Comparison of Display Modality and Human-in-the-Loop Presence for On-Orbit Inspection of Spacecraft.

OBJECTIVE: To investigate the impact of interface display modalities and human-in-the-loop presence on the awareness, workload, performance, and user strategies of humans interacting with teleoperated robotic systems while conducting inspection tasks onboard spacecraft. BACKGROUND: Due to recent advancements in robotic technology, free-flying teleoperated robot inspectors are a viable alternative to extravehicular activity inspection operations. Teleoperation depends on the user's situation awareness; consequently, a key to successful operations is practical bi-directional communication between human and robot agents. METHOD: Participants (n = 19) performed telerobotic inspection of a virtual spacecraft during two degrees of temporal communication, a Synchronous Inspection task and an Asynchronous Inspection task. Participants executed the two tasks while using three distinct visual displays (2D, 3D, AR) and accompanying control systems. RESULTS: Anomaly detection performance was better during Synchronous Inspection than the Asynchronous Inspection of previously captured imagery. Users' detection accuracy reduced when given interactive exocentric 3D viewpoints to accompany the egocentric robot view. The results provide evidence that 3D projections, either demonstrated on a 2D interface or augmented reality hologram, do not affect the mean clearance violation time (local guidance performance), even though the subjects perceived a benefit. CONCLUSION: In the current implementation, the addition of augmented reality to a classical egocentric robot view for exterior inspection of spacecraft is unnecessary, as its margin of performance enhancement is limited in comparison. APPLICATION: Results are presented to inform future human-robot interfaces to support crew autonomy for deep space missions.

Body-Worn IMU-Based Human Hip and Knee Kinematics Estimation during Treadmill Walking.

Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated (N=12) in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87∘ absolute root mean square error (RMSE)), hip flexion/extension angle (3.70∘ relative RMSE), and hip abduction/adduction angle (4.56∘ relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27∘ relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking).

Impact of Haptic Cues and an Active Ankle Exoskeleton on Gait Characteristics.

OBJECTIVE: This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. BACKGROUND: An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. METHODS: Participants (N = 16) were provided combinations of torque assistance and vibrotactile cues at pre-specified time points in late swing and early stance while walking on a self-paced treadmill. Participants were either given explicit instructions (N = 8) or were allowed to freely interpret (N=8) how to coordinate with cues. RESULTS: For the free interpretation group, the data support an 8% increase in stride length and 14% increase in speed with exoskeleton torque across cue timing, as well as a 5% increase in stride length and 7% increase in speed with only vibrotactile cues. When given explicit instructions, participants modulated speed according to cue timing-increasing speed by 17% at cues in late swing and decreasing speed 11% at cues in early stance compared to no cue when exoskeleton torque was off. When torque was on, participants with explicit instructions had reduced changes in speed. CONCLUSION: These findings support that the presence of torque mitigates how cues were used and highlights the importance of explicit instructions for haptic cuing. Interpreting cues while walking with an exoskeleton may increase cognitive load, influencing overall human-exoskeleton performance for novice users. APPLICATION: Interactions between haptic feedback and exoskeleton use during gait can inform future feedback designs to support coordination between users and exoskeletons.

Effects of Concurrent and Terminal Visual Feedback on Ankle Co-Contraction in Older Adults during Standing Balance.

This preliminary investigation studied the effects of concurrent and terminal visual feedback during a standing balance task on ankle co-contraction, which was accomplished via surface electromyography of an agonist-antagonist muscle pair (medial gastrocnemius and tibialis anterior muscles). Two complementary mathematical definitions of co-contraction indices captured changes in ankle muscle recruitment and modulation strategies. Nineteen healthy older adults received both feedback types in a randomized order. Following an analysis of co-contraction index reliability as a function of surface electromyography normalization technique, linear mixed-effects regression analyses revealed participants learned or utilized different ankle co-contraction recruitment (i.e., relative muscle pair activity magnitudes) and modulation (i.e., absolute muscle pair activity magnitudes) strategies depending on feedback type and following the cessation of feedback use. Ankle co-contraction modulation increased when concurrent feedback was used and significantly decreased when concurrent feedback was removed. Ankle co-contraction recruitment and modulation did not significantly change when terminal feedback was used or when it was removed. Neither ankle co-contraction recruitment nor modulation was significantly different when concurrent feedback was used compared to when terminal feedback was used. The changes in ankle co-contraction recruitment and modulation were significantly different when concurrent feedback was removed as compared to when terminal feedback was removed. Finally, this study found a significant interaction between feedback type, removal of feedback, and order of use of feedback type. These results have implications for the design of balance training technologies using visual feedback.

Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework.

Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34∘ root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints.

Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running.

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

Physical and Cognitive Load Effects Due to a Powered Lower-Body Exoskeleton.

