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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Gait Characterization and Analysis of Hereditary Amyloidosis Associated with Transthyretin Patients: A Case Series

Hereditary amyloidosis associated with transthyretin (ATTRv), is a rare autosomal dominant disease characterized by length-dependent symmetric polyneuropathy that has gait impairment as one of its consequences. The gait pattern of V30M ATTRv amyloidosis patients has been described as similar to that of diabetic neuropathy, associated with steppage, but has never been quantitatively characterized. In this study we aim to characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding and potential for supporting diagnosis and disease progression evaluation. We present a case series in which we conducted two gait analyses, 18 months apart, of five V30M ATTRv amyloidosis patients using a 12-camera, marker based, optical system as well as six force platforms. Linear kinematics, ground reaction forces, and angular kinematics results are analyzed for all patients. All patients, except one, showed a delayed toe-off in the second assessment, as well as excessive pelvic rotation, hip extension and external transverse rotation and knee flexion (in stance and swing phases), along with reduced vertical and mediolateral ground reaction forces. The described gait anomalies are not clinically quantified; thus, gait analysis may contribute to the assessment of possible disease progression along with the clinical evaluation.

Vilas-Boas, Maria do Carmo (ORCID:000000025012789X↗
DOE OSTI2024

User Impressions and Gait Analysis of Exoskeleton Device Usage in Generalized Tank Farm Activities

Tank farm workers involved in nuclear cleanup activities perform physically demanding tasks, typically while wearing heavy personal protective equipment (PPE). Exoskeleton devices have the potential to bring considerable benefit to this industry but have not been thoroughly studied in the context of nuclear cleanup. In this paper, we examine the performance of exoskeletons during a series of tasks emulating jobs performed on tank farms while participants wore PPE commonly deployed by tank farm workers. The goal of this study was to evaluate the effects of commercially available lower-body exoskeletons on a user’s gait kinematics and user perceptions. Three participants each tested three lower-body exoskeletons in a 70-min protocol consisting of level treadmill walking, incline treadmill walking, weighted treadmill walking, a weight lifting session, and a hand tool dexterity task. Results were compared to a no exoskeleton baseline condition and evaluated as individual case studies. The three participants showed a wide spectrum of user preferences and adaptations toward the devices. Individual case studies revealed that some users quickly adapted to select devices for certain tasks while others remained hesitant to use the devices. Temporal effects on gait change and perception were also observed for select participants in device usage over the course of the device session. Here, device benefit varied between tasks, but no conclusive aggregate trends were observed across devices for all tasks. Evidence suggests that device benefits observed for specific tasks may have been overshadowed by the wide array of tasks used in the protocol.

Tank farm↗
DOE OSTI2023

Handling gait disturbances with asynchronous timing

An example method may include i) detecting a disturbance to a gait of a robot, where the gait includes a swing state and a step down state, the swing state including a target swing trajectory for a foot of the robot, and where the target swing trajectory includes a beginning and an end; and ii) based on the detected disturbance, causing the foot of the robot to enter the step down state before the foot reaches the end of the target swing trajectory.

Blankespoor, Kevin↗
DOE OSTI2022

gait

SAND2022-6991 O gait is an extension to zeek, an open-source network analyzer that adds metadata to the existing connection and SSL logs. Most of this metadata is already exposed by the zeek script API. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Smutz, Charles↗
DOE OSTI2020

Wearable Sensors Measure Ankle Joint Changes of Patients with Parkinson’s Disease before and after Acute Levodopa Challenge

