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The Fast Fourier Transform is an essential algorithm of modern computational science. The highly parallel structure of the FFT allows for its efficient implementation on graphics processing units (GPUs), which are now widely used for general-purpose computing. This paper presents the VkFFT - an efficient GPU-accelerated multidimensional Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal projects. VkFFT aims to provide the community with a cross-platform open-source alternative to vendor-specific solutions while achieving comparable or better performance. This paper also discusses the optimizations implemented in VkFFT and verifies its performance and precision on modern high-performance computing GPUs. VkFFT is released under an MIT license.

Background: Hip and knee flexion joint motions are frequently examined in clinical practice using camera based motion capture (CBMC) systems; however, these systems require elaborate setups and dedicated space. Inertial measurement unit (IMU) based systems avoid these disadvantages but require validation before widespread adoption. Moreover, it is important for clinical practice to determine the stability of these systems for prolonged evaluation periods. The purpose of this study was to assess the validity of a three-sensor inertial measurement unit system for calculating hip and knee flexion angles during gait by comparing with a gold standard CBMC system. Validity was also examined before and after a treadmill walking session. Methods: Twenty healthy participants were tested. Twenty seconds of gait at preferred walking speed were analyzed before and after thirty-two minutes of treadmill walking using previously validated CBMC methods and with a custom IMU model. Measurement validity for the IMU system was evaluated using Bland & Altman 95 percent limits of agreement, linear regression, mean absolute error and root mean square error. The effects of a measurement zeroing calibration strategy were also investigated. Results: Strong measurement agreement was observed for both hip and knee flexion angles, although overall agreement for the hip exceeded that for the knee. Linear regressions between the datasets for each participant illustrated strong (> 0.94) relationships between IMU and CBMC measurements. More significant changes between timepoints were observed for the knee than for the hip. Error values were generally reduced when zeroing calibration was implemented. Conclusion: The IMU system presented in this study is a convenient and accessible technique to measure joint angles. The protocol described in the current study can be easily applied in the clinical setting for evaluation of clinical populations. Additional development work on sensor placement and calibration methods may further increase the accuracy of such methods. Clinical translation statement: The IMU system presented in this study is a convenient and accessible technique to measure joint angles. Additional developmentwork on sensor placement and calibration methods may further increase the accuracy of such methods.

Understanding the distinct functions of human muscles could not only help professionals obtain insights into the underlying mechanisms that we accommodate compromised neuromuscular system, but also assist engineers in developing rehabilitation devices. This study aims to determine the contribution of major muscle and the energy flow in the human musculoskeletal system at four sub-phases (collision, rebound, preload, push-off) during the stance of walking at different speeds. Gait experiments were performed with three self-selected speeds: slow, normal, and fast. Muscle forces and mechanical work were calculated by using a subject-specified musculoskeletal model. The functions of individual muscles were characterized as four functional behaviors (strut, spring, motor, damper), which were determined based on the mechanical energy. The results showed that during collision, hip flexors (iliacus and psoas major) and ankle dorsiflexors (anterior tibialis) were the most dominant muscles in buffering the stride with energy absorption; during rebound, the posterior muscles (gluteus maximus, gastrocnemius, posterior tibialis, soleus) contributed the most to energy generation; during preload, energy for preparing push-off was mainly absorbed by the muscles surrounding knee (vastus, semimembranosus, semitendinosus); during push-off, ankle plantar flexors (gastrocnemius, soleus, posterior tibialis, peroneus muscles, flexor digitorum, flexor hallucis) mainly behaved to generate energy for forward propulsion. With increased walking speed, additional energy (almost 400%) from harder stride was mainly absorbed by the flexor muscles. Hip extensors and adductors transferred more energy (around 150%) to the distal segments during rebound. Soleus and gastrocnemius muscles generated more energy (about 75%) to the proximal segments for propulsion. Along with our previous study of joint-level energy analysis, these findings could assist better understanding of human musculoskeletal behaviors during locomotion and provide principles for the bio-design of related assistive devices from motors performance enhancement to rehabilitation such as exoskeleton and prosthesis. [ABSTRACT FROM AUTHOR]

Robotic gait training may improve overground ambulation for individuals with poor control over pelvic motion. However, there is a need for an overground gait training robotic device that allows full control of pelvic movement and synchronizes applied forces to the user’s gait. This work evaluates an overground robotic gait trainer that applies synchronized forces on the user’s pelvis, the mobile Tethered Pelvic Assist Device. To illustrate one possible control scheme, we apply assistive frontal plane pelvic moments synchronized with the user’s continuous gait in real-time. Ten healthy adults walked with the robotic device, with and without frontal plane moments. The frontal plane moments corresponded to 10% of the user’s body weight with a moment arm of half their pelvic width. The frontal plane moments significantly increased the range of frontal plane pelvic angles from 2.6° to 9.9° and the sagittal and transverse planes from 4.6° to 10.1° and 3.0° to 8.3°, respectively. The frontal plane moments also significantly increased the activation of the left gluteus medius muscle, which assists in regulating pelvic obliquity. The right gluteus medius muscle activation did not significantly differ when frontal plane moments were applied. This work highlights the ability of the mobile Tethered Pelvic Assist Device to apply a continuous pelvic moment that is synchronized with the user’s gait cycle. This capability could change how overground robotic gait training strategies are designed and applied. The potential for gait training interventions that target gait deficits or muscle weakness can now be explored with the mobile Tethered Pelvic Assist Device. [ABSTRACT FROM AUTHOR]

