Joint Angles Of Lower Body Research Articles

A Motion Capture Dataset on Human Sitting to Walking Transitions

Sit-to-walk (STW) is a crucial daily task that impacts mobility, independence, and thus quality of life. Existing repositories have limited STW data with small sample sizes (n = 10). Hence, this study presents a STW dataset obtained via the time-up-and-go test, for 65 healthy adults across three age groups – young (19–35 years), middle (36–55 years) and older (above 56 years). The dataset contains lower body motion capture, ground reaction force, surface electromyography, inertial measurement unit data, and responses for the knee injury and osteoarthritis outcome score survey. For validation, the within subjects intraclass correlation coefficients for the maximum and minimum lower body joint angles were calculated with values greater than 0.74, indicating good test-retest reliability. The joint angle trajectories and maximum voluntary contractions are comparable with existing literature, matching in overall trends and range. Accordingly, this dataset allows STW biomechanics, executions, and characteristics to be studied across age groups. Biomechanical trajectories of healthy adults serve as a benchmark in assessing neuromusculoskeletal impairments and when designing assistive technology for treatment or rehabilitation.

Effects of running-induced fatigue on joint kinematics and kinetics during overground running: a systematic review and meta-analysis.

This study evaluated the acute fatigue-inducing effect of distance running on kinematics and kinetics during overground running. Standardised mean differences (SMD) with 95% confidence intervals (95% CI) were used to pool data across 16 studies. Effects during consistent (pre- and post-fatigue running speed within ± 5%) versus varied speed running (difference of >5% between running speeds) were analysed separately. There was strong evidence that running-induced fatigue significantly increases ground contact times at consistent running speeds (SMD 0.52 [95% CI 0.22, 0.82]) and moderate evidence that step length shortens at varied running speeds (SMD -1.27 [95% CI -1.79, -0.75]). There was strong evidence that fatigue does not change peak: hip and knee flexion angles, hip adduction angle, hip and knee internal rotation angles, hip and knee extension moments, hip and knee abduction moments, knee abduction angle, knee flexion and extension moments, knee adduction moment, rearfoot eversion angle, and plantarflexion moments, or knee flexion and plantarflexion range of motion during stance. Running-induced fatigue increases contact times and reduces step length, whereas lower-body joint angles and moments are unchanged. Minimising changes in stride parameters could provide a mechanism for reducing the effects of fatigue on running performance.

Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis

ObjectiveTo develop and validate a neural network to estimate hip contact forces (HCF), and lower body kinematics and kinetics during walking in individuals with hip osteoarthritis (OA) using synthesised anatomical key points and electromyography. To assess the capability of the neural network to detect directional changes in HCF resulting from prescribed gait modifications. DesignA calibrated electromyography-informed neuromusculoskeletal model was used to compute lower body joint angles, moments, and HCF for 17 participants with mild-to-moderate hip OA. Anatomical key points (e.g., joint centres) were synthesised from marker trajectories and augmented with bias and noise expected from computer vision-based pose estimation systems. Temporal convolutional and long short-term memory neural networks were trained using leave-one-subject-out validation to predict neuromusculoskeletal modelling outputs from the synthesised key points and measured electromyography data from 5 hip-spanning muscles. ResultsHCF was predicted with an average error of 13.4±7.1% of peak force. Joint angles and moments were predicted with an average root-mean-square-error of 5.3 degrees and 0.10 Nm/kg, respectively. The NN could detect changes in peak HCF that occur due to gait modifications with good agreement with neuromusculoskeletal modelling (r2 = 0.72) and a minimum detectable change of 9.5%. ConclusionThe developed neural network predicted HCF and lower body joint angles and moments in individuals with hip OA using noisy synthesised key point locations with acceptable errors. Changes in HCF magnitude due to gait modifications were predicted with high accuracy. These findings have important implications for implementation of load-modification based gait retraining interventions for people with hip OA in a natural environment (i.e., home, clinic).

Machine learning full 3-D lower-body kinematics and kinetics on patients with osteoarthritis from electromyography

Osteoarthritis (OA) is a degenerative disease that causes severe pain and reduces the range of motion of the joint, decreasing the quality of life for millions of individuals in the United States. Electromyography (EMG) sensors have been widely studied in biomechanics, showing applications in prosthetics, robotics, and control. While complex musculoskeletal models have been well established, the attempt of directly correlating EMG with kinematics and kinetics is still quite limited. Particularly, little work has been conducted on OA patients. In this work, we propose a method for estimating lower body joint angles (JAs) and ground reaction forces (GRFs) from surface-EMG sensors during a step-down task for individuals diagnosed with OA. The JAs and GRFs were measured by a Vicon motion capture system and force plates, respectively. EMG, JAs, and GRFs were used to train an echo state network (ESN) which afforded JAs with relative errors of 3.78% and 3.71% and the GRFs with relative errors of 3.619% and 4.596% for training and testing datasets, respectively. This study suggests the high fidelity of the ESN in automatically predicting full lower body kinematics and kinetics from the EMG signals. The results of this work promote the development of an EMG-controlled lower limb rehabilitation robot system for patients with OA.

