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Gait asymmetries are a common problem for clinical populations—such as stroke survivors and people with Parkinson’s disease—and are associated with increased gait instability and fall risk. Current methods to alter gait asymmetries rely heavily on split-belt treadmill training. Gait training using visual cues projected on a screen or in immersive virtual reality have been shown to produce greater improvements in gait asymmetries relative to traditional treadmill training alone. However, it is unclear the extent to which gait asymmetries can be systematically altered using an asymmetric visual cue, which represents a more cost-effective strategy relative to split-belt treadmill training or immersive virtual reality. Investigating whether using a visual asymmetric cue can alter gait symmetry in healthy adults is the first step in determining if this methodology is plausible for future research with clinical populations. The purpose of this dissertation was threefold: (1) to examine the extent to which healthy adults can synchronize to an asymmetric visual cue during treadmill walking; (2) to explore if the asymmetric walking pattern is retained once the visual cue is removed; and (3) to examine transfer of the asymmetric walking pattern to overground walking after the treadmill training session. Seventy-two healthy participants (age 23.89 ± 6.08 years) were enrolled in this study and quasi-randomized into four experimental groups (N = 64) or the control group (N = 8). All participants completed questionnaires related to health history/demographics, limb dominance, and physical activity. All groups completed three 10-minute walking sessions with wearable sensors (APDM Inc., Portland, OR) to record spatiotemporal gait measures. The first session was the same for all groups and consisted of walking at their self-selected speed on the treadmill. For session two, experimental groups 1 and 2 attempted to synchronize their gait to a visual cue (i.e., walking stick figure) ex

Falling poses a significant risk of injury for older adults, thus decreasing quality of life. Major risk factors for falling include decrements in gait and balance, and adverse patient-reported health and well-being. Virtual Reality (VR) can be a cost-effective, resource-efficient, and highly engaging training tool, and previous research has utilized VR to reduce fall-risk factors in a variety of populations with aging and pathology. However, there are barriers to implementing VR as a training tool to improve functional mobility in older adults that include the manner in which healthy older adults perform in VR relative to younger adults, the effect of extended duration training, and the relation of fall-risk clinical metrics to performance in VR. The purpose of this dissertation is threefold: (1) to compare performance between older and younger adults in VR and in real-world gait and balance tests as a result of a single bout of VR training; (2) to compare performance in VR and gait and balance within younger adults as a result of extended training duration; and (3) to evaluate clinical tests as prerequisite measures for performance within the VR environment. Thirty-five healthy adults participated in this study and were placed into either the older adult training group (n=8; 67.0±4.4yrs), younger training (n=13; 22.1±2.5yrs), or younger control (n=13; 21.7±1.0yrs). All participants completed an online patient-reported survey of balance confidence and health and well-being, as well as a pre-test of clinical assessments and walking and balance tests. The training groups then completed 15 trials of a VR obstacle course, while the controls walked overground for 15 minutes. The VR obstacle course included a series of gait and dynamic balance tasks, such as stepping on irregularly placed virtual stepping stones and walking a virtual balance beam. All participants repeated the walking and balance tests at post-test. The younger training group also completed 3 weeks of tr

There is a lack of consensus regarding when mature or adult-like gait is achieved in youth. The ability to adjust gait during the walk-to-run transition (WRT) may be a good indicator of whether gait has matured. Specifically, age-related differences in the preferred transition speed (PTS) and determinants of WRT can provide insight into self-organising behaviours and how effectively gait patterns are regulated in youth. This thesis therefore assessed WRT in 49 youth (10-17-year-olds) and 13 young adults (19-29-year-olds) to: 1) investigate how effectively youth can adjust to increasing gait speed; and 2) explore age-related differences in determinants of PTS. Participants completed a WRT treadmill protocol that started at a self-selected walking speed and increased by 0.06 m∙s⁻¹ every 30 s to determine PTS. Participants also walked and ran on a treadmill at speeds near PTS (PTS, PTS±0.14 m·s⁻¹, PTS±0.28 m·s⁻¹). During these tests, muscle activity (rectus femoris, biceps femoris, tibialis anterior, medial gastrocnemius), oxygen consumption, heart rate and perceived exertion were assessed for their role in determining PTS. There were no age-related differences in PTS despite there being anthropometric differences. However, 10-12-year-olds exhibited more exploratory behaviour when determining PTS, while adults and 15-17-year-olds generally used a single transition to determine PTS. Age-related differences in PTS determinants were observed. Specifically, the biceps femoris and medial gastrocnemius were additional weak links among 10-12-year-olds and 10-17-year-olds, respectively, suggesting these muscles continue developing through childhood and adolescence. Because youth transition to minimise the demands of more muscles than adults, they may have more conflicting sources of feedback arising from the musculature when adjusting their gait. The 10-14-year-olds also exhibited greater difficulties distinguishing differences in perceived exertion between walking and running