Medical and rehabilitation exoskeletons are being increasingly considered by therapists when choosing a treatment for individuals affected by lower limb impairments. Although all such exoskeletons seem to provide similar features and performance, there are, in practice, significant differences among them in terms of maximum walking speed, maximum torque, weight, autonomy, interaction with the user, or even the way to use it. In this review, the state of the art of the main commercial exoskeletons is described, while analyzing their properties, advantages, and disadvantages. Three groups are considered: complete exoskeletons, partial exoskeletons and open lines of research. A comparative analysis between them is performed while considering the main scientific and technical aspects to be improved. In conclusion to this analysis, the balance between feasibility and innovation in exoskeletons development is a design challenge. Commercial exoskeletons must fulfil standards whilst ensuring their safety and robustness. However, achieving a new generation of exoskeletons means a need to implement new hardware paradigms, and to enhance control strategies focused on assist-as-needed scheme. Finally, some aspects to improve current designs of the exoskeleton are presented. [ABSTRACT FROM AUTHOR]

Autonomous lower-limb exoskeletons must modulate assistance based on locomotion mode (e.g., ramp or stair ascent) to adapt to the corresponding changes in human biological joint dynamics. However, current mode classification strategies for exoskeletons often require user-specific tuning, have a slow update rate, and rely on additional sensors outside of the exoskeleton sensor suite. In this study, we introduce a deep convolutional neural network-based locomotion mode classifier for hip exoskeleton applications using an open-source gait biomechanics dataset with various wearable sensors. Our approach removed the limitations of previous systems as it is 1) subject-independent (i.e., no user-specific data), 2) capable of continuously classifying for smooth and seamless mode transitions, and 3) only utilizes minimal wearable sensors native to a conventional hip exoskeleton. We optimized our model, based on several important factors contributing to overall performance, such as transition label timing, model architecture, and sensor placement, which provides a holistic understanding of mode classifier design. Our optimized DL model showed a 3.13% classification error (steady-state: 0.80 $\pm$ 0.38% and transitional: 6.49 $\pm$ 1.42%), outperforming other machine learning-based benchmarks commonly practiced in the field (p<0.05). Furthermore, our multi-modal analysis indicated that our model can maintain high performance in different settings such as unseen slopes on stairs or ramps. Thus, our study presents a novel locomotion mode framework, capable of advancing robotic exoskeleton applications toward assisting community ambulation. [ABSTRACT FROM AUTHOR]

In this article, we present a personal mobility device for lower-body impaired users through a lightweight exoskeleton on wheels. On its core, a novel passive exoskeleton provides postural transition leveraging natural body postures with support to the trunk on sit-to-stand/stand-to-sit (STS) transitions by a single gas spring as an energy storage unit. We propose a direction-dependent coupling of knees and hip joints through a double-pulley wire system, transferring energy from the torso motion toward balancing the moment load at the knee joint actuator. Herewith, the exoskeleton maximizes energy transfer and the naturalness of the user’s movement. We introduce an embodied user interface for hands-free navigation through a torso pressure sensing with minimal trunk rotations, resulting on average $\text{19}^{\circ } \pm \text{13}^{\circ }$ on six unimpaired users. We evaluate the design for STS assistance on 11 unimpaired users observing motions and muscle activity during the transitions. Results comparing assisted and unassisted STS transitions validate a significant reduction (up to 68%, $p< \text{0.01}$) at the involved muscle groups. Moreover, we show its feasibility through natural torso leaning movements of $+\text{12}^{\circ }\pm \text{6.5}^{\circ }$ and $- \text{13.7}^{\circ } \pm \text{6.1}^{\circ }$ for standing and sitting, respectively. Passive postural transition assistance warrants further work on increasing its applicability and broadening the user population. [ABSTRACT FROM AUTHOR]

Objective: Development of walking assist exoskeletons is a growing area of study, offering a solution to restore, maintain, and enhance mobility. However, applying this technology to the elderly is challenging and there is currently no consensus as to the optimal strategy for assisting elderly gait. The gait patterns of elderly individuals often differ from those of the younger population, primarily in the ankle and hip joints. This study used musculoskeletal simulations to predict how ankle and hip actuators might affect the energy expended by elderly participants during gait. Methods: OpenSim was used to generate simulations of 10 elderly participants walking at self-selected slow, comfortable, and fast speeds. Ideal flexion/extension assistive actuators were added bilaterally to the ankle or hip joints of the models to predict the maximum metabolic power that could be saved by exoskeletons that apply torques at these joints. Results: Compared to the unassisted scenario, the use of ideal hip actuators resulted in 21±5%, 26±5%, and 30±6% reductions in average metabolic power consumption at slow, comfortable, and fast walking speeds, respectively; use of ideal ankle actuators resulted in 12±3%, 14±2%, and 16±1% metabolic savings, respectively. Conclusion: The simulations suggest that providing hip assistance to elderly individuals during walking can result in significantly greater metabolic savings than ankle assistance, assuming kinematics and total joint moments do not change substantially with assistance. Significance: The achieved research results and analysis provide exoskeleton developers guidance on optimally designing walking assist exoskeletons, thus promoting consensus toward the optimal strategy for assisting elderly individuals. [ABSTRACT FROM AUTHOR]