Lower Body Joint Angle Prediction Using Machine Learning and Applied Biomechanical Inverse Dynamics.

Extreme angles in lower body joints may adversely increase the risk of injury to joints. These injuries are common in the workplace and cause persistent pain and significant financial losses to people and companies. The purpose of this study was to predict lower body joint angles from the ankle to the lumbosacral joint (L5S1) by measuring plantar pressures in shoes. Joint angle prediction was aided by a designed footwear sensor consisting of six force-sensing resistors (FSR) and a microcontroller fitted with Bluetooth LE sensors. An Xsens motion capture system was utilized as a ground truth validation measuring 3D joint angles. Thirty-seven human subjects were tested squatting in an IRB-approved study. The Gaussian Process Regression (GPR) linear regression algorithm was used to create a progressive model that predicted the angles of ankle, knee, hip, and L5S1. The footwear sensor showed a promising root mean square error (RMSE) for each joint. The L5S1 angle was predicted to be RMSE of 0.21° for the X-axis and 0.22° for the Y-axis, respectively. This result confirmed that the proposed plantar sensor system had the capability to predict and monitor lower body joint angles for potential injury prevention and training of occupational workers.

Improved 3D Human Motion Capture Using Kinect Skeleton and Depth Sensor

Kinect has been utilized as a cost-effective, easy-to-use motion capture sensor using the Kinect skeleton algorithm. However, a limited number of landmarks and inaccuracies in tracking the landmarks’ positions restrict Kinect’s capability. In order to increase the accuracy of motion capturing using Kinect, joint use of the Kinect skeleton algorithm and Kinect-based marker tracking was applied to track the 3D coordinates of multiple landmarks on human. The motion’s kinematic parameters were calculated using the landmarks’ positions by applying the joint constraints and inverse kinematics techniques. The accuracy of the proposed method and OptiTrack (NaturalPoint, Inc., USA) was evaluated in capturing the joint angles of a humanoid (as ground truth) in a walking test. In order to evaluate the accuracy of the proposed method in capturing the kinematic parameters of a human, lower body joint angles of five healthy subjects were extracted using a Kinect, and the results were compared to Perception Neuron (Noitom Ltd., China) and OptiTrack data during ten gait trials. The absolute agreement and consistency between each optical system and the robot data in the robot test and between each motion capture system and OptiTrack data in the human gait test were determined using intraclass correlations coefficients (ICC3). The reproducibility between systems was evaluated using Lin’s concordance correlation coefficient (CCC). The correlation coefficients with 95% confidence intervals (95%CI) were interpreted substantial for both OptiTrack and proposed method (ICC > 0.75 and CCC > 0.95) in humanoid test. The results of the human gait experiments demonstrated the advantage of the proposed method (ICC > 0.75 and RMSE = 1.1460°) over the Kinect skeleton model (ICC < 0.4 and RMSE = 6.5843°).

Estimating Range of Lower Body Joint Angles with a Sensorized Overground Body-Weight Support System.

Recent trends in rehabilitation and therapy are turning to data-driven approaches to personalize treatment. Due to such approaches, data collection methods have become more complex and expensive, in terms of financial resources, technological knowledge, and time required to implement the data collection method. Such costs might deter clinical applications of otherwise good data collection methods. Hence, a method to collect data in a non-intrusive manner is proposed. Sensors are embedded into a commonly used rehabilitation tool, the walking trainer, for gait data collection. This study shows that, in principle, lower body joint angles can be collected in a non-intrusive manner, with a slight trade off to precision. In this study, the focus would be on the pelvic and hip movements, since the pelvic segment of the human body is implicated in a variety of gait problemsClinical relevance - The proposed usage model allows clinicians access to additional kinematic data, while minimizing changes to existing clinical evaluation processes and being non-intrusive. Having additional kinematic data would give further insight into a patient's current state, thereby improving the efficiency of individualized therapy.

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system.