Gait is a process of a complete cycle of walking that consist of two-step cycles. It can be said that gait has a high degree of biometric which means that every person has its own unique style of walking. Gait recognition using smartphone accelerometer has been widely used in many research and applications due to the cheap assembly, durability and reliability of the Inertial Measurement Unit (IMU) Microelectromechanical System (MEMS) technology. Gait recognition has been used in many areas such as biomechanics, neuro-rehabilitation, sports medicine, security and many others. Latest research achievement in gait recognition approach is the ability to sufficiently recognize a person with small variations and single data enrolment. In the standard gait recognition, there are four main workflows or levels that include data acquisition, pre-processing, features extraction and classification. However, most of the current research is concentrated on the data acquisition and features extractions with a minimal concentration on other workflows, hence the best accuracy is not fully achieved and optimized. In this thesis, we found several problems at the data acquisition stage, pre-processing stage, and classification stage. At the data acquisition stage, gait data is obtained from predefined places such as pocket, pouch, trousers and other parts of the body. However, due to the limitation of the clothes and culture, the mentioned places may not be suitable for smartphone placement. At the pre-processing stage, linear interpolation is widely used by researchers in order to create a fix sampling rate between data points. However, they never examine the best interpolation rate for usage as the rate affects the number of data and this would significantly affect the overall accuracy. At the classification stage, there are two problems that were observed. The first problem is the single classifier mapping applied by the current researchers which are not suitable because the gait rec

Gait is a process of a complete cycle of walking that consist of two-step cycles. It can be said that gait has a high degree of biometric which means that every person has its own unique style of walking. Gait recognition using smartphone accelerometer has been widely used in many research and applications due to the cheap assembly, durability and reliability of the Inertial Measurement Unit (IMU) Microelectromechanical System (MEMS) technology. Gait recognition has been used in many areas such as biomechanics, neuro-rehabilitation, sports medicine, security and many others. Latest research achievement in gait recognition approach is the ability to sufficiently recognize a person with small variations and single data enrolment. In the standard gait recognition, there are four main workflows or levels that include data acquisition, pre-processing, features extraction and classification. However, most of the current research is concentrated on the data acquisition and features extractions with a minimal concentration on other workflows, hence the best accuracy is not fully achieved and optimized. In this thesis, we found several problems at the data acquisition stage, pre-processing stage, and classification stage. At the data acquisition stage, gait data is obtained from predefined places such as pocket, pouch, trousers and other parts of the body. However, due to the limitation of the clothes and culture, the mentioned places may not be suitable for smartphone placement. At the pre-processing stage, linear interpolation is widely used by researchers in order to create a fix sampling rate between data points. However, they never examine the best interpolation rate for usage as the rate affects the number of data and this would significantly affect the overall accuracy. At the classification stage, there are two problems that were observed. The first problem is the single classifier mapping applied by the current researchers which are not suitable because the gait rec

With the development of modern computer based systems, gait analysis has been an active research topic in recent years and gait tracking system is widely applied into physical therapy and rehabilitation such as stroke rehabilitation and elderly clinical trials. The demand for gait digitization stimulates the improvement of gait tracking system. However, for this specific system, accuracy remains a problem although the IMU based tracking system precedes the optical gait tracking system for its simple use and relatively independence of the environmental influence. Moreover, large hospitals and rehabilitation centers suffer high expenses on the tracking system. In this thesis, we focus on the proposed “9-Axis gait tracking system”, which is made up of two modules, a popular commercial 9-Axis sensor and a Wi-Fi enabled MCU, with total cost around 100 HKD. It records the gait data with finely calibrated IMU, and draws the support from the state-of-the-art orientation filtering algorithm with experimental optimal control settings plus merged gravity and geomagnetic information, as well as the linear cancellation procedure for velocity drift to reconstruct accurate and stable gait trajectory for analysis. We evaluate performance of 9-Axis gait tracking system by comparing it with 6- Axis gait tracking system implemented in both outdoor and indoor environment with single step test, straight-line walking test in short and long distance, square path test, triangle path test, zigzag path test, slight and strong magnetic interference environment, in most of which show overwhelming advantage of 9-Axis data’s trajectory. As for commercial use, the proposed IMU based “9-Axis gait tracking system” keeps small in size and simple in operation. The single node system is ready for daily use in sports and fitness while the full tracking system is able to bring professional rehabilitation analysis report to people in special need with lower cost. Key words: gait tracking, 9-Axis sensor