This article presents a method to design a nonholonomic virtual constraint (NHVC) controller that produces multiple distinct stance-phase trajectories for corresponding walking speeds. NHVCs encode velocity-dependent joint trajectories via momenta conjugate to the unactuated degree(s) of freedom (DOFs) of the system. We recently introduced a method for designing NHVCs that allow for stable bipedal robotic walking across variable terrain slopes. This work extends the notion of NHVCs for application to variable-cadence powered prostheses. Using the segmental conjugate momentum for the prosthesis, an optimization problem is used to design a single stance-phase NHVC for three distinct walking speed trajectories (slow, normal, and fast). This stance-phase controller is implemented with a holonomic swing phase controller on a powered knee–ankle prosthesis, and experiments are conducted with an non-disabled user walking in steady and nonsteady velocity conditions. The control scheme is capable of representing: 1) multiple, task-dependent reference trajectories and 2) walking gait variance due to both temporal and kinematic changes in user motion. [ABSTRACT FROM AUTHOR]

Robust locomotion is a challenging task for humanoid robots, especially when considering dynamic disturbances. This article proposes a disturbance observer-based cascaded model predictive control (MPC) approach for bipedal locomotion, with the capability of exploiting ankle, stepping, hip and height variation strategies. Specifically, based on the variable-height inverted pendulum model, a nonlinear MPC that is run at a low frequency is built for 3-D locomotion (i.e., with height variation) while accounting for the footstep modulation as well. Differing from previous works, the nonlinear MPC is formulated as a convex optimization problem by semidefinite relaxation. Subsequently, assuming a flywheel at the pelvis center, a linear MPC that is run at a high frequency is proposed to regulate angular momentum (e.g., through rotating the upper body), which is solved by convex quadratic programming. To run the cascaded MPC in a closed-loop manner, a high order sliding mode observer is designed to estimate system states and dynamic disturbances simultaneously. Simulation and hardware experiments demonstrate the walking robustness in real-world scenarios, including 3-D walking with varying speeds, walking across non-coplanar terrains and push recovery. [ABSTRACT FROM AUTHOR]

Vacuum electron beam welding (EBW) is selected as the welding of the water-cooled ceramic breeder (WCCB) blanket. In the process of welding the first wall (FW) of WCCB blanket, it is necessary to add a backing plate to the arc section. At this stage, the thickness of reduced activation ferrite/martensite (RAFM) steels is up to 31 mm. In this article, thick plate RAFM steels after hot isostatic pressing (HIP) are welded by EBW, and then different heat treatments are performed. The results showed that, in the welding condition, hardness of welded joints was changed slightly. The highest hardness of the joint is 440 HV while the lowest is 290 HV, and a softening zone was observed in the heat-affected zone (HAZ). Moreover, after different heat treatments, the welding seam changed into the tempered martensite. In addition, the hardness of base metal and welded joints is all reduced, and the performance of welded joints in quenched and tempered conditions is better than that in normalized and tempered conditions. [ABSTRACT FROM AUTHOR]

Gait asymmetry is an important clinical characteristic of the hemiplegic gait most stroke survivors suffered, leading to restricted functional mobility and long-term negative impact on their quality of life. In recent years, robot assistance has been proven able to improve stroke patients’ functional walking, but few studies have been conducted to specifically correct gait asymmetry of stroke patients during the whole gait cycle. In this work, an adaptive oscillator-based assistive torque control was developed and implemented on a unilateral hip exoskeleton driven by a novel nonlinear series elastic actuator (nSEA), aiming at correcting gait asymmetry at hip joints during the whole gait cycle. The adaptive oscillator-based gait asymmetry detection method extracted continuous gait phase and gait asymmetry seamlessly, and then the proposed assistive control attempted to correct gait asymmetry by delivering precise assistive torques synchronized with the continuous gait phase of the patients’ gait. An initial experimental study was conducted to evaluate the proposed assistive control on seven healthy subjects with artificial impairment. The participants walked on a treadmill with assistance from the hip exoskeleton, while artificial impairment was added to mimic the hemiplegic gait with both spacial and temporal asymmetry (such as reduced hip flexion in the impaired side and reduced hip extension in the healthy side). Experimental results suggested the effectiveness of the proposed assistive control in restoring gait symmetry to levels comparable to a normal gait of the participants ($p < 0.05$). [ABSTRACT FROM AUTHOR]