Background . Previous studies found levodopa could improve the activity of the ankle joints of patients with Parkinson’s disease (PD). But ankle joint movement is composed of four motion ranges. The specific changes of four motion ranges in PD remain unknown. Objective . The purpose of this study was to decompose the complex ankle joint movement, measure ankle joint changes before and after the acute levodopa challenge test (ALCT), and investigate the effects of these parameters on gait performance. Methods . 29 PD patients and 30 healthy control subjects (HC) completed the Instrumented Stand and Walk (ISAW) test and gait parameters were collected by the JiBuEn gait analysis system. The percentage of improvement of gait data and the UPDRS III in the on-drug condition (ON) were determined with respect to the off-drug condition (OFF). Results . We observed a reduction in the heel strike angle (HS), 3-plantarflexion (3-PF) angle, and 4-dorsiflexion (4-DF) angle of ankle joints. We did not find significant difference in the toe-off angle (TO), 1-plantarflexion (1-PF) angle, and 2-dorsiflexion (2-DF) angle among three groups. Stride length improvement rate was significantly correlated with HS ( r s = 0.616, P < 0.001 ) and 3-PF ( r s = 0.639, P < 0.001 ) improvement rates. The improvement in the sum of rigidity items (UPDRS motor subsection item 22) was also correlated with HS ( r s = 0.389, P = 0.037 ) and 3-PF ( r s = 0.373, P = 0.046 ) improvement rates. Conclusions . Exogenous levodopa supplementation can significantly reduce the rigidity of patients with PD, improve their 3-PF and 4-DF of ankle joint kinematic parameters, and ultimately enhance their gait.

Wu, Zhuang↗
DOE OSTI2025

A generative artificial intelligence framework for long-time plasma turbulence simulations

Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.

Artificial intelligence↗
DOE OSTI2021

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone ( n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88–0.94]; 0.59[0.48–0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = −0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Wu, Chao-Yi↗
DOE OSTI2022

Dual-Biometric Human Identification Using Radar Deep Transfer Learning

Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Design and Closed‐Loop Motion Planning of an Untethered Swimming Soft Robot Using 2D Discrete Elastic Rods Simulations

Despite tremendous progress in the development of untethered soft robots in recent years, existing systems lack the mobility, model‐based control, and motion planning capabilities of their piecewise rigid counterparts. As in conventional robotic systems, the development of versatile locomotion of soft robots is aided by the integration of hardware design and control with modeling tools that account for their unique mechanics and environmental interactions. Here, a framework for physics‐based modeling, motion planning, and control of a fully untethered swimming soft robot is introduced. This framework enables offline co‐design in the simulation of robot parameters and gaits to produce effective open‐loop behaviors and enables closed‐loop planning over motion primitives for feedback control of a frog‐inspired soft robot testbed. This pipeline uses a discrete elastic rods (DERs) physics engine that discretizes the soft robot as many stretchable and bendable rods. On hardware, an untethered aquatic soft robot that performs frog‐like rowing behaviors is engineered. Hardware validation verifies that the simulation has sufficient accuracy to find the best candidates for sets of parameters offline. The simulator is then used to generate a trajectory library of the robot's motion in simulation that is used in real‐time closed‐loop path following experiments on hardware.

Huang, Xiaonan↗
DOE OSTI2020

Regression models using shapes of functions as predictors

Functional variables are often used as predictors in regression problems. A commonly used parametric approach, called scalar-on-function regression, uses the $\mathbb L^2$ inner product to map functional predictors into scalar responses. This method can perform poorly when predictor functions contain undesired phase variability, causing phases to have disproportionately large influence on the response variable. One past solution has been to perform phase–amplitude separation (as a pre-processing step) and then use only the amplitudes in the regression model. In this paper, we propose a more integrated approach, termed elastic functional regression model (EFRM), where phase-separation is performed inside the regression model, rather than as a pre-processing step. This approach generalizes the notion of phase in functional data, and is based on the norm-preserving time warping of predictors. Due to its invariance properties, this representation provides robustness to predictor phase variability and results in improved predictions of the response variable over traditional models. We demonstrate this framework using a number of datasets involving gait signals, NMR data, and stock market prices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cerebellar dysfunction in a mouse model of childhood-onset manganese-induced dystonia parkinsonism