Marker-based and Marker-less Motion Capturing Video Data: Person & Activity Identification Comparison Based on Machine Learning Approaches

Biomechanics is the study of physiological properties of data and the measurement of human behavior. In normal conditions, behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style, body movements in walking patterns, writing style and voice tunes. One cannot perform any change in these inputs that make results reliable and increase the accuracy. The aim of our study is to perform a comparative analysis between the marker-based motion capturing system (MBMCS) and the marker-less motion capturing system (MLMCS) using the lower body joint angles of human gait patterns. In both the MLMCS and MBMCS, we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity (walk and running). Using five state of the art machine learning algorithms, we obtained 44.6% and 64.3% accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm (two angles as features). In the second set of experiments, we used six machine learning algorithms to obtain 65.9% accuracy with the k-nearest neighbor (KNN) algorithm (two angles as features) and 74.6% accuracy with an ensemble algorithm. Also, by increasing features (6 angles), we obtained higher accuracy of 99.3% in MBMCS for person recognition and 98.1% accuracy in MBMCS for activity recognition using the KNN algorithm. MBMCS is computationally expensive and if we re-design the model of OpenPose with more body joint points and employ more features, MLMCS (low-cost system) can be an effective approach for video data analysis in a person identification and activity recognition process.

Fatigue-Related and Timescale-Dependent Changes in Individual Movement Patterns Identified Using Support Vector Machine.

The scientific and practical fields—especially high-performance sports—increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by identifying the unique movement patterns of individuals and distinguishing their intra-individual changes over time. The objective of this investigation is to analyze biomechanically described movement patterns during the fatigue-related accumulation process within a single training session of a high number of repeated executions of a ballistic sports movement—specifically, the frontal foot kick (mae-geri) in karate—in expert athletes. The two leading research questions presented for consideration are (1) Can characteristics of individual movement patterns be observed throughout the entire training session despite continuous changes, i.e., even as fatigue-related processes increase? and (2) How do intra-individual movement patterns change as fatigue-related processes increase throughout a training session? Sixteen expert karatekas performed 606 frontal foot kicks directed toward an imaginary target. The kicks were performed in nine sets at 80% (K-80) of the self-experienced maximal intensity. In addition, six kicks at maximal intensity (K-100) were performed after each of the nine sets. Between the sets, the participants took a 90-s break. Three-dimensional full-body kinematic data of all kicks were recorded with 10 infrared cameras. The normalized waveforms of nine upper- and lower-body joint angles were classified using a supervised machine learning method (support vector machine). The results of the classification revealed a disjunct distinction between the kinematic movement patterns of individual athletes. The identification of unique movement patterns of individual athletes was independent of the intensity and the degree of fatigue-related processes. In other words, even with the accumulation of fatigue-related processes, the unique movement patterns of an individual athlete can be clearly identified. During the training session, changes in intra-individual movement patterns could also be detected, indicating the occurrence of adaptations in individual movement patterns throughout the fatigue-related accumulation process. The results suggest that these adaptations can be modeled in terms of changes in patterns rather than increasing variance. Practical consequences are critically discussed.

Metabolic testing does not alter distance running lower body sagittal kinematics

BackgroundDistance running fitness is commonly assessed using metabolic testing (MT). During MT, the runner must wear a mask that covers the nose and mouth. It is unclear if this increased challenge alters running kinematics and/or stride-to-stride variability (SSV). In this study we thoroughly assess the sagittal plane lower body joint angles. Research questionAre there significant differences between standard treadmill running kinematics and those collected during MT? MethodsTwenty recreational runners participated (34.8 ± 10.0 years; 20+ miles per week). Six Vicon Bonita cameras were used to collect kinematic data (200 Hz). A metabolic cart (Parvo Medics TrueOne 2400) was used for heart rate (HR) collection and testing. Participants ran 4 × 4 min at preferred pace: 2 control runs (CON) and 2 MT runs. Ten strides were used to generate average stance and swing joint angle plots. The phase plots were compared for CON and MT and mean difference scores were calculated (to determine the kinematic change). SSV was determined by assessing the standard deviations among the 10 strides. Repeated measures ANOVA was used to test for significant differences among CON and MT trials. Reliability was assessed for 8 discrete joint angles using ICC analysis. ResultsThere were no significant differences between CON and MT for both the joint angle plot comparisons and SSV. For the discrete kinematic measures, ICC scores were strong (0.89-0.99) between CON and MT. During MT, there were slight increases (p < 0.01) in HR (145 ± 14 vs. 147 ± 14) and RPE (10.4 ± 1.5 vs. 11.4 ± 1.5). SignificanceResults from this study support the validity of simultaneously conducting a kinematic and MT analysis. However, clinicians and performance coaches should be aware that 1) MT is slightly more physiologically demanding than CON and 2) approaches from this study can be used during individual assessments to confirm that kinematics are similar (between CON and MT).