Hip-assisted soft exosuits have been reported for their effect on reducing the metabolic power of human walking. Currently, most studies focus on uni-directional assistance (HF: hip flexion, or HE: hip extension). This paper investigates the effect of bi-directional assistance (HFE: hip flexion and extension) on the biomechanics and physiology of the wearer for understanding the potential benefits in synergistic effect of multi-muscle assistance. A belt-type soft exosuit was developed to provide the bi-directional assistance for augmenting walking. The assistance strategy was presented by imitating the contraction mechanisms of hip flexor and extensor muscles in walking. Tests on 8 healthy subjects were conducted and the results were compared with traditional uni-directional assistance. Metabolic powers, muscle activities, kinematics, and kinetics were measured and analyzed. Results showed that HFE assistance reduced the metabolic power by 7% and 12.4% relative to no exosuit and assistance turned off, respectively, larger than the sum of HF and HE. Furthermore, HFE reduced more total joint positive biological work and the activity of more related muscles, and at the same time increased upward and downward accelerations of body mass center, promoting walking energy exchange. These findings demonstrate that bi-directional assistance by combining hip flexion and extension has a significant synergistic effect on augmenting human walking as well as a benefit of increasing biological efficiency. [ABSTRACT FROM AUTHOR]

Healthy ageing modifies neuromuscular control of human overground walking. Previous studies found that ageing changes gait biomechanics, but whether there is concurrent ageing-related modulation of neuromuscular control remains unclear. We analyzed gait kinematics and electromyographic signals (EMGs; 14 lower-limb and trunk muscles) collected at three speeds during overground walking in 11 healthy young adults (mean age of 23.4 years) and 11 healthy elderlies (67.2 years). Neuromuscular control was characterized by extracting muscle synergies from EMGs and the synergies of both groups were ${k}$ -means-clustered. The synergies of the two groups were grossly similar, but we observed numerous cluster- and muscle-specific differences between the age groups. At the population level, some hip-motion-related synergy clusters were more frequently identified in elderlies while others, more frequent in young adults. Such differences in synergy prevalence between the age groups are consistent with the finding that elderlies had a larger hip flexion range. For the synergies shared between both groups, the elderlies had higher inter-subject variability of the temporal activations than young adults. To further explore what synergy characteristics may be related to this inter-subject variability, we found that the inter-subject variance of temporal activations correlated negatively with the sparseness of the synergies in elderlies but not young adults during slow walking. Overall, our results suggest that as humans age, not only are the muscle synergies for walking fine-tuned in structure, but their temporal activation patterns are also more heterogeneous across individuals, possibly reflecting individual differences in prior sensorimotor experience or ageing-related changes in limb neuro-musculoskeletal properties. [ABSTRACT FROM AUTHOR]

Reaching and maintaining high walking speeds is challenging for a human when carrying extra weight, such as walking with a heavy backpack. Robotic limbs can support a heavy backpack when standing still, but accelerating a backpack within a couple of steps to race-walking speeds requires limb force and energy beyond natural human ability. Here, we conceive a human-driven robot exoskeleton that could accelerate a heavy backpack faster and maintain top speeds higher than what the human alone can when not carrying a backpack. The key components of the exoskeleton are the mechanically adaptive but energetically passive spring limbs. We show that by optimally adapting the stiffness of the limbs, the robot can achieve near-horizontal center of mass motion to emulate the load-bearing mechanics of the bicycle. We find that such an exoskeleton could enable the human to accelerate one extra body weight up to top race-walking speeds in ten steps. Our finding predicts that human-driven mechanically adaptive robot exoskeletons could extend human weight-bearing and fast-walking ability without using external energy. [ABSTRACT FROM AUTHOR]

An ultra-robust accurate gait phase estimator is developed by training a time-delay neural network (D67) on data collected from the hip and knee joint angles of 14 participants during treadmill and overground walking. Collected data include normal gait at speeds ranging from 0.1m/s to 1.9m/s and conditions such as long stride, short stride, asymmetric walking, stop-start, and abrupt speed changes. Spatial analysis of our method indicates an average RMSE of 1.74±0.23% and 2.35±0.52% in gait phase estimation of test participants in the treadmill and overground walking, respectively. The temporal analysis reveals that D67 detects heel-strike events with an average MAE of 1.70±0.54% and 2.74±0.92% of step duration on test participants in the treadmill and overground walking, respectively. Both spatial and temporal performances are uniform across participants and gait conditions. Further analyses indicate the robustness of the D67 to smooth and abrupt speed changes, limping, variation of stride length, and sudden start or stop of walking. The performance of the D67 is also compared to the state-of-the-art techniques confirming the superior and comparable performance of the D67 to techniques without and with a ground contact sensor, respectively. The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton. [ABSTRACT FROM AUTHOR]

For exoskeletons to be successful in real-world settings, they will need to be effective across a variety of terrains, including on inclines. While some single-joint exoskeletons have assisted incline walking, recent successes in level-ground assistance suggest that greater improvements may be possible by optimizing assistance of the whole leg. To understand how exoskeleton assistance should change with incline, we used human-in-the-loop optimization to find whole-leg exoskeleton assistance torques that minimized metabolic cost on a range of grades. We optimized assistance for three non-disabled, expert participants on 5 degree, 10 degree, and 15 degree inclines using a hip-knee-ankle exoskeleton emulator. For all assisted conditions, the cost of transport was reduced by at least 50% relative to walking in the device with no assistance, which is a large improvement to walking comparable to the benefits of whole-leg assistance on level-ground (N = 3). Optimized extension torque magnitudes and exoskeleton power increased with incline. Hip extension, knee extension and ankle plantarflexion often grew as large as allowed by comfort-based limits. Applied powers on steep inclines were double the powers applied during level-ground walking, indicating that greater exoskeleton power may be optimal in scenarios where biological powers and costs are higher. Future exoskeleton devices could deliver large improvements in walking performance across a range of inclines if they have sufficient torque and power capabilities. [ABSTRACT FROM AUTHOR]

