Full Motions Research Articles

Learning data science methods through a mobile device and full body motion data

This study presents a mobile app that facilitates undergraduate students to learn data science through their own full body motions. Leveraging the built-in camera of a mobile device, the proposed app captures the user and feeds their images into an open-source computer-vision algorithm that localizes the key joint points of human body. As students can participate in the entire data collection process, the obtained motion data is context-rich and personally relevant to them. The app utilizes the collected motion data to explain various concepts and methods in data science under the context of human movements. The app also visualizes the geometric interpretation of data through various visual aids, such as interactive graphs and figures. In this study, we use principal component analysis, a commonly used dimensionality reduction method, as an example to demonstrate the proposed learning framework. Strategies to encompass other learning modules are also discussed for further improvement.

Wrinkled, cracked and bridged carbon networks for highly sensitive and stretchable strain sensors

With rapid advance of wearable electronics technologies, flexible strain sensors having both high sensitivity and high stretchability are highly desired. In this work, inspired by lotus roots whose fibers remain joined even after fracture, highly sensitive and stretchable strain sensors are designed using single-walled carbon nanotube (SWNT)/graphene oxide (GO) hybrid thin films with unique wrinkled, cracked and bridged morphologies. The distinctive wrinkled and cracked structure is created by tuning the pre-stretching releasing of the hybrid film on a silicone rubber substrate. Under tension, the myriad SWNTs bridged the wrinkled film by sliding within the hybrid film giving rise to high stretchability, while the nano/microscale cracks provide the strain sensor with a high sensitivity through tunneling. Thanks to the synergy arising from the wrinkles, cracks and bundles bridged the cracks, the hybrid sensor exhibits a wide sensing range of 100 %, an ultrahigh gauge factor of 2000 with excellent stability for over 1000 cycles. These exceptional properties enable the sensor to monitor full range human motions from tiny eye blinks to large joint movements. A wearable gaming controller is prototyped using the developed sensor to demonstrate voice-empowered maneuver of car racing games.

Testing the earthquake damage and vulnerability of the Cherichira aqueduct bridge, Kairouan (Tunisia) with discrete element modeling

The Cherichira aqueduct, originating from Roman times, supplied the city of Kairouan, Tunisia, with water and has had alternating phases of damage and repair after the Roman and during the Aghlabid and Fatimid era. A crucial section of the lifeline is the Cherichira aqueduct bridge (CAB), and scholars have discussed the possibility that earthquake ground motions caused damage which disrupted the water supply of Kairouan. However, little was known about the dynamic behavior of the bridge and its vulnerability to earthquake ground motions. Computer-aided design based on a detailed laser scan model of the remains of the bridge and published data were used to reconstruct the CAB of the Aghlabid period. Subsequently converted into a discrete element model, the digital version of the CAB was subjected to analytic ground motion signals and full 3D simulations of local earthquakes. The CAB model shows a fundamental eigenfrequency close to 1 Hz in the direction transverse to its trend, and single-component ground motions in this direction with peak particle velocities above 1.0 m/s cause damage to the top of the CAB. Among the earthquake scenarios with full 3D ground motions applied, only the activation of a nearby thrust fault caused distinct damage. While fractures in the ruins of the CAB cutting through the upper part of the bridge which includes the water canal are a likely cause for disrupting the water flow and are similar to the damage pattern produced in the model calculations, a solely seismogenic cause of the total collapse of some parts of the CAB cannot be verified by the simulations.

Radial velocities from far-red spectra of Carina Arm O and early B stars

ABSTRACT Massive O and early B stars are important markers of recent star formation and exert a significant influence on their environments during their short lives via photoionization and winds and when they explode as supernovae. In the Milky Way, they can be detected at great distances but often lie behind large dust columns, making detection at short wavelengths difficult. In this study, the use of the less extinguished far-red spectrum (8400–8800 Å) for radial velocity measurement is examined. Results are reported for a sample of 164 confirmed OB stars within a 2° field positioned on the Carina Arm. Most stars are at distances between 3 and 6 kpc, and Westerlund 2 is at the field edge. The measured radial velocities have errors concentrated in the 3–10 km s−1 range, with a systematic uncertainty of 2–3 km s−1. These are combined with Gaia-mission astrometry to allow full space motions to be constructed. Up to 22 stars are likely to be runaways, although 8 of them are as likely to be interloping (so far undetected) binaries. The mean azimuthal motion of the sample fits in with recent measurements of Galactic disc rotation. In the Galactocentric radial direction, the mean motion indicates modest infall at a speed of ∼10 km s−1. This experiment shows that weak Paschen lines in the far-red can yield credible radial velocity determination, offering the prospect of exploring OB-star kinematics over much more of the Galactic disc than has hitherto been possible.

