Metabolic adaptation and related biomechanics in an ankle-based exoskeleton system during six sessions of steady state walking.

Ankle-based exoskeletons have demonstrated metabolic benefits during steady-state walking; however, variability exists in individual adaptation timelines necessary to achieve those benefits. This study assessed timelines for metabolic and gait-related adaptation while wearing an ankle-based exoskeleton while powered (EXOP) compared to unpowered (EXNP) and no device worn (NOEX). Metabolic (VO2) and biomechanics data were collected while 14 participants walked on a treadmill at 1.3 m/s for six sessions. To better understand variability in responses to wearing exoskeletons, the cohort was divided based on the slope of the VO2 response of the first two sessions in the EXOP condition, and gait parameters were compared between subgroups. Repeated measures analyses of variance revealed a significant (p ≤ 0.001) 10% VO2 reduction for EXOP compared to EXNP and a non-significant 2.5% reduction for EXOP v NOEX. Lack of significant session-based comparisons indicated no additional VO2 adaptation; however, significant session-related results for peak knee flexion (interaction, p = 0.042) and step width (session main effect, p = 0.003) suggest gait-related adaptation continued during the sessions. Subgroup results indicated different response profiles to wearing exoskeletons; and implications of classifying initial responses based on metabolic response are discussed as an approach to understand what drives variation in responses to these devices.

After initial training, VO₂ reductions were observed with an ankle-based exoskeleton during the initial session and those reductions were maintained for the remaining sessions. Some gait-related variables continued to change over the remaining sessions. Exploratory work based on differences between early metabolic responses revealed potential adaptation strategy subgroups.

Users Maintain Task Accuracy and Gait Characteristics During Missed Exoskeleton Actuations Through Adaptations In Joint Kinematics.

In operational settings, lower-limb active exoskeletons may experience errors, where an actuation that should be present is missed. These missed actuations may impact users' trust in the system and the adapted human-exoskeleton coordination strategies. In this study, we introduced pseudorandom catch trials, in which an assistive exoskeleton torque was not applied, to understand the immediate responses to missed actuations and how users' internal models to an exoskeleton adapt upon repeated exposure to missed actuations. Participants (N = 15) were instructed to complete a stepping task while wearing a bilateral powered ankle exoskeleton. Human-exoskeleton coordination and trust were inferred from task performance (step accuracy), step characteristics (step length and width), and joint kinematics at selected peak locations of the lower limb. Step characteristics and task accuracy were not impacted by the loss of exoskeleton torque as hip flexion was modulated to support completing the stepping task during catch trials, which supports an impacted human-exoskeleton coordination. Reductions in ankle plantarflexion during catch trials suggest user adaptation to the exoskeleton. Trust was not impacted by catch trials, as there were no significant differences in task performance or gait characteristics between earlier and later strides. Understanding the interactions between human-exoskeleton coordination, task accuracy, and step characteristics will support development of exoskeleton controllers for non-ideal operational settings.

Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task.

Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance.

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.