To prevent lower back pain (LBP) in the industrial workplace, various powered back support exoskeletons (BSEs) have been developed. However, conventional kinematics-triggered assistance (KA) strategies induce latency, degrading assistance efficiency. Therefore, we proposed and experimentally evaluated a surface electromyography (sEMG)-triggered assistance (EA) strategy. Nine healthy subjects participated in the lifting experiments: 1) external loads test, 2) extra latency test, and 3) repetitive lifting test. In the external loads test, subject performed lifting with four different external loads (0 kg, 7.5 kg, 15 kg, and 22.5 kg). The assistance was triggered earlier by EA compared to KA from 114 ms to 202 ms, 163 ms to 269 ms for squat and stoop lifting respectively, as external loads increased from 0 kg to 22.5 kg. In the extra latency test, the effects of extra latency (manual switch, 0 ms, 100 ms and 200 ms) in EA on muscle activities were investigated. Muscle activities were minimized in the fast assistance (0 ms and 100 ms) condition and increased with extra latency. In the repetitive lifting test, the EA strategy significantly reduced L1 muscle fatigue by 70.4% in stoop lifting, compared to KA strategy. Based on the experimental results, we concluded that fast assistance triggered by sEMG improved assistance efficiency in BSE and was particularly beneficial in heavy external loads situations. The proposed assistive strategy can be used to prevent LBP by reducing back muscle fatigue and is easily applicable to various industrial exoskeleton applications. [ABSTRACT FROM AUTHOR]

Most exoskeletons are designed to reduce the metabolic costs of performing aerobic tasks such as walking, running, and hopping. This study presents an exoskeleton that boosts vertical jumping—a fast, short movement during which the muscles are exerted at peak capacity. It was hypothesized that a passive exoskeleton would increase vertical jump height without requiring external energy input. The device comprises springs that work in parallel with the muscles of the quadriceps femoris. The springs store mechanical energy during knee flexion (the negative work phase) and release that energy during the subsequent knee extension (the positive work phase), augmenting the muscles. Ten healthy participants were evaluated in two experimental sessions. In the first session, the participants jumped without receiving instructions on how to use the exoskeleton, and the results showed no difference in jump height when jumping with the exoskeleton or jumping without it. In the second session, the participants were instructed to achieve deeper initial squat heights at the start of the jump. This resulted in a 6.4% increase in average jump height compared to jumping without the exoskeleton (each participant performed five jumps for each the two conditions). This is the first time that a passive exoskeleton has been shown to improve the height of a vertical jump from a dead stop. [ABSTRACT FROM AUTHOR]

Motor developmental delay (MDD) usually affects the inter-joint coordination for limb movement. However, the mechanism between the abnormal inter-joint coordination and MDD is still unclear, which poses a challenge for clinical diagnosis and motor rehabilitation of MDD in infant’s early life. This study aimed to explore whether the joint activities of limbs during infant crawling are represented with kinematic synergies of joint angles, and evaluate the impacts of MDD on the inter-joint coordination using those synergies. 20 typically developing infants, 16 infants at risk of developmental delay, 11 infants at high risk of developmental delay and 13 infants with confirmed developmental delay were recruited for self-paced crawling on hands and knees. A motion capture system was employed to trace infants’ limbs in space, and angles of shoulder, elbow, hip and knee over time were computed. Kinematic synergies were derived from joint angles using principal component analysis. Sample entropy and Spearman’s rank correlation coefficients were calculated among those synergies to evaluate the crawling complexity and the symmetry of bilateral limbs, respectively. We found that the first two synergies with different contributions to the crawling movements sufficiently represented the joint angular profiles of limbs. MDD further delayed the development of motor function for lower limbs and mainly increased the crawling complexity of joint flexion/extension to some extent, but did not obviously change the symmetry of bilateral limbs. These results suggest that the time-varying kinematic synergy of joint angles is a potential index for objectively evaluating the abnormal inter-joint coordination affected by MDD. [ABSTRACT FROM AUTHOR]

Trajectory planning of the knee joint plays an essential role in controlling the lower limb prosthesis. Nowadays, the idea of mapping the trajectory of the healthy limb to the motion trajectory of the prosthetic joint has begun to emerge. However, establishing a simple and intuitive coordination mapping is still challenging. This paper employs the method of experimental data mining to explore such a coordination mapping. The coordination indexes, i.e., the mean absolute relative phase (MARP) and the deviation phase (DP), are obtained from experimental data. Statistical results covering different subjects indicate that the hip motion possesses a stable phase difference with the knee, inspiring us to construct a hip-knee Motion-Lagged Coordination Mapping (MLCM). The MLCM first introduces a time lag to the hip motion to avoid conventional integral or differential calculations. The model in polynomials, which is proved more efficient than Gaussian process regression and neural network learning, is then constructed to represent the mapping from the lagged hip motion to the knee motion. In addition, a strong linear correlation between hip-knee MARP and hip-knee motion lag is discovered for the first time. By using the MLCM, one can generate the knee trajectory for the prosthesis control only via the hip motion of the healthy limb, indicating less sensing and better robustness. Numerical simulations show that the prosthesis can achieve normal gaits at different walking speeds. [ABSTRACT FROM AUTHOR]