Highly Sensitive MXene Helical Yarn/Fabric Tactile Sensors Enabling Full Scale Movement Detection of Human Motions

AbstractTextile‐based wearable electronics combined with nanomaterials are ideal devices that have good electrical and thermal conductivity, and flexibility. With a metal conductivity and metalloid flexibility, 2D titanium carbide MXene is excellent enough for producing intelligent textiles. Here, a wrapping forming method is provided to fabricate MXene textile strain (MTS) sensors with high gauge factor (GF) of 715.94 as high as strain of 200%. Periodic cycling indicated that the MTS wrapped yarn is stable, which can track changes of facial expressions precisely as the small deformation. Weaving MTS yarns into fabric as glove and sensing belt can recognize sign language and record middle and large motions of human body, respectively. On the other hand, walking postures and electric heating are accurately detected by coating MXene dispersions on elastic and breathable spacer fabric as piezoresistive insoles, which is especially beneficial to the elderly and patients with foot diseases. Therefore, a unique strategy is proposed for full scale human motions monitoring by fabricating MXene helical yarns served as sensitive strain sensors, weft rib fabric that detected the in‐plane deformations and spacer fabric piezoresistive insole behaved as pressure sensors to monitor the step patterns so that suiting for wearable versatile sensing system.

Proper motions of OB stars in the far Carina Arm

ABSTRACT In large-scale maps of the Galactic disc, the Carina Arm stands out as a clear spiral feature, hosting prominent star clusters and associations rich in massive stars. We study the proper motions of 4199 O and early B most likely in the far Carina Arm, at distances mainly in excess of 4 kpc from the Sun, within the sky region, 282° < ℓ < 294° and −3° < b < +1° (Galactic coordinates). The sample is constructed by extending an existing blue-selected catalogue, and cross-matching with Gaia EDR3 astrometry. The observed pattern of proper motions is modulated into a saw-tooth pattern, with full amplitude approaching 1 mas yr−1, recurring roughly every 2–3 degrees of longitude (200–300 pc at the median OB-star distance of 5.8 kpc). Kinematic perturbation of underlying circular rotation is most likely present. The data also reveal a moving group containing >50 OB stars at ℓ ∼ 286°, b ∼ −1.4° behind the main run of the far arm. An analysis of relative proper motions is performed that yields an incidence of runaway O stars of at least 10 per cent (potentially >20 per cent when full space motions become available). To map where runaways have run away from, we set up simulations for the region that assume linear trajectories and test for trajectory impact parameter in order to identify likely ejection hot spots. We find the method currently gives good results for times of flight of up to ∼4 Myr. It shows convincingly that only NGC 3603 and Westerlund 2 have ejected OB stars in significant numbers. Indeed, both clusters have experienced intense spells of ejection between 0.6–0.9 and 0.5–0.8 Myr ago, respectively.

Small-Scale Magnetic Actuators with Optimal Six Degrees-of-Freedom.