Humans with pathogenic variants of the manganese (Mn) transporter gene SLC39A14 exhibit highly elevated brain Mn concentrations and childhood-onset dystonia-parkinsonism. Here we show that Slc39a14-knockout (KO) mice, a preclinical model of the disease with elevated Mn concentrations in the CB, express deficits in physiological tremor implicating cerebellar (CB) dysfunction. Imaging of intracellular Mn in Purkinje cells (PCs) using synchrotron-based X-ray fluorescence microscopy confirmed highly elevated Mn concentrations in the PCs of Slc39a14-KO mice. To determine biological pathways altered in the CB of Slc39a14-KO mice relative to wildtype (WT), we performed RNA sequencing and discovered significant upregulation of pathways and genes regulating immune response and cell death. To substantiate these findings, we performed quantitative autoradiography of the neuroinflammation biomarker Translocator Protein 18 kDa (TSPO) which was significantly increased in the CB of Slc39a14-KO mice relative to WT. The latter findings were confirmed via immunostaining with the microglial marker Iba-1, revealing widespread microglia activation and clustering in the CB cortex. Immunostaining for cleaved caspase-3 (cCASP3), a marker of apoptosis, showed increased number of PCs with positive immunolabeling for cCASP3 in Slc39a14-KO mice relative to WT. Degeneration of PCs was confirmed by Hematoxylin and Eosin (H&E) staining. Lastly, functional electrophysiological assessment of CB neurocircuitry revealed a marked decrease in firing rates of cerebellar nuclei (CN) neurons and increased variability of PC simple spikes firing. Collectively, these findings show, for the first time, Mn-induced PC degeneration and dysfunctional CB circuitry in Slc39a14-KO mice providing additional evidence for the pathological underpinnings of the dystonia-like movements, balance, and gait abnormalities in SLC39A14 mutation carriers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-performance piezoelectric composites via β phase programming

Polymer-ceramic piezoelectric composites, combining high piezoelectricity and mechanical flexibility, have attracted increasing interest in both academia and industry. However, their piezoelectric activity is largely limited by intrinsically low crystallinity and weak spontaneous polarization. Here, we propose a Ti 3 C 2 T x MXene anchoring method to manipulate the intermolecular interactions within the all-trans conformation of a polymer matrix. Employing phase-field simulation and molecular dynamics calculations, we show that OH surface terminations on the Ti 3 C 2 T x nanosheets offer hydrogen bonding with the fluoropolymer matrix, leading to dipole alignment and enhanced net spontaneous polarization of the polymer-ceramic composites. We then translated this interfacial bonding strategy into electrospinning to boost the piezoelectric response of samarium doped Pb (Mg1/3Nb 2/3 )O 3 -PbTiO 3 /polyvinylidene fluoride composite nanofibers by 160% via Ti 3 C 2 T x nanosheets inclusion. With excellent piezoelectric and mechanical attributes, the as-electrospun piezoelectric nanofibers can be easily integrated into the conventional shoe insoles to form a foot sensor network for all-around gait patterns monitoring, walking habits identification and Metatarsalgi prognosis. This work utilizes the interfacial coupling mechanism of intermolecular anchoring as a strategy to develop high-performance piezoelectric composites for wearable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Emergent microrobotic oscillators via asymmetry-induced order

Abstract Spontaneous oscillations on the order of several hertz are the drivers of many crucial processes in nature. From bacterial swimming to mammal gaits, converting static energy inputs into slowly oscillating power is key to the autonomy of organisms across scales. However, the fabrication of slow micrometre-scale oscillators remains a major roadblock towards fully-autonomous microrobots. Here, we study a low-frequency oscillator that emerges from a collective of active microparticles at the air-liquid interface of a hydrogen peroxide drop. Their interactions transduce ambient chemical energy into periodic mechanical motion and on-board electrical currents. Surprisingly, these oscillations persist at larger ensemble sizes only when a particle with modified reactivity is added to intentionally break permutation symmetry. We explain such emergent order through the discovery of a thermodynamic mechanism for asymmetry-induced order. The on-board power harvested from the stabilised oscillations enables the use of electronic components, which we demonstrate by cyclically and synchronously driving a microrobotic arm. This work highlights a new strategy for achieving low-frequency oscillations at the microscale, paving the way for future microrobotic autonomy.

42 ENGINEERING↗
DOE OSTI2023

Geolocation tracking for human identification and activity recognition using radar deep transfer learning

Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.

