. Significance : The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe’s performance a daunting task. Aim : We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes. Approach : Our software—modular optode configuration analyzer (MOCA)—implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities. Results : Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts. Conclusion : Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.

We introduce, for the first time, a wearable, modular, and fiberless fNIRS system made of diamond-shaped flexible-circuit-based modules, capable of full-head coverage, acquisition of 3-D positions, utilizing inter-module channels, and high flexibility in connection re-configurations.

a b s t r a c tThis paper presents the conceptual design and analysis of a system intended to increase the range, scientific capability, and safety of manned lunar surface exploration, requiring only a modest increase in capability over the Apollo mission designs. The system is intended to enable two astronauts, exploring with an unpressurized rover, to remove their space suits for an 8-h rest away from the lunar base and then conduct a second day of surface exploration before returning to base. This system is composed of an Environmental Control and Life Support System on the rover, an inflatable habitat, a solar shield and a solar power array. The proposed system doubles the distance reachable from the lunar base, thus increasing the area available for science and exploration by a factor of four. In addition to increasing mission capability, the proposed system also increases fault tolerance with an emergency inflatable structure and additional consumables to mitigate a wide range of suit or rover failures. The mass, volume, and power analyses of each subsystem are integrated to generate a total system mass of 124 kg and a volume of 594 L, both of which can be accommodated on the Apollo Lunar Roving Vehicle with minor improvements. & 2015 IAA. Published by Elsevier Ltd. on behalf of IAA. This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/4.0/).

Given projections of an increase in the number of consumers wearing sensors in the upcoming years coupled with NASA's technological roadmap for future human space exploration, there is a need to understand and appropriately incorporate the variability of humans during the use of sensors that extract human activity and diagnostics. Accurate estimations of variability in multiple donnings of sensor suites may aid algorithm development for wearable motion capture systems that make use of Inertial Measurement Units (IMUs). The accuracy of any algorithm incorporating these sensors is limited by the accuracy of the sensor to segment calibration. In this study, 22 participants self-placed IMUs on three locations and performed six prescribed motions during each of these five donnings. Placement of the IMU was quantified as distance, orientation, and rotation. For orientation of the sensors at the beginning of the prescribed motions, the bicep orientation mean was less than the forearm, which was less than the chest. No difference in sensor rotation was found between the bicep and forearm, but both locations differed from the chest location. It was found that even with a guide to assist with the starting and ending positions of a motion, the placement of the sensor on the human-body varied at the beginning and end of a motion. This study found no consistent effect of donning on placement, but did find an effect of motion on the measures. Since the placement measures did vary through a motion, the underlying assumptions of a rigid body model used by motion capture algorithms might not be appropriate. Motion capture algorithms need to be careful when using these rigid body assumptions and account for the changes in position and Euler angles due to natural human variability and calibration during multiple donnings of IMUs by non-experts. This study will aid in the development of quick don and doff sensor suites that can be reliably used by a non-expert for real-time decision making.