Knowledge on joint impedance during walking in various conditions is relevant for clinical decision-making and the development of robotic gait trainers, leg prostheses, leg orthotics and wearable exoskeletons. Whereas ankle impedance during walking has been experimentally assessed, knee and hip joint impedance during walking have not been identified yet. Here we developed and evaluated a lower limb perturbator to identify hip, knee and ankle joint impedance during treadmill walking. The lower limb perturbator (LOPER) consists of an actuator connected to the thigh via rods. The LOPER allows to apply force perturbations to a free-hanging leg, while standing on the contralateral leg, with a bandwidth of up to 39 Hz. While walking in minimal impedance mode, the interaction forces between LOPER and the thigh were low (<5N) and the effect on the walking pattern was smaller than the within-subject variability during normal walking. Using a non-linear multibody dynamical model of swing leg dynamics, the hip, knee and ankle joint impedance were estimated at three time points during the swing phase for nine subjects walking at a speed of 0.5 m/s. The identified model was well able to predict the experimental responses for the hip and knee, since the mean variance accounted (VAF) for was 99% and 96%, respectively. The ankle lacked a consistent response and the mean VAF of the model fit was only 77%, and therefore the estimated ankle impedance was not reliable. The averaged across-subjects stiffness varied between the three time points within 34–66 and 0–3.5 Nm/rad Nm/rad for the hip and knee joint respectively. The damping varied between 1.9–4.6 and 0.02–0.14 Nms/rad Nms/rad for hip and knee respectively. The developed LOPER has a negligible effect on the unperturbed walking pattern and allows to identify hip and knee impedance during the swing phase. [ABSTRACT FROM AUTHOR]

Gait impairment represented by crouch gait is the main cause of decreases in the quality of lives of children with cerebral palsy. Various robotic rehabilitation interventions have been used to improve gait abnormalities in the sagittal plane of children with cerebral palsy, such as excessive flexion in the hip and knee joints, yet in few studies have postural improvements in the coronal plane been observed. The aim of this study was to design and validate a gait rehabilitation system using a new cable-driven mechanism applying assist in the coronal plane. We developed a mobile cable-tensioning platform that can control the magnitude and direction of the tension vector applied at the knee joints during treadmill walking, while minimizing the inertia of the worn part of the device for less obstructing the natural movement of the lower limbs. To validate the effectiveness of the proposed system, three different treadmill walking conditions were performed by four children with cerebral palsy. The experimental results showed that the system reduced hip adduction angle by an average of $4.57~\pm ~1.79^{\circ }$ compared to unassisted walking. Importantly, we also observed improvements of hip joint kinematics in the sagittal plane, indicating that crouch gait can be improved by postural correction in the coronal plane. The device also improved anterior and lateral pelvic tilts during treadmill walking. The proposed cable-tensioning platform can be used as a rehabilitation system for crouch gait, and more specifically, for correcting gait posture with minimal disturbance to the voluntary movement. [ABSTRACT FROM AUTHOR]

Little is known about the effect of pulsed electromagnetic fields (PEMFs) as an option for preventing osteoporosis. This study sought to investigate the effectiveness of PEMFs for the management of primary osteoporosis in older adults. We searched databases from the inception to date to target trials examining the effects of PEMFs compared to placebo or sham or other agents for the management of primary osteoporosis for a meta-analysis using random effects model. Eight trials including 411 participants were included. PEMFs was non-inferior to conventional pharmacological agents and exercise respectively in preventing the decline of Bone Mineral Density (BMD) at the lumbar (MD 8.76; CI −9.64 to 27.16 and MD 1.33; CI −2.73 to 5.39) and femur neck (MD 0.04; CI −1.09 to 1.16 and MD 1.50; CI −0.26 to 3.26), and significantly improving balance function measured by Berg Balance Scale (BBS) (MD 0.91; CI 0.32 to 1.49) and Timed Up and Go test (MD −3.61; CI −6.37 to −0.85), directly after intervention. The similar trends were observed in BMD and BBS at 12- and 24-weeks follow-up from baseline. PEMFs had positive effects non-inferior to first-line treatment on BMD and better over placebo on balance function in older adults with primary osteoporosis, but with moderate to very low certainty evidence and short-term follow-ups. There is a need for high-quality randomised controlled trials evaluating PEMFs for the management of primary osteoporosis. [ABSTRACT FROM AUTHOR]