Magnetic miniature robots (MMRs) are small-scale, untethered actuators which can be controlled by magnetic fields. As these actuators can non-invasively access highly confined and enclosed spaces; they have great potential to revolutionize numerous applications in robotics, materials science, and biomedicine. While the creation of MMRs with six-degrees-of-freedom (six-DOF) represents a major advancement for this class of actuators, these robots are not widely adopted due to two critical limitations: i) under precise orientation control, these MMRs have slow sixth-DOF angular velocities (4degree s-1 ) and it is difficult to apply desired magnetic forces on them; ii) such MMRs cannot perform soft-bodied functionalities. Here a fabrication method that can magnetize optimal MMRs to produce 51-297-fold larger sixth-DOF torque than existing small-scale, magnetic actuators is introduced. A universal actuation method that is applicable for rigid and soft MMRs with six-DOF is also proposed. Under precise orientation control, the optimal MMRs can execute full six-DOF motions reliably and achieve sixth-DOF angular velocities of 173degree s-1 . The soft MMRs can display unprecedented functionalities; the six-DOF jellyfish-like robot can swim across barriers impassable by existing similar devices and the six-DOF gripper is 20-folds quicker than its five-DOF predecessor in completing a complicated, small-scale assembly.

PanoMan: Sparse Localized Components–based Model for Full Human Motions

Parameterizing Variations of human shapes and motions is a long-standing problem in computer graphics and vision. Most of the existing methods only deal with a specific kind of motion, such as body poses, facial expressions, or hand gestures. We propose PanoMan (sParse locAlized compoNents based mOdel for full huMAn motioNs) to handle shape variation and full-motion across body, face, and hand in a unified framework. Like previous approaches, we factor shape variation into principal components to obtain a human shape space that approximates the shape of arbitrary identity. We then analyze sparse localized components in terms of relative edge length and dihedral angle to capture full motions of body poses, facial expressions, and hand gestures. The final piece of our model is a multilayer perceptron (MLP) that fits the residual between the ground truth and the aforementioned two-level approximation. As an application, we employ the discrete-shell deformation to drive the model to fit sparse constraints such as joint positions and surface feature points. We thoroughly evaluate PanoMan on body, face, and hand motion benchmarks as well as scanned data. The existing skinning-based techniques suffer from joint collapsing when encountering twisting motion of joints. Experiments show that PanoMan can capture all kinds of full human motions with high quality and is easier than the state-of-the-art models in recovering poses with wide joint twisting and complex hand gestures.

Emotion Transfer for 3D Hand and Full Body Motion Using StarGAN

In this paper, we propose a new data-driven framework for 3D hand and full-body motion emotion transfer. Specifically, we formulate the motion synthesis task as an image-to-image translation problem. By presenting a motion sequence as an image representation, the emotion can be transferred by our framework using StarGAN. To evaluate our proposed method’s effectiveness, we first conducted a user study to validate the perceived emotion from the captured and synthesized hand motions. We further evaluate the synthesized hand and full body motions qualitatively and quantitatively. Experimental results show that our synthesized motions are comparable to the captured motions and those created by an existing method in terms of naturalness and visual quality.

A numerical solver for coupled dynamic simulation of glacial ice impacts considering hydrodynamic-ice-structure interaction

Glacial ice features pose great threats on the safety of ships and offshore structures in the arctic. House sized bergy bits or growlers are of particular concern because of the detection capability limits of marine radars. Analysis and design of structures against collisions from such glacial ice bodies has always been challenging due to the complicated hydrodynamic-ice-structure interaction.This paper proposes a numerical solver for coupled simulation of glacial ice impacts accounting for the effects of hydrodynamic-ice-structure interaction. The solver adopts user subroutines provided in LS-DYNA and combines three different modules, i.e. the BWH (Bressan-Williams-Hill) criterion for the prediction of fracture of steels, a hydrostatic pressure dependent plasticity-based material model for constitutive modelling of ice, and the linear potential flow theory for hydrodynamic loads.The proposed solver is verified and calibrated to ice resistance data from field tests and is then applied to simulate ice collisions on a semi-submersible platform column. Collision scenarios with both in-plane 3DOF and full 6DOF ice motions are considered. The results are discussed with respect to ice motion trajectories, ice crushing and structural damage under the combined action of ice indentation and sliding loads. The dissipated energy predicted by external dynamic models is compared with simulation results and discussed.