Alkasimi, Ahmad↗
DOE OSTI2024

From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

Bolme, David↗
DOE OSTI2020

Long-term persistence of infectious Zika virus: Inflammation and behavioral sequela in mice

The neurodevelopmental defects associated with ZIKV infections early in pregnancy are well documented, however the potential defects and long-term consequences associated with milder infections in late pregnancy and perinatal period are less well understood. To model these, we challenged 1 day old (P1) immunocompetent C57BL/6 mice with ZIKV. The animals developed a transient neurological syndrome including unsteady gait, kinetic tremors, severe ataxia and seizures 10–15 days post-infection (dpi) but symptoms subsided after a week, and most animals survived. Despite apparent recovery, MRI of convalescent mice show reduced cerebellar volume that correlates with altered coordination and motor function as well as hyperactivity and impulsivity. Persistent mRNA levels of pro-inflammatory genes including Cd80 , Il-1α , and Ifn-γ together with Cd3 , Cd8 and perforin ( PrfA) , suggested persistence of low-grade inflammation. Surprisingly, the brain parenchyma of convalescent mice harbor multiple small discrete foci with viral antigen, active apoptotic processes in neurons, and cellular infiltrates, surrounded by activated astrocytes and microglia as late as 1-year post-infection. Detection of negative-sense strand viral RNA and isolation of infectious virus derived from these convalescent mice by blinded passage in Vero cells confirmed long-term persistence of replicating ZIKV in CNS of convalescent mice. Although the infection appears to persist in defined reservoirs within CNS, the resulting inflammation could increase the risk of neurodegenerative disorders. This raises concern regarding possible long-term effects in asymptomatic children exposed to the virus and suggests that long-term neurological and behavioral monitoring as well as anti-viral treatment to clear virus from the CNS may be useful in patients exposed to ZIKV at an early age.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

TEAMER: Electrically Engaged Undulation (EEL) System

The Electrically Engaged UnduLation (EEL) system is a buoyancy-driven submersible device for powering oceanographic instruments. Physically, EEL is a slender body whose flexible spine is made up of energy units interconnected by uniaxial hinges. Each unit consists of a pair of piezoelectric elements that converts the bending stress into electrical current to a battery charging circuit. An outer plastic skin forms a seal against water and allows for flexibility at hinge locations. At the top is a bluff body with electronics that holds a ballast for buoyancy adjustment. The bluff body is also responsible for creating fluid instabilities in its wake. When gliding through the water (mode 2), the spine will flex in response to the alternating vortices that shed from the head. This "lock-in" phenomenon occurs when the frequency at which vortices shed resonates with the EEL natural frequency, during which the efficient gaits were found in species of sea snake, eels, and fish. For active propulsion, a single motor can be placed at the first segment and provide the oscillatory input for propulsion similar to a dolphin's kick. Such anguilliform swimming is both efficient and nearly silent compared to a spinning propeller. Ultimately, mimicking bio-locomotion provides a viable path to a drag-reduced, self-propelled energy harvesting system for ocean monitoring.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

TEAMER: Electrically Engaged Undulation System for Unmanned Underwater Vehicles

This TEAMER RFTS 1 (Request for Technical Support) project supported the flume tank testing of a long range, high endurance unmanned underwater vehicle (UUV) to monitor maritime space. Today, battery-powered remotely operated vehicles (ROVs) lack the duration to make persistent, wide-area data collection possible.The proposed solution, an Electrically Engaged UnduLation (EEL) drone, can sustain missions for longer duration through hydrodynamic energy harvesting. Power is provisioned via the piezoelectric effect, a material-led phenomenon that converts applied stress into electricity. The EEL subsystems include power, propulsion, navigation, ballast, telemetry, and instrumentation. By mimicking the gait of aquatic eels, EEL can counter currents during maneuvering and level-flight. The identified opportunity is in the future capability of extreme endurance UUVs in swarms. The specific goal for the EEL development is to expand the spatio-temporal coverage of the existing ocean observation mission by overcoming significant challenges of autonomous robotics. Some of the challenges presented include novel compliant mechanism for robust actuation, bio-inspired design to emulate efficient locomotion, smart material-based energy harvesting for sustained power, and swarming architecture through enabled autonomy.

16 TIDAL AND WAVE POWER↗