With the development of modern computer based systems, gait analysis has been an active research topic in recent years and gait tracking system is widely applied into physical therapy and rehabilitation such as stroke rehabilitation and elderly clinical trials. The demand for gait digitization stimulates the improvement of gait tracking system. However, for this specific system, accuracy remains a problem although the IMU based tracking system precedes the optical gait tracking system for its simple use and relatively independence of the environmental influence. Moreover, large hospitals and rehabilitation centers suffer high expenses on the tracking system. In this thesis, we focus on the proposed “9-Axis gait tracking system”, which is made up of two modules, a popular commercial 9-Axis sensor and a Wi-Fi enabled MCU, with total cost around 100 HKD. It records the gait data with finely calibrated IMU, and draws the support from the state-of-the-art orientation filtering algorithm with experimental optimal control settings plus merged gravity and geomagnetic information, as well as the linear cancellation procedure for velocity drift to reconstruct accurate and stable gait trajectory for analysis. We evaluate performance of 9-Axis gait tracking system by comparing it with 6- Axis gait tracking system implemented in both outdoor and indoor environment with single step test, straight-line walking test in short and long distance, square path test, triangle path test, zigzag path test, slight and strong magnetic interference environment, in most of which show overwhelming advantage of 9-Axis data’s trajectory. As for commercial use, the proposed IMU based “9-Axis gait tracking system” keeps small in size and simple in operation. The single node system is ready for daily use in sports and fitness while the full tracking system is able to bring professional rehabilitation analysis report to people in special need with lower cost. Key words: gait tracking, 9-Axis sensor

With the development of modern computer based systems, gait analysis has been an active research topic in recent years and gait tracking system is widely applied into physical therapy and rehabilitation such as stroke rehabilitation and elderly clinical trials. The demand for gait digitization stimulates the improvement of gait tracking system. However, for this specific system, accuracy remains a problem although the IMU based tracking system precedes the optical gait tracking system for its simple use and relatively independence of the environmental influence. Moreover, large hospitals and rehabilitation centers suffer high expenses on the tracking system. In this thesis, we focus on the proposed “9-Axis gait tracking system”, which is made up of two modules, a popular commercial 9-Axis sensor and a Wi-Fi enabled MCU, with total cost around 100 HKD. It records the gait data with finely calibrated IMU, and draws the support from the state-of-the-art orientation filtering algorithm with experimental optimal control settings plus merged gravity and geomagnetic information, as well as the linear cancellation procedure for velocity drift to reconstruct accurate and stable gait trajectory for analysis. We evaluate performance of 9-Axis gait tracking system by comparing it with 6- Axis gait tracking system implemented in both outdoor and indoor environment with single step test, straight-line walking test in short and long distance, square path test, triangle path test, zigzag path test, slight and strong magnetic interference environment, in most of which show overwhelming advantage of 9-Axis data’s trajectory. As for commercial use, the proposed IMU based “9-Axis gait tracking system” keeps small in size and simple in operation. The single node system is ready for daily use in sports and fitness while the full tracking system is able to bring professional rehabilitation analysis report to people in special need with lower cost. Key words: gait tracking, 9-Axis sensor