Powered exoskeletons for people with complete paraplegia have been controlled based on predefined joint-reference trajectories. As the target users of such robots may not realize any voluntary movement, the human body is fully constrained and follows the movement of the powered exoskeleton joints. The predefined gait pattern, however, may or may not be adequate for every user because the gait pattern is resulting from complex interactions between the body segments and environment, as well as dynamic characteristics of the body segments. As all the persons and their body segments have different dynamic characteristics, therefore, a bespoke tuning of gait parameters is necessary in order to realize the natural gait motion, which is optimal for each user. In this article, an adaptive gait pattern adjustment method is proposed. The proposed method observes the ground contact timing, which is directly related to the adequacy of the gait pattern for the user wearing a robot. Based on the ground contact timing, the joint-reference trajectories are adjusted, which are parameterized by the trunk inclination angle. The proposed method iteratively calculates an appropriate trunk inclination angle from the information of ground contact timing. In this article, the derivation of the proposed method and its experimental verification with WalkON Suit, a powered exoskeleton, are introduced. The proposed algorithm successfully worked for two practical users with complete paraplegia, and the adapted gait patterns showed excessive performance in walking speed, oxygen consumption, palm force on crutches, etc. The results were also verified by winning both gold and bronze medals in the global competition, Cybathlon 2020, while accomplishing the best records among all the teams. [ABSTRACT FROM AUTHOR]

For a lower limb exoskeleton, accurate and continuous estimation of the gait phase in real time is a fundamental requirement to provide a well-tailored assistive action at the proper time in the gait cycle (GC). This article presents a new gait phase estimator for a portable hip exoskeleton based on discrete wavelet transform (DWT) and adaptive oscillators. The algorithm is capable of continuously tracking the gait phase and identifying the relevant biomechanical gait events online, i.e., heel strike (HS) and toe-off (TO). The proposed method exploits only the hip joint angle signals measured by hip encoders, avoiding the need to use additional sensors to those already built in the exoskeleton. The novel phase estimator has been benchmarked against a state-of-the-art method, based on the maximum flexion angle (MFA), with pressure-sensitive insoles used as the reference ground truth for the event detection. To validate the method, two experimental activities were carried out. Experiments conducted with eight healthy subjects walking on a treadmill at different speeds, with and without hip assistance, demonstrated that the DWT-based method outperformed the MFA method in all operative conditions, reducing the rms of the phase reset error by 64.0% in assistive mode, and identifying the HS and TO events with low delay (0.4% and 1.1% GC, respectively, for HS and TO). Experiments carried out with three transfemoral amputees showed similar performance, paving the way for clinical applications of the method. [ABSTRACT FROM AUTHOR]

Wearable robots are undergoing a disruptive transition, from the rigid machines that populated the science-fiction world in the early 1980s to lightweight robotic apparel, hardly distinguishable from our daily clothes. In less than a decade of development, soft robotic suits have achieved important results in human motor assistance and augmentation. In this article, we start by giving a definition of soft robotic suits and proposing a taxonomy to classify existing systems. We then critically review the modes of actuation, the physical human–robot interface and the intention-detection strategies of state-of-the-art soft robotic suits, highlighting the advantages and limitations of different approaches. Finally, we discuss the impact of this new technology on human movements, for both augmenting human function and supporting motor impairments, and identify areas that are in need of further development. [ABSTRACT FROM AUTHOR]

Powered intelligent lower limb prostheses have been gaining interest as they provide functionality for walking on different terrains. This study proposes a hierarchical planner for intelligent lower limb prostheses based on sensor fusion and a central pattern generator (CPG). Electromyographic (EMG) and inertial measurement unit (IMU) signals were recorded and fused in the feature and decision levels. The high-level planner consists of cascade classifiers with a gait phase dependence. A secondary classifier for each stand phase and swing phase was developed to recognize five gait patterns. The mid-level planner was designed as a walking frequency estimator based on the Newton method. The low-level planner incorporates the CPG models, achieving the planning of lower limb joint trajectories. The proposed layered planner can recognize the users’ walking intention and gait speed in real-time, ensuring coordination between the joints of both legs. Eight healthy subjects were recruited, and the average accuracy of motion pattern recognition reached 99.13% and 99.39% for the standing and swing phases, respectively. The relative root mean square error (RMSE) of the walking frequency estimate was 2.3% under approximately 3.5 m/s gait speed. The promising results indicate that this method effectively predicts the continuous joint angle for lower limb prostheses. [ABSTRACT FROM AUTHOR]

Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers. [ABSTRACT FROM AUTHOR]

The generation of motor adaptation in response to mechanical perturbation during human walking is seldom considered in an exoskeleton system. Reshaping step trajectory over consecutive gait cycles for a walking exoskeleton is investigated in this article. Step adjustment of a walking exoskeleton can adapt to human walking intention by shaping step trajectory. This work develops an admittance adaptive fuzzy control strategy for a walking exoskeleton robot to provide assistance for human lower limb movement. Considering human walking intention and utilizing an admittance model, it shapes a reference trajectory to ensure that the walking exoskeleton follows it according to the human–robot force produced by its wearer. Considering a nonlinear and dynamic model with uncertainties, this work designs an integral-type Lyapunov function controller to track a reference trajectory. A disturbance observer is integrated into the controller design to compensate for uncertain disturbance in order to achieve an effective tracking performance. Finally, this work conducts experiments on two healthy subjects with the proposed method on a walking exoskeleton to validate its effectiveness. The results show that it can be applied to walking exoskeletons to enhance human mobility. [ABSTRACT FROM AUTHOR]

