors in data communication system or resolution in measurement. The 2 developed sensors and 2 commercially available sensors were evaluated in measurements of inclination and joint angles of a rigid body model comparing to the results of an optical motion measurement system. The angles were measured by correcting the angles calculated from outputs of gyroscopes based on a Kalman filter using outputs of accelerometers. All of the sensors showed small RMS errors under the static and dynamic conditions. The results suggested that various inertial sensors could measure inclination and joint angles stably if angles are calculated by using the Kalman filtering based method. It would be possible to replace sensors with inexpensive or latest commercially available sensors.

HUMAN GAIT BEHAVIOR INTERPRETATION BY A MOBILE HOME HEALTHCARE ROBOT

The ultimate goal of this study is to develop autonomous mobile home healthcare robots which closely monitor and evaluate the patients' motor function, and their at-home training therapy process, providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs), and meanwhile, relieve therapists from great burden in canonical rehabilitation. In order to achieve this goal, we have developed the following programs/algorithms for monitoring human activities and recognizing human behaviors: (1) control programs for a mobile robot to track and follow a human subject by three different viewpoints; (2) algorithms for analyzing lower limb joint angles from RGB-D images from a Kinect sensor setup at a mobile robot; and (3) algorithms for recognizing human gait behavior. In (1), side viewpoint, front/back viewpoint and a middle angle viewpoint (between two former viewpoints) tracking were developed. In (2), depth image compensation with colored markers was implemented to deal with the skeleton point extraction error caused by mixing-up and frame flying of depth image during tracking and following human subjects by the mobile robot. In (3), we have proposed a hidden Markov model (HMM) based human behavior recognition using lower limb joint angles and trunk angle. Experimental results showed that joint trajectory could be measured and analyzed with high accuracy compared to a motion tracking system, and human behavior could be recognized from the joint trajectory.

Observational training in visual half-fields and the coding of movement sequences

An experiment was conducted to determine if gating information to different hemispheres during observational training facilitates the development of a movement representation. Participants were randomly assigned to one of three observation groups that differed in terms of the type of visual half-field presentation during observation (right visual half-field (RVF), left visual half-field (LVF), or in central position (CE)), and a control group (CG). On Day 1, visual stimuli indicating the pattern of movement to be produced were projected on the respective hemisphere. The task participants observed was a 1300ms spatial-temporal pattern of elbow flexions and extensions. On Day 2, participants physically performed the task in an inter-manual transfer paradigm with a retention test, and two contralateral transfer tests; a mirror transfer test which required the same pattern of muscle activation and limb joint angles and a non-mirror transfer test which reinstated the visual-spatial pattern of the sequence. The results demonstrated that participants of the CE, RVF and the LVF groups showed superior retention and transfer performance compared to participants of the CG. Participants of the CE- and LVF-groups demonstrated an advantage when the visual-spatial coordinates were reinstated compared to the motor coordinates, while participants of the RVF-group did not promote specific transfer patterns. These results will be discussed in the context of hemisphere specialization.

Human gait recognition via deterministic learning

Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. Human gait recognition is a typical difficulty in the area of dynamical pattern recognition. It classifies and identifies individuals by their time-varying gait signature data. Recently, a new dynamical pattern recognition method based on deterministic learning theory was presented, in which a time-varying dynamical pattern can be effectively represented in a time-invariant manner and can be rapidly recognized. In this paper, we present a new model-based approach for human gait recognition via the aforementioned method, specifically for recognizing people by gait. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, side silhouette lower limb joint angles and angular velocities are selected as gait features. A five-link biped model for human gait locomotion is employed to demonstrate that functions containing joint angle and angular velocity state vectors characterize the gait system dynamics. Due to the quasi-periodic and symmetrical characteristics of human gait, the gait system dynamics can be simplified to be described by functions of joint angles and angular velocities of one side of the human body, thus the feature dimension is effectively reduced. Locally-accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks (NNs) through deterministic learning. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles versus angular velocities. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. In the test phase, by comparing the set of estimators with the test gait pattern, a set of recognition errors are generated, and the average L(1) norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern similar to one of the training gait patterns can be rapidly recognized according to the smallest error principle. Finally, experiments are carried out on the NLPR and UCSD gait databases to demonstrate the effectiveness of the proposed approach.