One of the most common neurological injuries in the elderly is a stroke event, affecting nearly 800,000 adults in the U.S. alone every year. Since falls occur at a rate of 73% per year with people who are more than six months past the stroke event compared to approximately 30% with aged matched healthy, the potential consequences for injury are devastating. Current literature does not completely address the specific deficits in gait and balance after a stroke. To resolve this problem, the purpose of this thesis was to compare gait mechanics to clinical tests that indicate fall risks in 20 healthy elderly adults (63.4±8.9) and 7 non-cerebellar/non-brain stem post-stroke survivors (57.6±7.7). The dependent variables for gait were step width, step length, stride length, step time and stride time. The metrics of mean, standard deviation (SD), coefficient of variation (CoV), detrended fluctuation analysis alpha (DFA α) and sample entropy (SampEn) were calculated for each dependent variable. Further, the Timed Up and Go (TUG), Berg balance assessment (Berg), Functional Gait Assessment (FGA), Activities-Specific Balance Confidence Scale (ABC), lower extremity strength, and lower extremity flexibility were all taken as clinical assessments. The data showed that most dependent variables for mean, SD, and CoV were different between groups, whereas DFA α and SampEn generally were not. The TUG, Berg, FGA, and ABC showed group differences. No differences in strength or flexibility were observed between the unaffected limbs of the stroke survivor group and matched limbs of the healthy elderly group. However, significant differences were observed in strength and flexibility between the affected and matched limbs between groups. Sixty-four out of a possible 200 correlations between the gait and clinical metrics were significant. These data suggest that summary metrics (mean, SD, and CoV) may be the strongest indicators of gait dysfunction after a stroke.

A Master of Science thesis in Electrical Engineering by Amer Al -Tayyan entitled, "Decision-level Gait Fusion for Human Identification at a Distance," submitted in June 2014. Thesis advisors are Dr. Khaled Assaleh and Dr. Tamer Shanableh. Available are both soft and hard copies of the thesis., Gait Recognition is one of the latest and most attractive biometric techniques currently under research, due to its potential application in identification of individuals at a distance, unobtrusively and even using low resolution images. In this thesis a comprehensive study of the gait problem is presented, covering gait databases and the different approaches used for preprocessing, feature extraction and classification. The objective is to achieve a robust technique that performs well, independently of the input data and the many covariates that affect this behavioral biometric technique. Firstly, gait data is processed using three gait representation methods as the features sources; Accumulated Prediction Image (API) and two novel gait representations namely; Accumulated Flow Image (AFI) and Edge-Masked Active Energy Image (EMAEI). Secondly, each of these methods is tested using three matching schemes; Image Projection with Linear Discriminant Functions (LDF), Multilinear Principal Component Analysis (MPCA) with K Nearest Neighbor (KNN) classifier and the third method: MPCA+ Linear Discriminant Analysis (MPCALDA) with KNN classifier. Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the above-mentioned gait representations. We endup having nine recognition modules which are analyzed individually using four different experimental setups and compared to the results reported in six of the most recent papers that used the same database and the same experimental setups. Finally, decisions from the nine recognizers are fused using decision-level (majority voting) scheme. A comparison between unweighted and weighted voting schemes for final decision is also shown. The experimental results show clearly that the proposed approach outperforms the state-of-the-art gait approaches used in the literature, and reports the highest recognition rates known to the date of writing this report. As a result of the comprehensive study and exte, College of Engineering, Department of Electrical Engineering

The purpose of this study was to establish how performance in cognitive (i.e., working memory) and motor (i.e., gait) tasks vary when the tasks are performed in isolation and concurrently. Sixteen collegiate students, three males and 13 females, comprised the sample. The hypothesis was gait performance would decline when performed with a working memory task (dual-task condition) compared to when gait performance was performed alone (single task condition). I expected that an increase in working memory requirements during the dual tasks would lead to a corresponding decrement in gait performance by shifting gait toward maladaptive behavior. The results did not support my hypothesis. A MANOVA was used to determine if task condition influenced performance in any of the gait performance metrics (stride interval standard mean, standard deviation, or DFA α) or cognitive performance metrics (absolute score, partial score, speed error, accuracy error, or math error). No differences were observed in any of the gait or cognitive metrics as a function of the task condition. It is possible that differences were not found in gait performance metrics because the cognitive task and gait task were not drawing from the same resources. It is also conceivable that the cognitive task used in this study did not significantly tax memory to cause a decrement in gait performance.