Predictors of Walking Efficiency in Children With Cerebral Palsy: Lower-Body Joint Angles, Moments, and Power.

People with cerebral palsy (CP) experience increased muscle stiffness, muscle weakness, and reduced joint range of motion. This can lead to an abnormal pattern of gait, which can increase the energy cost of walking and contribute to reduced participation in physical activity. The aim of the study was to examine associations between lower-body joint angles, moments, power, and walking efficiency in adolescents with CP. This was a cross-sectional study. Sixty-four adolescents aged 10 to 19 years with CP were recruited. Walking efficiency was measured as the net nondimensional oxygen cost (NNcost) during 6 minutes of overground walking at self-selected speed. Lower-body kinematics and kinetics during walking were collected with 3-dimensional motion analysis, synchronized with a treadmill with integrated force plates. The associations between the kinematics, kinetics, and NNcost were examined with multivariable linear regression. After adjusting for age, sex, and Gross Motor Function Classification System level, maximum knee extension angle (β=-0.006), hip angle at midstance (β=-0.007), and maximum hip extension (β=-0.008) were associated with NNcost. Age was a significant modifier of the association between the NNcost and a number of kinematic variables. This study examined kinetic and kinematic variables in the sagittal plane only. A high interindividual variation in gait pattern could have influenced the results. Reduced knee and hip joint extension are associated with gait inefficiency in adolescents with CP. Age is a significant factor influencing associations between ankle, knee, and hip joint kinematics and gait efficiency. Therapeutic interventions should investigate ways to increase knee and hip joint extension in adolescents with CP.

Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors.

Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ>0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE <5°. Ground reaction forces are estimated with a mean RMSE < 0.27 BW. Additionaly, peak vertical ground reaction force, loading rate and maximal knee flexion during stance were compared, however, no significant differences were found. With multiple subject training the accuracy of estimating discrete and continuous outcomes decreases, however, good agreement (ρ > 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects.

Preserved gait kinematics during controlled body unloading

BackgroundBody weight supported locomotor training was shown to improve walking function in neurological patients and is often performed on a treadmill. However, walking on a treadmill does not mimic natural walking for several reasons: absent self-initiation, less active retraction of leg required and altered afferent input. The superiority of overground training has been suggested in humans and was shown in rats demonstrating greater plasticity especially in descending pathways compared to treadmill training. We therefore developed a body weight support system allowing unrestricted overground walking with minimal interfering forces to train neurological patients. The present study investigated the influence of different amounts of body weight support on gait in healthy individuals.MethodsKinematic and electromyographic data of 19 healthy individuals were recorded during overground walking at different levels of body weight support (0, 10, 20, 30, 40, and 50%). Upper body inclination, lower body joint angles and multi-joint coordination as well as time-distance parameters were calculated. Continuous data were analyzed with regard to distinct changes within a gait cycle across all unloading conditions.ResultsTemporal gait parameters were most sensitive to changes in body unloading while spatial variables (step length, joint angles) showed modest responses when unloaded by as much as 50% body weight. The activation of the gastrocnemius muscle showed a gradual decrease with increasing unloading while the biceps femoris muscle showed increased activity levels at 50% unloading. These changes occurred during stance phase while swing phase activity remained unaltered.ConclusionsHealthy individuals were able to keep their walking kinematics strikingly constant even when unloaded by half of their body weight, suggesting that the weight support system permits a physiological gait pattern. However, maintaining a given walking speed using close-to-normal kinematics while being unloaded was achieved by adapting muscle activity patterns. Interestingly, the required propulsion to maintain speed was not achieved by means of increased gastrocnemius activity at push-off, but rather through elevated biceps femoris activity while retracting the leg during stance phase. It remains to be investigated to what extent neurological patients with gait disorders are able to adapt their gait pattern in response to body unloading.

Daily changes of individual gait patterns identified by means of support vector machines

Despite the common knowledge about the individual character of human gait patterns and about their non-repeatability, little is known about their stability, their interactions and their changes over time. Variations of gait patterns are typically described as random deviations around a stable mean curve derived from groups, which appear due to noise or experimental insufficiencies. The purpose of this study is to examine the nature of intrinsic inter-session variability in more detail by proving separable characteristics of gait patterns between individuals as well as within individuals in repeated measurement sessions. Eight healthy subjects performed 15 gait trials at a self-selected speed on eight days within two weeks. For each trial, the time-continuous ground reaction forces and lower body kinematics were quantified. A total of 960 gait patterns were analysed by means of support vector machines and the coefficient of multiple correlation. The results emphasise the remarkable amount of individual characteristics in human gait. Support vector machines results showed an error-free assignment of gait patterns to the corresponding individual. Thus, differences in gait patterns between individuals seem to be persistent over two weeks. Within the range of individual gait patterns, day specific characteristics could be distinguished by classification rates of 97.3% and 59.5% for the eight-day classification of lower body joint angles and ground reaction forces, respectively. Hence, gait patterns can be assumed not to be constant over time and rather exhibit discernible daily changes within previously stated good repeatability. Advantages for more individual and situational diagnoses or therapy are identified.