Effects of body positions on the saddle on pedalling technique for cyclists and triathletes

Cyclists usually change their body position on the saddle depending on the characteristics of the race. We compared the effects of cycling at three body positions on the saddle (preferred/self-selected, most forward, most backward) on pedalling technique for cyclists and triathletes. Twelve cyclists and nine triathletes performed four trials starting with the maximal aerobic workload, followed by three trials at the workload of their ventilatory threshold. Force applied on the right pedal via an instrumented pedal, lower limb kinematics via video and muscle activation via electromyography were recorded during all trials. Pedalling technique was quantified using total force applied on the pedal, pedal force effectiveness, activation of six lower limb muscles, joint angles and mechanical work at the ankle, knee and hip joints. Analyses using effect sizes showed no large effects from changes in position on the saddle for pedal forces, ankle joint work and ankle kinematics. There were large increases in knee joint angle and mechanical work and rectus femoris activation along with smaller hip work at the forward position on the saddle. Differences between cyclists and triathletes were not substantial. Effects of changes in saddle positions were limited to the hip and knee joints.

Scheduling observational and physical practice: Influence on the coding of simple motor sequences

The main purpose of the present experiment was to determine the coordinate system used in the development of movement codes when observational and physical practice are scheduled across practice sessions. The task was to reproduce a 1,300-ms spatial-temporal pattern of elbow flexions and extensions. An intermanual transfer paradigm with a retention test and two effector (contralateral limb) transfer tests was used. The mirror effector transfer test required the same pattern of homologous muscle activation and sequence of limb joint angles as that performed or observed during practice, and the non-mirror effector transfer test required the same spatial pattern movements as that performed or observed. The test results following the first acquisition session replicated the findings of Gruetzmacher, Panzer, Blandin, and Shea (2011) . The results following the second acquisition session indicated a strong advantage for participants who received physical practice in both practice sessions or received observational practice followed by physical practice. This advantage was found on both the retention and the mirror transfer tests compared to the non-mirror transfer test. These results demonstrate that codes based in motor coordinates can be developed relatively quickly and effectively for a simple spatial-temporal movement sequence when participants are provided with physical practice or observation followed by physical practice, but physical practice followed by observational practice or observational practice alone limits the development of codes based in motor coordinates.

Three-dimensional kinematic analysis of upper and lower limb motion during gait of post-stroke patients

The aim of this study was to analyze the alterations of arm and leg movements of patients during stroke gait. Joint angles of upper and lower limbs and spatiotemporal variables were evaluated in two groups: hemiparetic group (HG, 14 hemiparetic men, 53 ± 10 years) and control group (CG, 7 able-bodied men, 50 ± 4 years). The statistical analysis was based on the following comparisons (P ≤ 0.05): 1) right versus left sides of CG; 2) affected (AF) versus unaffected (UF) sides of HG; 3) CG versus both the affected and unaffected sides of HG, and 4) an intracycle comparison of the kinematic continuous angular variables between HG and CG. This study showed that the affected upper limb motion in stroke gait was characterized by a decreased range of motion of the glenohumeral (HG: 6.3 ± 4.5, CG: 20.1 ± 8.2) and elbow joints (AF: 8.4 ± 4.4, UF: 15.6 ± 7.6) on the sagittal plane and elbow joint flexion throughout the cycle (AF: 68.2 ± 0.4, CG: 46.8 ± 2.7). The glenohumeral joint presented a higher abduction angle (AF: 14.2 ± 1.6, CG: 11.5 ± 4.0) and a lower external rotation throughout the cycle (AF: 4.6 ± 1.2, CG: 22.0 ± 3.0). The lower limbs showed typical alterations of the stroke gait patterns. Thus, the changes in upper and lower limb motion of stroke gait were identified. The description of upper limb motion in stroke gait is new and complements gait analysis.

Changes in the Lower Limb Joint Angle during the Simulated Skiing

Changes in the Lower Limb Joint Angle during the Simulated Skiing