Purpose: Barefoot running (BF) is popular in the running community. Biomechanical changes occur with BF, especially when initial contact changes from rearfoot strike (RFS) to forefoot strike (FFS). In addition, changes in lumbar spine range of motion (ROM), particularly involving lumbar lordosis, have been associated with increased low back pain (LBP). However it is not known how changing from RFS to FFS affects lumbar lordosis or LBP. Thus, the purpose of this study was to determine if a change from RFS to FFS would change lumbar lordosis, and/or decrease shock attenuation, and/or change comfort levels in healthy recreational/experienced runners. Methods: Forty-three subjects performed a warm up on the treadmill where a self-selected footstrike pattern was determined. Instructions on running RFS/FFS were taught and two conditions were examined. Each condition consisted of 90 s of BF with RFS or FFS; order randomly assigned. A comfort questionnaire was completed after both conditions. Fifteen consecutive strides from each condition were extracted for analyses. Results: Statistically significant differences between FFS and RFS shock attenuation (p Conclusion: Change in footstrike from RFS to FFS decreased overall ROM in the lumbar spine but did not make a difference in flexion or extension in which the lumbar spine is positioned. Shock attenuation was greater in RFS. RFS was perceived a more comfortable running pattern.

This paper describes a method for identifying a person while walking by means of a triaxial accelerometer attached to the waist. Human gait is considered as a dynamical system whose attractor is reconstructed by time delay vectors. A Spectral Analysis on the state space reconstruction is used to characterize the attractor. The method is compared to other common methods used in gait recognition tasks through a preliminary test., Peer Reviewed, Postprint (published version)

This paper describes a method for identifying a person while walking by means of a triaxial accelerometer attached to the waist. Human gait is considered as a dynamical system whose attractor is reconstructed by time delay vectors. A Spectral Analysis on the state space reconstruction is used to characterize the attractor. Parameters involved in the reconstruction and characterization process are evaluated to examine the effect in gait identification. The method is tested in five volunteers, obtaining an overall accuracy of 92%., Peer Reviewed, Postprint (author’s final draft)

This paper evaluates a set of computational algorithms for the automatic estimation of human postures and gait properties from signals provided by an inertial body sensor. The use of a single sensor device imposes limitations for the automatic estimation of relevant properties, like step length and gait velocity, as well as for the detection of standard postures like sitting or standing. Moreover, the exact location and orientation of the sensor is also a common restriction that is relaxed in this study. Based on accelerations provided by a sensor, known as the `9 2', three approaches are presented extracting kinematic information from the user motion and posture. Firstly, a two-phases procedure implementing feature extraction and Support Vector Machine based classi cation for daily living activity monitoring is presented. Secondly, Support Vector Regression is applied on heuristically extracted features for the automatic computation of spatiotemporal properties during gait. Finally, sensor information is interpreted as an observation of a particular trajectory of the human gait dynamical system, from which a reconstruction space is obtained, and then transformed using standard principal components analysis, nally Support Vector Regression is used for prediction. Daily living Activities are detected and spatiotemporal parameters of human gait are estimated using methods sharing a common structure based on feature extraction and kernel methods. The approaches presented are susceptible to be used for medical purposes., Peer Reviewed, Preprint

[Truncated abstract] Knee osteoarthritis (OA) patients have high levels of pain, functional and strength deficits of the quadriceps, decreased proprioceptive acuity, and increased co-contraction and knee joint loading in gait, compared to age matched controls. The increased knee joint loading in this population occurs most commonly in the medial tibio-femoral compartment, due to increased external adduction moments, and with increasing disease severity there is a concomitant increase in the knee adduction moments. A key finding within the knee OA literature is that dynamic loading in gait, due to increased external adduction moments, strongly predicts pain and radiographic disease progression. Current research has shown that exercise interventions reduce pain and time to complete functional activities; however, the effect of these interventions on knee joint loading and muscular activation in gait is still unclear. In addition, the need for specific knee joint strengthening to cause these alterations has not been investigated and it remains unknown if improvements occur due to specific muscle strengthening or due to some general effect of exercise. Therefore, the primary aim of this research study was to examine the effects of general (upper body) and specific (lower body) resistance training interventions on self-perceived outcomes, neuromuscular function and kinematic, kinetic and muscle activation during gait of OA patients compared with asymptomatic controls. ... The examination of gait data following exercise (Study 4) showed trends for changes in the muscle co-contraction ratios. Specifically, the medial/lateral co-contraction ratio (MLCCR) displayed a trend in early stance where the upper body exercise group increased their lateral muscle activity and the lower body group reduced their lateral muscle activity, and the medial/lateral hamstring co-contraction ratio (HAMCCR) displayed this same trend during loading. The trend toward reduced lateral muscle activa, Thesis (Ph.D.)--University of Western Australia, 2009