Volumetric Motion Magnification: Subtle Motion Extraction from 4D Data

The extension of Phase-based Video Motion Magnification into three-dimensions is presented in this work. The technique uses a 3D complex steerable pyramid to decompose volumetric frames of 4D dataset. The resulting decomposition can be processed to filter, amplify, or attenuate subtle motions occurring locally within the 4D data, before inversion of the transform is performed to recreate the volumetric images. Recent research has used Phase Based Video Motion Magnification to extract subtle motion from 2D slices of internal deformation data. This work demonstrates how Phase Based Video Motion Magnification can be extended to work on full volumetric images to extract full 3D volumetric motions. The Volumetric Motion Magnification technique is demonstrated qualitatively, to amplify and extract the indiscernible motion of a cylinder for visual identification. Then, the ability of the technique to enhance the motion noise floor of Digital Volume Correlation by more than a factor of 10 is demonstrated.

Electro-hydro-elastic modeling of Structure-Borne Traveling Waves and their application to aquatic swimming motions

Structure-Borne Traveling Waves (SBTWs) can replicate the combined undulatory and oscillatory motions utilized by body and/or caudal fin (BCF) aquatic swimmers. However, when SBTWs are generated in a dense fluid media, such as water, there is poor understanding of the Fluid–Structure Interactions (FSI). There is also limited understanding of how to achieve the full undulatory motions. This study demonstrates the effect of FSI on SBTWs and investigates the relationship between SBTW quality (undulatory vs. oscillatory motion) and forcing input. SBTWs function by leveraging a surface’s structural properties (mode shapes and natural frequencies) to deform a surface into a dynamic waveform. SBTWs are a combination of traveling and standing waves, and can range in quality from pure traveling wave (undulatory) to pure standing wave (oscillatory). SBTWs require a small number of actuators, can be generated on any thin-walled surface (flat or curved), and have customizable parameters (direction, frequency, wavelength). However, SBTWs are sensitive to FSI due to their dependence on the structural properties. In this study, an electro-hydro-elastic (EHE) model is developed to capture the effect of FSI on SBTWs and investigate how the forcing input affects the resultant waveform (undulatory vs. oscillatory). The EHE model consists of a clamped-free beam with discrete, piezoelectric actuators in a quiescent fluid. Slender beam theory and the hydrodynamic function are used to capture the added fluid mass and viscous damping, assuming small displacement amplitudes. The model accurately replicates experimental SBTWs in quiescent water across a range of parameters and is able to predict the SBTW quality. It is shown that the quality is maximized (i.e. pure traveling wave/undulatory motion) when the actuators provide equal forcing to the structure. This can be achieved through judicious actuator placement and control of the relative voltage inputs. This study demonstrates that SBTWs can be generated in quiescent water and they can replicate the combined undulatory and oscillatory motions seen in BCF swimming. This represents a critical step towards implementing SBTWs for underwater propulsion, and future studies should evaluate the propulsive capabilities (i.e. thrust).

Assessment of Upper-Extremity Joint Angles Using Harmony Exoskeleton.

The biomechanical complexity of the human shoulder, while critical for functionality, poses a challenge for objective assessment during sensorimotor rehabilitation. With built-in sensing capabilities, robotic exoskeletons have the potential to serve as tools for both intervention and assessment. The bilateral upper-extremity Harmony exoskeleton is capable of full shoulder articulation, forearm flexion-extension, and wrist pronation-supination motions. The goal of this paper is to characterize Harmony's anatomical joint angle tracking accuracy towards its use as an assessment tool. We evaluated the agreement between anatomical joint angles estimated from the robot's sensor data and optical motion capture markers attached to the human user. In 9 healthy participants we examined 6 upper-extremity joint angles, including shoulder girdle angles, across 4 different motions, varying active/passive motion of the user and physical constraint of the trunk. We observed mostly good to excellent levels of agreement between measurement systems with for shoulder and distal joints, magnitudes of average discrepancies varying from 0.43° to 16.03° and width of LoAs ranging between 9.44° and 41.91°. Slopes were between 1.03 and 1.43 with r > 0.9 for shoulder and distal joints. Regression analysis suggested that discrepancies observed between measured robot and human motions were primarily due to relative motion associated with soft tissue deformation. The results suggest that the Harmony exoskeleton is capable of providing accurate measurements of arm and shoulder joint kinematics. These findings may lead to robot-assisted assessment and intervention of one of the most complex joint structures in the human body.

