Prediction of Lunar- and Martian-Based Intra- and Site-to-Site Task Performance.

BACKGROUND: This study aimed to investigate the feasibility of determining the physiological parameters associated with the ability to complete simulated exploration type tasks at metabolic rates which might be expected for lunar and Martian ambulation. METHODS: Running V̇O2max and gas exchange threshold (GET) were measured in 21 volunteers. Two simulated extravehicular activity field tests were completed in 1 G in regular athletic apparel at two intensities designed to elicit metabolic rates of ∼20.0 and ∼30.0 ml · kg(-1) · min(-1), which are similar to those previously reported for ambulation in simulated lunar- and Martian-based environments, respectively. RESULTS: All subjects were able to complete the field test at the lunar intensity, but 28% were unable to complete the field test at the Martian intensity (non-Finishers). During the Martian field test there were no differences in V̇O2 between Finishers and non-Finishers, but the non-Finishers achieved a greater %V̇O2max compared to Finishers (78.4 ± 4.6% vs. 64.9 ± 9.6%). Logistic regression analysis revealed fitness thresholds for a predicted probability of 0.5, at which Finishing and non-Finishing are equally likely, and 0.75, at which an individual has a 75% chance of Finishing, to be a V̇O2max of 38.4 ml · kg(-1) · min(-1) and 40.0 ml · kg(-1) · min(-1) or a GET of 20.1 ml · kg(-1) · min(-1) and 25.1 ml · kg(-1) · min(-1), respectively (χ(2) = 10.2). Logistic regression analysis also revealed that the expected %V̇O2max required to complete a field test could be used to successfully predict performance (χ(2) = 19.3). DISCUSSION: The results of the present investigation highlight the potential utility of V̇O2max, particularly as it relates to the metabolic demands of a surface ambulation, in defining successful completion of planetary-based exploration field tests.

Real-time processing of electromyograms in an automated hand-forearm ergometer data collection and analysis system.

An automated hand-forearm ergometer with realtime data analysis would be a helpful tool to evaluate muscle fatigue mid-experiment, offering insights into changes in electromyogram parameters that can be used to track fatigue in the hand and forearm musculature. This work presents real-time additions to a custom, automated hand-forearm ergometer that will perform mid-experiment signal processing and help to identify fatigue onset and predict task failure.

A paraeducator glove for counting disabled-child behaviors that incorporates a Bluetooth Low Energy wireless link to a smart phone.

Behavior tracking with severely disabled children can be a challenge, since dealing directly with a child's behavior is more immediately pressing than the need to record an event for tracking purposes. By the time a paraeducator (`para') is able to break away and record events, behavior counts can be forgotten. This paper presents a paraeducator glove design that can help to track behaviors with minimal distraction by allowing a paraeducator to touch their thumb to one of their other four fingers, where each finger represents a different behavior. Count data are packaged by a microcontroller board on the glove and then sent wirelessly to a smart phone via a Bluetooth Low Energy (BLE) link. A customized BLE profile was designed for this application to promote real-time recording. These data can be forwarded to a database for further analysis. This para glove design addresses basic needs of a wearable device that employs BLE, including local data collection, BLE data transmission, and remote data recording. More functional sensors can be added to this platform to support other wearable scenarios.

Automated hand-forearm ergometer data collection system.

Handgrip contractions are a standard exercise modality to evaluate cardiovascular system performance. Most conventional ergometer systems of this nature are manually controlled, placing a burden on the researcher to guide subject activity while recording the resultant data. This paper presents updates to a hand-forearm ergometer system that automate the control and data-acquisition processes. A LabVIEW virtual instrument serves as the centerpiece for the system, providing the subject/researcher interfaces as well as coordinating data acquisition from both traditional and new sensors. Initial data indicate the viability of the system with regard to its ability to obtain consistent and physiologically meaningful data.