OBJECTIVE: The aim of this study is to determine the effects of a powered exoskeleton on measures of physical and cognitive performance. BACKGROUND: US warfighters carry heavy equipment into battle, and exoskeletons may reduce that burden. While exoskeletons are currently evaluated for their effects on physical performance, their cognitive effects are not currently considered. METHOD: Twelve military members participated in a simulated patrol task under three conditions: wearing a powered exoskeleton (PWR), an unpowered exoskeleton (UNP), and without wearing an exoskeleton (OFF). While following a confederate over obstacles at a constant pace, participants performed additional audio and visual tasks. Dependent measures included visual misses, visual reaction time, audio misses, audio reaction time, incremental lag time, and NASA-TLX scores. RESULTS: The variability in the follow-task lag time was lowest with OFF and highest with UNP, highlighting reduced ability to maintain pace with the exoskeleton. Visual reaction time was significantly slower with PWR compared to OFF for 5 of 12 subjects. The NASA-TLX overall workload scores were lower for OFF compared to PWR and UNP. CONCLUSION: Efforts to understand individual variability are warranted such that exoskeleton designs can be used for a wider set of the population. While not all subjects had measurable differences in the selected performance tasks, the perception of increased workload was present across subjects. It remains to be determined what difference in reaction time would be operationally relevant for task-specific settings. APPLICATION: Findings draw attention to the need to consider "cognitive fit" and subject differences in the design and implementation of exoskeletons.

Static, Dynamic, and Cognitive Fit of Exosystems for the Human Operator.

OBJECTIVE: To define static, dynamic, and cognitive fit and their interactions as they pertain to exosystems and to document open research needs in using these fit characteristics to inform exosystem design. BACKGROUND: Initial exosystem sizing and fit evaluations are currently based on scalar anthropometric dimensions and subjective assessments. As fit depends on ongoing interactions related to task setting and user, attempts to tailor equipment have limitations when optimizing for this limited fit definition. METHOD: A targeted literature review was conducted to inform a conceptual framework defining three characteristics of exosystem fit: static, dynamic, and cognitive. Details are provided on the importance of differentiating fit characteristics for developing exosystems. RESULTS: Static fit considers alignment between human and equipment and requires understanding anthropometric characteristics of target users and geometric equipment features. Dynamic fit assesses how the human and equipment move and interact with each other, with a focus on the relative alignment between the two systems. Cognitive fit considers the stages of human-information processing, including somatosensation, executive function, and motor selection. Human cognitive capabilities should remain available to process task- and stimulus-related information in the presence of an exosystem. Dynamic and cognitive fit are operationalized in a task-specific manner, while static fit can be considered for predefined postures. CONCLUSION: A deeper understanding of how an exosystem fits an individual is needed to ensure good human-system performance. Development of methods for evaluating different fit characteristics is necessary. APPLICATION: Methods are presented to inform exosystem evaluation across physical and cognitive characteristics.

Correction: McGrath, T., et al. An Auto-Calibrating Knee Flexion-Extension Axis Estimator using Principal Component Analysis with Inertial Sensors. Sensors 2018, 18(6), 1882.

The authors wish to make the following revisions to this paper [...].

An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors.

Inertial measurement units (IMUs) have been demonstrated to reliably measure human joint angles—an essential quantity in the study of biomechanics. However, most previous literature proposed IMU-based joint angle measurement systems that required manual alignment or prescribed calibration motions. This paper presents a simple, physically-intuitive method for IMU-based measurement of the knee flexion/extension angle in gait without requiring alignment or discrete calibration, based on computationally-efficient and easy-to-implement Principle Component Analysis (PCA). The method is compared against an optical motion capture knee flexion/extension angle modeled through OpenSim. The method is evaluated using both measured and simulated IMU data in an observational study (n = 15) with an absolute root-mean-square-error (RMSE) of 9.24∘ and a zero-mean RMSE of 3.49∘. Variation in error across subjects was found, made emergent by the larger subject population than previous literature considers. Finally, the paper presents an explanatory model of RMSE on IMU mounting location. The observational data suggest that RMSE of the method is a function of thigh IMU perturbation and axis estimation quality. However, the effect size for these parameters is small in comparison to potential gains from improved IMU orientation estimations. Results also highlight the need to set relevant datums from which to interpret joint angles for both truth references and estimated data.

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration.

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.

Estimating Stair Running Performance Using Inertial Sensors.

Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Characterising applied forces during positive pressure ventilation: a randomised cross-over simulation study.