CFD Simulations and Experiments of a Submarine in Turn, Zigzag, and Surfacing Maneuvers

We present simulations and experiments of the generic submarine Joubert BB2 performing standard turn, zigzag, and surfacing maneuvers in calm water at depth. The free-sailing experiments, performed at Maritime Research Institute Netherlands (MARIN), are unique in that they present an open dataset for the community to benchmark maneuvering prediction methodologies. Computations were performed with explicitly gridded sailplanes, tail planes, and propellers using a dynamic overset technique. This study analyzes a 20-degree turning maneuver with vertical control commanding the stern planes and a 20/20 zigzag maneuver with vertical control commanding both sail and stern planes, both of them at a nominal speed of 10 knots, and a 20-degree rise maneuver with horizontal control at 12 knots. The results show that computational fluid dynamics can predict well motions and speeds for free-sailing conditions, but controller commands are harder to replicate. Computations of the rise maneuver with surfacing compare well with experiments, including a crashback maneuver to stop the submarine. Introduction Simulation of free-running submarine maneuvers is demanding in terms of computational fluid dynamics (CFD) capabilities and computational resources. Full 6-DoF motions, moving appendages, control algorithms, and scalable performance are some of the requirements to execute free-running maneuvering simulations. Open literature data for validation of submarine maneuvers are very limited and consist mostly of static conditions (see for instance rotating arm [Toxopeus et al. 2012] and static drift [Roddy 1990; Toxopeus 2008] data for Defense Advanced Research Projects Agency (DARPA) Suboff and static drift [Quick & Woodyatt 2014] for the Joubert hull form [Joubert 2006]). A new dataset has been recently made available to the research community for the generic submarine model BB2 (Overpelt et al. 2015), based on the Joubert hull form.

CAPTURING REAL-WORLD LOSSES OF BALANCE AND RECOVERY RESPONSES IN OLDER ADULTS AT RISK FOR FALLS

Losses of balance (LOBs) such as trips can lead to falls in older adults; what actually happens during real-world LOBs is unclear. With 4 wearable inertial measurement units (IMUs), we recorded feet, trunk and wrist movements over 2 weeks. Using a wrist voice recorder to report the LOBs, we applied our IMU processing algorithms and reconstructed the full body LOB and recovery motions. We recruited 7 at-risk older adults (M=76 yrs) who reported 114 LOBs of which we reconstructed over 90%. Using a rating system, 52% of the LOBs involved a significant trip, stumble, recovery step, and/or large trunk motion. 25% involved double or stutter steps and smaller trunk motions. The other 23% had less striking associated motions. These data suggest that most, but not all, self-reported real world LOBs involve substantial postural destabilization and near falls. Analyses of the voice-recorded context under which the LOBs occurred are ongoing.

Electrically conductive and fluorine free superhydrophobic strain sensors based on SiO2/graphene-decorated electrospun nanofibers for human motion monitoring

It is desirable and still challenging to develop flexible, breathable and anti-corrosive wearable strain sensors with high stretchability and sensitivity that can realize full range body motions. Here, a superhydrophobic and conductive nanofiber composites (SCNCs) with a hierarchical SiO2/graphene shell and polyurethane (PU) nanofiber core microstructure were fabricated by assembling graphene on PU nanofibers under the assistance of ultrasonication, followed by stretching-induced SiO2 nanoparticles decoration onto the graphene shell. The introduction of graphene and SiO2 nanoparticles improves both Young’s modulus, tensile strength and the elongation at break of PU nanofibrous membrane. The superhydrophobicity and conductivity can be almost maintained after the SCNCs are subject to cyclic stretching or abrasion or even exposed to harsh conditions. When used as strain sensors, the SCNCs show high stretchability, reliability and good durability and can be used in harsh environment including acid and salt conditions. The SCNCs are then assembled to monitor full range body motions including both subtle and large body movements, making it a promising candidate in wearable electronics.