순환 신경망을 이용한 보행단계 분류기

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

Earliest Gait Deviations During Slips: Implications For Recovery

OCCUPATIONAL APPLICATIONS This study identified that deviations in vertical force and knee angle/angular velocity of the slipping leg occur earlier in stance and with greater magnitude than other lower-body motions when a person experiences an unexpected slip. Deviations in the ankle angle/angular velocity and hip angular velocity occurred soon after the knee angle and with smaller magnitudes. These results suggest that foot somatosensation and ankle/knee proprioception may play a role in sensing a slip. Therefore, workers with sensation loss in their foot, ankle, and/or knee may have an impaired ability to respond to slipping. Exposure to simulated slip perturbations may also be part of slip-and-fall prevention programs. To ensure biofidelity of such training perturbations, foot forces should initially be reduced, followed by extension deviations of the knee, and then plantarflexion deviations of the ankle. TECHNICAL ABSTRACT Rationale: Slip-and-fall accidents are a serious occupational and public health concern. The biomechanical deviations due to a slip occurring prior to the postural response onset are still not well understood. Understanding this period of the slip would provide insight into the sensory cues for slipping and may provide guidance in developing slip-training protocols. Purpose: This study examined the timing and magnitude of deviations in vertical force and lower-body joint angles and angular velocities of slips compared to unperturbed walking. Methods: Twenty-nine younger and 29 middle-aged participants walked under normal unperturbed conditions and during an unexpected slip. Joint angle and angular velocity trajectories and ground reaction forces were evaluated. Deviations occurring during the slipping trial that exceeded ±2.58 standard deviations (99% confidence interval) were identified as the onset of deviation from normal walking. Results: Deviation timing (and peak magnitude in the first 200 ms) of vertical force, knee angle, knee angular velocity, ankle angle, ankle angular velocity, and hip angular velocity of the slipping leg occurred at 58 ms (0.17 BW reduction), 116 ms (6.7° extension), 111 ms (87°/s extension), 156 ms (4.4° plantarflexion), 122 ms (86°/s plantarflexion), and 149 ms (18.9°/s flexion), respectively. Deviations normalized to baseline stride-to-stride standard deviation revealed largest deviations in vertical force and knee angle and then knee angular velocity and ankle angle and angular velocity. No age effects were found. Conclusions: These results suggest that foot somatosensation as well as ankle and knee proprioception from the slipping leg may be among the first sensory cues to slipping. Exposure to simulated slip perturbations may be part of slip-and-fall prevention programs. To ensure biofidelity of such perturbations, foot forces should initially be reduced, followed by extension deviations of the knee, and then plantarflexion deviations of the ankle.

노인의 보행보조기구 사용 보행시 보행패턴의 변화연구

Walking is not only an essential component of the human mobility, but also is a good exercise. Inability to walk freely can reduce an individual's quality of life and independence substantially. Being a relatively low impact activity, walking is particularly good for the elderly and research has shown that regular walking in the elderly reduces the chance of fall-related injuries and mental diseases as well. In spite of the documented benefits of regular walking, it is still difficult to walk without the aid of assistive devices for the frail elderly who have lower extremity problems. Assistive walking devices(AWD), such as crutches, canes, hiking-poles, T-Poles and walkers, are often prescribed to the elderly to make their walking be safe and efficient. Many researchers have demonstrated the effects of AWDs such as reducing lower extremity loading, improved dynamic/gait stability, yet, no study has been done for gait pattern when the elderly gait with AWDs. Therefore, the purpose of this study was to examine whether T-Poles, one of the AWDs, change the elderly gait pattern. Eight community-dwelling female elderly participated in this study. Laboratory kinematics during walking with T-Poles(PW) and with out T-Poles(NPW) was assessed. PW showed significant increase in step width, stride length, gait velocity and decrease in swing time. No significances were found in lower body joint angles but meaningful trend and pattern were found. Maybe the reason was due to the participants. Our participants were healthy enough so that the effect of T-Poles was minimum. PW also showed typical gait phases which are no single support phase during a gait cycle. It indicates that walking with T-Poles may guarantee safe and confident walking to the frail elderly.