OBJECTIVE: To characterise applied force on the face and head during simulated mask ventilation with varying mask, device and expertise level. DESIGN: Randomised cross-over simulation study. SETTING: A quiet, empty room in the children's hospital. PARTICIPANTS: Neonatal healthcare providers, categorised as novices and experts in positive pressure ventilation (PPV). INTERVENTIONS: PPV for 2 min each in a 2×2 within-subjects design with two masks (round and anatomic) and two ventilation devices (T-piece and self-inflating bag (SIB)). MAIN OUTCOME MEASURES: Applied force (Newton (N)) measured under the head and at four locations on the manikin's face (nasal bridge, mentum, left and right zygomatic arches) and symmetry of force applied around the mask rim. RESULTS: For the 51 participants, force applied to the head was greater with the SIB than the T-piece (mean (SD): 16.03 (6.96) N vs 14.31 (5.16) N) and greater with the anatomic mask than the round mask (mean (SD): 16.07 (6.80) N vs 14.26 (5.35) N). Underhead force decreased over the duration of PPV for all conditions. Force measured on the face was greatest at the left zygomatic arch (median (IQR): 0.97 (0.70-1.43) N) and least at the mentum (median (IQR): 0.44 (0.28-0.61) N). Overall, experts applied more equal force around the mask rim compared with novices (median (IQR): 0.46 (0.26-0.79) N vs 0.65 (0.24-1.18) N, p<0.001). CONCLUSION: We characterised an initial dataset of applied forces on the face and head during simulated PPV and described differences in force when considering mask type, device type and expertise.

Performance on a target acquisition task differs between augmented reality and touch screen displays.

Target acquisition tasks quantify human motor and perceptual abilities while performing discrete tasks to support interface design and sensorimotor assessments. This study investigated the effects of display, Touchscreen and Augmented Reality (AR), on a standardized 2D multidirectional target acquisition task. Thirty-two participants performed the target acquisition task with both modality types and at two indexes of difficulty. The touchscreen modality yielded improved performance over AR as measured by accuracy, precision, error rates, throughput, and movement time. Throughput using the nominal index of difficulty was 10.12 bits/s for touchscreen and 3.11 bits/s for AR. AR designers can use the results to improve performance when designing AR interfaces by selecting larger buttons when accuracy and efficiency are required and by embedding perception cues to button target surfaces such as depth and proximity cues.

Defining a systems framework for characterizing physical work demands with wearable sensors.

Measuring the physical demands of work is important in understanding the relationship between exposure to these job demands and their impact on the safety, health, and well-being of working people. However, work is changing and our knowledge of job demands should also evolve in anticipation of these changes. New opportunities exist for noninvasive long-term measures of physical demands through wearable motion sensors, including inertial measurement units, heart rate monitors, and muscle activity monitors. Inertial measurement units combine accelerometers, gyroscopes, and magnetometers to provide continuous measurement of a segment's motion and the ability to estimate orientation in 3-dimensional space. There is a need for a system-thinking perspective on how and when to apply these wearable sensors within the context of research and practice surrounding the measurement of physical job demands. In this paper, a framework is presented for measuring the physical work demands that can guide designers, researchers, and users to integrate and implement these advanced sensor technologies in a way that is relevant to the decision-making needs for physical demand assessment. We (i) present a literature review of the way physical demands are currently being measured, (ii) present a framework that extends the International Classification of Functioning to guide how technology can measure the facets of work, (iii) provide a background on wearable motion sensing, and (iv) define 3 categories of decision-making that influence the questions that we can ask and measures that are needed. By forming questions within these categories at each level of the framework, this approach encourages thinking about the systems-level problems inherent in the workplace and how they manifest at different scales. Applying this framework provides a systems approach to guide study designs and methodological approaches to study how work is changing and how it impacts worker safety, health, and well-being.

An Augmented Reality Hand-Eye Sensorimotor Impairment Assessment for Spaceflight Operations.

INTRODUCTION: Following a transition from microgravity to a gravity-rich environment (e.g., Earth, Moon, or Mars), astronauts experience sensorimotor impairment, primarily from a reinterpretation of vestibular cues, which can impact their ability to perform mission-critical tasks. To enable future exploration-class missions, the development of lightweight, space-conscious assessments for astronauts transitioning between gravity environments without expert assistance is needed.METHODS: We examined differences in performance during a two-dimensional (2D) hand-eye multidirectional tapping task, implemented in augmented reality in subjects (N = 20) with and without the presence of a vestibular-dominated sensorimotor impairment paradigm: the binaural bipolar application of a pseudorandom galvanic vestibular stimulation (GVS) signal. Metrics associated with both the impairment paradigm and task performance were assessed.RESULTS: Medial-lateral sway during balance on an anterior-posterior sway-referenced platform with eyes closed was most affected by GVS (effect size: 1.2), in addition to anterior-posterior sway (effect size: 0.63) and the vestibular index (effect size: 0.65). During the augmented reality task, an increase in time to completion (effect size: 0.63), number of misses (effect size: 0.52), and head linear accelerations (effect size: 0.30) were found in the presence of the selected GVS waveform.DISCUSSION: Findings indicate that this multidirectional tapping task may detect emergent vestibular-dominated impairment (near landing day performance) in astronauts. Decrements in speed and accuracy indicate this impairment may hinder crews' ability to acquire known target locations while in a static standing posture. The ability to track these decrements can support mission operations decisions.Allred AR, Weiss H, Clark TK, Stirling L. An augmented reality hand-eye sensorimotor impairment assessment for spaceflight operations. Aerosp Med Hum Perform. 2024; 95(2):69-78.