A study of full space motions of outer Galactic disc A and F stars in two deep pencil beams

A and F stars can be used as probes of outer Galactic disk kinematics: here we extend the work of Harris et al. (2018) by crossmatching their A/F sample with Gaia DR2 to bring in proper motions. These are combined with the already measured radial velocities and spectro-photometric distances to obtain full space motions. We use this sample of 1173 stars, located in two pencil-beam sightlines (l=178deg and l=118deg), to sample the Galactocentric velocity field out to almost R_G=15 kpc. We find there are significant differences in all three (radial, azimuthal and vertical) kinematic components between the two directions. The rotation curve is roughly flat in the anticentre direction, confirming and extending the result of Kawata et al. (2018a) thanks to the greater reach of our spectro-photometric distance scale. However at l=118deg the circular velocity rises outwards from R_G=10.5 kpc and there is a more pronounced gradient in radial motion than is seen at l=178deg. Furthermore, the A star radial motion differs from the F stars by ~10 km/s. We discuss our findings in the context of perturbers potentially responsible for the trends, such as the central bar, spiral arms, the warp and external satellites. Our results at l=178deg are broadly consistent with previous work on K giants in the anticentre, but the kinematics at l=118deg in the Perseus region do not yet reconcile easily with bar or spiral arm perturbation.

Synthesis of kinematically constrained full-body motion from stochastic motion model

Humanoid robots are expected not only to understand human behaviors but also to perform human-like actions in order to be integrated into our daily lives. Learning by imitation is a powerful framework that can allow robots to generate the same motions that humans do. However, generation of motions by robots that are precisely the same as learned motions is not typically helpful in real environments, which are likely to be different from the environment where the motions were learned. For example, a robot may learn to reach for a glass on a table, but this motion cannot be used as is to reach for a cup on a cupboard shelf because the location of the cup is different from the location of the glass. Objects manipulated by robots and humans can be located in a variety of places. The robots therefore have to synthesize motions that depend on the current environment in order to reach a target object. Adaptive motion synthesis from memorized motions is an essential technique for allowing robots to perform human-like motions and accomplish motion tasks. This paper proposes a novel approach to synthesize full body motion by using both motions encoded as Hidden Markov Models and kinematic task constraints. We design an objective function that evaluates similarity between synthesized and memorized motions, satisfaction of the kinematic constraints, and smoothness of the generated motion. We develop an algorithm to find a motion trajectory that maximizes this objective function. The experiments demonstrate the utility of the proposed framework for the synthesis of full body motions by robots.

고속의 계층적 역기구학을 통한 휴머노이드 구조 로봇의 실시간 전신 동작 생성

A new hardware design for a rescue robot is proposed to enhance manipulation capability and mobility performance in an unstructured environment. The implementation of fast hierarchical inverse kinematics as a practical means of hard-real-time motion planning for a humanoid rescue robot is also considered. To achieve online full body dexterous manipulation performance, a hierarchical task priority is established among the motion tasks (or primitives) to precisely resolve task conflicts. Full body inverse kinematic solutions are derived in the recursive form by exploiting the previously calculated result of the higher priority task. This allows the current level step solution to be obtained for the lower priority motion task, enhancing computational efficiency. The least-squares optimization formulation provides theoretical and practical solutions in a unified and consistent manner. Realistic numerical simulations to generate complex full body motions in hard real-time demonstrate the effectiveness and performance of the design. Inverse kinematic solutions are found within 0.2 msec and 0.4 msec (on average) for 13 and 19 degrees of freedom control, respectively.

Gaussian process for 6-DoF rigid motions

Data-driven modeling approaches receive significant attention in robotics as they are capable of representing system behavior to which first-order principles cannot be employed. Modeling of human motions, based on observations is one of the many application areas. So far, however, the available probabilistic approaches cannot handle dynamics evolving in the space of rigid motions, as rotations are not appropriately considered. In this article, we present a mathematical framework for Gaussian process modeling, where the valid input domain is generalized to full rigid motions, namely the special Euclidean group SE(3). The kernel functions inside the Gaussian process are modified to exploit properties of the input data representation by dual quaternions. We further prove that the presented covariance functions maintain the Gaussian process properties. The correctness and accuracy of our approach is validated on simulated and real human motion data. We analyze the estimation performance of the novel Gaussian process framework in comparison to state of the art techniques, and show significantly improved model behavior of rigid motions.