Biology subjects

Stenum, J.

Publications and source records attributed to Stenum, J..

5 recordsLinked to original sources

bioRxiv2026

Energy Expenditure During Walking With a Novel Treadmill Controller That Induces Gait Asymmetry

Gait asymmetry is a common manifestation of walking impairment among clinical populations. We recently developed a novel treadmill walking approach called dynamic treadmill walking that can provide asymmetric gait training by changing the treadmill speed between fast and slow speeds within a single stride. Here, we studied the energy expenditure associated with a variety of dynamic treadmill walking conditions. We hypothesized that the metabolic power required for dynamic treadmill walking in all conditions would approximate the metabolic power associated with conventional walking at the mean of the fast and slow speeds employed in the task. Eleven young adults without gait impairment walked on an instrumented treadmill and breathed into a metabolic measurement system. During dynamic treadmill walking, the treadmill fluctuated between 0.75m/s and 1.50m/s, each for 50% of an individual's stride time. We used a metronome to synchronize participants' right heel-strikes with four different timing conditions. Net metabolic power during dynamic treadmill walking was significantly greater than normal walking at the mean speed of the task (1.125m/s) and generally lower than walking at the fast speed (1.5m/s). We did not observe any significant associations between net metabolic power and several measures of gait asymmetry during dynamic treadmill walking. These findings establish dynamic treadmill walking as a promising technique for improving gait symmetry in individuals who cannot tolerate fast treadmill walking, a common gait rehabilitation approach. Future work will assess the feasibility, metabolic demands, and clinical efficacy of using dynamic treadmill walking to improve gait symmetry in clinical populations.

physiology↗

bioRxiv2022

Simple within-stride changes in treadmill speed can drive selective changes in human gait symmetry

BackgroundMillions of people worldwide are affected by clinical conditions that result in gait asymmetry (e.g., stroke, cerebral palsy, lower limb amputation). There is a need for customizable rehabilitation approaches that can flexibly target different aspects of gait asymmetry with minimal need for specialized equipment. Here, we studied how simple within-stride changes in treadmill speed could drive selective, predictable changes in human gait symmetry. MethodsIn Experiment 1, ten healthy young adults walked on an instrumented treadmill with and without a closed-loop controller engaged. The controller changed the treadmill speed to 1.50 m/s or 0.75 m/s depending on whether the right or left leg generated propulsive (i.e., forward-directed) ground reaction forces, respectively. In Experiment 2, a separate group of ten healthy young adults walked on the treadmill with and without an open-loop controller engaged. This controller changed the treadmill speed to 1.50 m/s or 0.75 at a prescribed time interval. We used a metronome to guide the participants to step at a series of different time points relative to the controller-driven speed change. We collected kinematic and kinetic data in Experiments 1 and 2. ResultsIn Experiment 1, participants walked with asymmetric kinematics and ground reaction forces when the closed-loop controller was engaged. The leg that accelerated during propulsion (right leg) showed a smaller leading limb angle and a larger trailing limb angle than the leg that decelerated during propulsion (left leg). The right leg also generated smaller propulsive forces than the left leg. In Experiment 2, the patterns of asymmetry in spatiotemporal gait parameters, kinematics, and ground reaction forces depended on the timing of the speed change within the gait cycle. Step times, leading limb angles, and peak propulsion became asymmetric when the treadmill speed changed early in stance. When the treadmill speed changed later in stance, step lengths, step times, and propulsion impulses became asymmetric. ConclusionsSimple manipulations of treadmill speed can drive predictable, selective changes in human gait symmetry. Future work will explore this customizable technique as a potential approach for restoring gait symmetry in clinical populations.

bioengineering↗

bioRxiv2021

Video-based quantification of human movement frequency using pose estimation

Assessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1-4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearsons correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, low-cost, accessible approach to quantitative assessment of repetitive movements in clinical populations.

bioengineering↗

bioRxiv2020

Two-dimensional video-based analysis of human gait using pose estimation

Walking is the primary mode of human locomotion. Accordingly, people have been interested in studying human gait since at least the fourth century BC. Human gait analysis is now common in many fields of clinical and basic research, but gold standard approaches - e.g., three-dimensional motion capture, instrumented mats or footwear, and wearables - are often expensive, immobile, data-limited, and/or require specialized equipment or expertise for operation. Recent advances in video-based pose estimation have suggested exciting potential for analyzing human gait using only two-dimensional video inputs collected from readily accessible devices (e.g., smartphones, tablets). However, we currently lack: 1) data about the accuracy of video-based pose estimation approaches for human gait analysis relative to gold standard measurement techniques and 2) an available workflow for performing human gait analysis via video-based pose estimation. In this study, we compared a large set of spatiotemporal and sagittal kinematic gait parameters as measured by OpenPose (a freely available algorithm for video-based human pose estimation) and three-dimensional motion capture from trials where healthy adults walked overground. We found that OpenPose performed well in estimating many gait parameters (e.g., step time, step length, sagittal hip and knee angles) while some (e.g., double support time, sagittal ankle angles) were less accurate. We observed that mean values for individual participants - as are often of primary interest in clinical settings - were more accurate than individual step-by-step measurements. We also provide a workflow for users to perform their own gait analyses and offer suggestions and considerations for future approaches.

bioengineering↗

bioRxiv2020

People can learn new walking patterns without walking

Humans can learn many new walking patterns. People have learned to snowshoe up mountains, racewalk marathons, and march in precise synchrony. But what is required to learn a new walking pattern? Here, we demonstrate that people can learn new walking patterns without actually walking. Through a series of experiments, we observe that stepping with only one leg can facilitate learning of an entirely new walking pattern (i.e., split-belt treadmill walking). We find that the nervous system learns from the relative motion between the legs - whether or not both legs are moving - and can transfer this learning to novel gaits. We also show that locomotor learning requires active movement: observing another person adapt their gait did not result in significantly faster learning. These findings reveal that people can learn new walking patterns without bilateral gait training, as stepping with one leg can facilitate adaptive learning that transfers to novel gait patterns.

neuroscience↗