Ankle exoskeleton torque controllers based on soleus muscle models.

Powered exoskeletons are typically task-specific, but to facilitate their wider adoption they should support a variety of tasks, which requires generalizeable controller designs. In this paper, we present two potential controllers for ankle exoskeletons based on soleus fascicles and Achilles tendon models. The methods use an estimate of the adenosine triphosphate hydrolysis rate of the soleus based on fascicle velocity. Models were evaluated using muscle dynamics from the literature, which were measured with ultrasound. We compare the simulated behavior of these methods against each other and to human-in-the-loop optimized torque profiles. Both methods generated distinct profiles for walking and running with speed variations. One of the approaches was more appropriate for walking, while the other approach estimated profiles similar to the literature for both walking and running. Human-in-the-loop methods require long optimizations to set parameters per individual for each specific task, the proposed methods can produce similar profiles, work across walking and running, and be implemented with body-worn sensors without requiring torque profile parameterization and optimization for every task. Future evaluations should examine how human behavior changes due to external assistance when using these control models.

Telerehabilitation use and experiences in occupational and physical therapy through the early stages of the COVID-19 pandemic.

Telehealth has helped to increase access to rehabilitative services such as occupational and physical therapy. The early COVID-19 pandemic amplified the need for remote access to care, and the rapid implementation of telehealth systems provided a unique opportunity to learn from clinicians' experiences adopting telehealth for telerehabilitation applications. To understand these experiences, a self-administered online survey was conducted to capture perspectives on ease of telerehabilitation use and adoption from occupational and physical therapists. The survey captured retrospective views on telerehabilitation use pre-pandemic as well as real-time perspectives on telerehabilitation during the early stages of the pandemic (July to August 2020). The survey gathered information on clinician demographics (N = 109), clinicians' experiences with adopting or utilizing telerehabilitation systems, and their perceptions on remotely performing cognitive, emotional, and physical assessments via video-conferencing (a common mode of telehealth). Responses demonstrated a modest increase in telerehabilitation as a care setting (rate increase from 3.4% to 19.3%), and telerehabilitation was more generally tried during the early stages of the pandemic (41 clinicians explicitly reported telerehabilitation use). However, technology access and acceptance remained low, with 38 clinicians (35%) expressing concerns that technology was ineffective or impractical, unavailable, not covered by insurance, or not desired by their patients. Video-conferencing technology was perceived as generally ill-equipped to support clinicians in performing remote assessment tasks. Physical assessment tasks were considered particularly difficult, with 55% of clinicians rating their ability to perform these tasks in the range of moderately difficult to unable to perform. To address these difficulties and better augment clinical care, clinicians require more robust assessment methods that may combine video, mobile, and wearable technologies that would be accessible to a patient at home. When designing future telerehabilitation tools, information captured through these modes must be task-relevant, standardized, and understandable to a remote clinician.

Walking speed and dual task input modality impact performance on a self-paced treadmill.

Interference between a walking task (target speeds on a self-paced treadmill) and dual visual and tactile-visual response time task was investigated. Ambulatory dual-task scenarios reveal how attention is divided between walking and additional tasks, but the impact of walking speed and dual-task modality on gait characteristics and dual-task performance is unclear. The purpose of this study was to evaluate the effect of visual and tactile-visual dual-task on gait performance. Participants (n=15) targeted four speeds (0.5, 1.0, 1.3, and 1.5 m/s) on a self-paced treadmill with a visual speed indicator (a green region centered at the target speed). Participants completed the same speed profile on the treadmill without (Self-Paced) and with a response time dual task (Self-Paced with Dual Task) requiring finger-tap responses to go/no-go cues. Six gait characteristics were calculated: proportion of time in the desired speed green region (GTP), speed ratio (ratio of mean to target speed), time to green region after target speed change (NRT), normalized stride width (NSW), normalized stride length (NSL), and stride time (ST). Both stride length and width were normalized by participant leg length. Lower GTP and greater speed ratio at slower speeds during dual tasking indicate speed-dependent changes in gait characteristics. Changes in NSL and ST were more affected by speed than dual task. These findings support that when speed is a parameter that is tracked, participants do not universally decrease speed in the presence of a dual task. These findings can support the decisions made when designing new wearable technologies that support navigation, communication, and mobility.