It is thought that abnormalities in knee loads during walking are instrumental in the development and degradation of the joint tissues. These loads are extremely difficult to measure in-vivo and thus typically have been inferred from the external loads acting on the joint and muscle activity data (EMG) which are both relatively easy to measure. Large external adduction moments have been implicated in the development and progression of OA, knee joint pain, and worse outcomes after knee surgery such as high tibial osteotomy and it is thought this is due to these increasing the load sustained by the medial compartment of the knee. However, any estimates of joint loading should also include the contribution of the muscles as these have the capacity to stabilise the knee against large external loads, but may increase the joint contact loads as a result. Muscle activation patterns are known to be different in pathological gait, but it is not known whether these correspond to increased joint loads, as EMG does not necessarily represent muscle force. EMG-driven musculoskeletal modelling is a tool which facilitates the estimation of muscle force from EMG and this thesis is concerned with the application of such a model to gait to examine the contribution of the muscles to joint stabilisation and the subsequent joint contact forces. In order to apply the EMG-driven neuromuscular skeletal model to different subjects, an analytical scaling technique was developed to facilitate adjustment of selected muscle properties according to anthropometric dimensions. It adjusts these properties such that the force generating range of each muscle is preserved across subjects. It is suggested that this provides a good rationale for scaling muscle properties, and it is shown that the developed algorithm best achieves this compared to some other scaling techniques which have been presented previously., Thesis (Ph.D.)--University of Western Australia, 2009

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982., MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING, Bibliography: leaf 88., by Cheryl A. Gira., B.S.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982., MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING, Includes bibliographical references., by Kevin Paul Tyra., M.S.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982., MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING, Bibliography: leaf 88., by Cheryl A. Gira., B.S.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982., MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING, Bibliography: leaf 88., by Cheryl A. Gira., B.S.

Judging time-to-contact with a target is an important criterion for avoiding harm in everyday walking and running tasks, and maximizing performance in high-velocity sporting tasks. The information-based regulation of step length and duration during target-directed locomotion was examined in relation to gait mode, approach velocity, target task, expertise, and sporting performance during a series of four experiments. The first three experiments examined novice performers (Each n=12, 6 males, 6 females), whilst the last experiment examined expert gymnasts (n=5). Two reference strips with alternating 50cm black and white intervals were placed on either side of the approach strip for all of the experiments. One 50Hz-panning video camera filmed the approach from an elevated position. In Experiment 4, two stationary 250Hz cameras filmed the post-flight performance of the gymnastic vaults and, in addition, two qualified judges provided a performance score for each vaulting trial. The panning video footage in each experiment was digitized to deduce the gait characteristics. In Experiment 4, the high-speed video footage was analyzed three-dimensionally to obtain the performance measures such as post-flight height. The utilization of visual stimulus in target-directed locomotion is affected by the observer's state of motion as characterized by the mode of locomotion and also often the speed of locomotion. In addition, experience plays an important role in the capacity of the observer to utilize visual stimulus to control the muscular action of locomotion when either maintaining or adjusting the step mechanics. The characteristics of the terrain and the target also affect the observer's movement. Visual regulation of step length decreases at higher approach speeds in novice performers, where as expert performers are capable of increasing visual regulation at higher approach speeds. Conservatism in final foot placement by female participants accounts for the observed increase i

The purpose of this study was to assess the collective influence of selected metabolic, gait, and morphological variables on age-related differences in the submaximal aerobic demand (VO2) of walking. Four age groups of seven females (ages 6,10,13 and 18 or 19) comprised the sample. Measurements of walking VO2, ventilatory equivalent, and step frequency at 1.56 m sec-1 and 0% grade, were obtained at 10-min intervals during three treadmill testing sessions totaling 70 minutes. Sitting metabolic rate was assessed over a 10-min time period following 10 min of seated rest. Limb morphology (thigh to shank ratio) was determined from measurements of limb volume using a water fill plethysmograph. Average vertical ground reaction forces and ground contact time were measured as participants walked across a force platform at 1.56 m-sec-1. Body surface area to mass ratio was predicted from knowledge of body height and mass. Analysis of variance (ANOVA) and Tukey post-hoc multiple comparison procedures were used to assess whether mean values of the dependent (walking VO2) and independent variables (ventilatory equivalent, step frequency, resting metabolic rate, limb morphology, average vertical ground reaction force, ground contact time and body surface area to mass ratio) were significantly different across age groups.