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.

Characterizing Relationships Among the Cognitive, Physical, Social-emotional, and Health-related Traits of Military Personnel.

INTRODUCTION: Personnel engaged in high-stakes occupations, such as military personnel, law enforcement, and emergency first responders, must sustain performance through a range of environmental stressors. To maximize the effectiveness of military personnel, an a priori understanding of traits can help predict their physical and cognitive performance under stress and adversity. This work developed and assessed a suite of measures that have the potential to predict performance during operational scenarios. These measures were designed to characterize four specific trait-based domains: cognitive, health, physical, and social-emotional. MATERIALS AND METHODS: One hundred and ninety-one active duty U.S. Army soldiers completed interleaved questionnaire-based, seated task-based, and physical task-based measures over a period of 3-5 days. Redundancy analysis, dimensionality reduction, and network analyses revealed several patterns of interest. RESULTS: First, unique variable analysis revealed a minimally redundant battery of instruments. Second, principal component analysis showed that metrics tended to cluster together in three to five components within each domain. Finally, analyses of cross-domain associations using network analysis illustrated that cognitive, health, physical, and social-emotional domains showed strong construct solidarity. CONCLUSIONS: The present battery of metrics presents a fieldable toolkit that may be used to predict operational performance that can be clustered into separate components or used independently. It will aid predictive algorithm development aimed to identify critical predictors of individual military personnel and small-unit performance outcomes.