This article presents a graph convolutional network model (GCNM) for gait phase classification to control a lower limb exoskeleton. The GCNM can recognize four leg phases between the foot and ground, including heel strike, foot flat, heel off, and swing. The proposed model can solve the gait phase classification problem from non-Euclidean domain based on a graph mechanism for exoskeletons. Real-time performance of the gait data acquisition system was then verified on a lower limb exoskeleton. The system quantifies the time delay with an optoelectronic system, VICON BONITA 10. Comparison works with some existing state-of-the-art methods from the Euclidean domain, i.e., long short-term memory (LSTM) and deep convolutional neural network (DCNN), were also provided. Experimental works show that the proposed model has a significantly higher prediction accuracy and better robustness in gait phase classification for different people in level, uphill, and downhill environments. Moreover, the label rate is under ${\rm {10}}{\rm {\% }}$ , compared to LSTM and DCNN, which are usually at ${\rm {70}}$ – ${\rm {80}}{\rm {\% }}$. Finally, the GCNM's maximum accuracy of gait phase classification is ${\rm {97}}{\rm {.43\% }}$ ; thus, the effectiveness of the proposed model is verified. [ABSTRACT FROM AUTHOR]

Legged robots can move on various grounds via walking; however, they have difficulty in moving fast. To overcome this limitation, some legged robots have been studying dynamic motions such as jumping and running. In this article, we present a strategy for generating an optimal jumping motion for legged robots in real time. For convenience, we divided the proposed jumping trajectory into vertical and horizontal directions. The vertical motion trajectory ensures a continuous center of mass position, speed, and acceleration, as well as minimizes the maximum torque and maximum speed of the joints; we generated it via a nonlinear optimal process. Besides, for the horizontal motion, we proposed a novel model predictive control using a height varying inverted pendulum model. In the proposed method, the zero moment point is placed in the support polygon for stable jumping; also, the robot does not slip on the ground and takes off at a desired velocity. We verified the jumping motion using RoK-3, a biped robot developed for the experiment. [ABSTRACT FROM AUTHOR]

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement. [ABSTRACT FROM AUTHOR]

This article presents a strategy for autonomous locomotion trajectory planning for high-level control of lower limb exoskeletons by defining a novel set of adaptive central pattern generators (ACPGs) to facilitate safe and compliant interaction with the human. A time-varying bounded-gain adaptive disturbance observer is designed for estimating the human–robot interaction (HRI) needed for online central pattern generator (CPG)-based trajectory shaping and low-level nonlinear trajectory tracking control. The proposed ACPG dynamics for each exoskeleton joint updates the motion frequency and amplitude based on the observed HRI torque, which is also coupled with adjacent joints’ CPGs to regulate their phase differences in real time. An integrated Lyapunov analysis is conducted to ensure the closed-loop system’s stability and uniformly ultimately boundedness of both the tracking error and the torque estimation error in the controlled exoskeleton. Experimental studies are performed with an able-bodied human wearer by applying arbitrary torques on the exoskeleton’s joints in order to evaluate the proposed autonomous control strategy in online adjustment and personalization of the locomotion. [ABSTRACT FROM AUTHOR]

To tackle the increasingly larger training data and models, researchers and engineers resort to multiple servers in a data center for distributed machine learning (DML). On one hand, DML enables us to leverage the computation power of multiple servers, which can effectively accelerate those computation-intensive tasks. On the other hand, DML also incurs significant communication cost due to parameter synchronization among these servers. In this paper, we want to explore the impact of synchronization topology, including both logical topology and physical topology, on the DML performance. First, we revisit the existing logical topologies, e.g., parameter server and ring allreduce, for parameter synchronization, and we find that these flat synchronization topologies is inefficient when running a large-scale DML training. Therefore, we propose a hierarchical parameter synchronization topology, called HiPS, which can achieve efficient parameter synchronization even on a large scale. Then, we compare two representative physical network topologies, namely, Fat-Tree and BCube. Based on our analyses, BCube has many advantages over Fat-Tree, e.g., higher bandwidth, better load balance, and lower hardware cost. The simulation results also show that BCube is more friendly to RDMA. Relying on the advantages of HiPS and BCube, the GST of “HiPS+BCube” is 12% ~ 70% lower than other combinations. Moreover, when the cluster size increases from 16 to 1024, the performance of “HiPS+BCube” only drops by 6.5%, while the performance of “Ring+BCube” drops by 44.6%. Hence, we believe “HiPS+BCube” is the optimal solution to benefit DML in large scale. [ABSTRACT FROM AUTHOR]

Objective: This study determined the contribution of the deep hip muscles to hip stability. Methods: Hip stability was defined as rotational hip stiffness in the sagittal plane, which was calculated for walking trials for 12 participants via an electromyography (EMG)–informed neuromusculoskeletal model which included all 22 hip spanning muscles. Three model configurations which differed in deep hip muscle excitations but had identical excitations for all other muscles were compared: (1) deep hip muscles informed by intramuscular EMG measurements (assisted activation); (2) deep hip muscles with simulated zero activation (no activation); (3) deep hip muscles with simulated maximal activation (maximal activation). Sagittal plane rotational hip stiffness across the gait cycle was compared between the three model configurations using a within-participant analysis of variance via statistical parametric mapping (p < 0.05). Results: Compared to the assisted activation configuration, hip stiffness (mean (95% confidence interval)) was 0.8% (0.7 to 0.9) lower in the no activation configuration, and 3.2% (3.0 to 3.4) higher in the maximal activation configuration. Conclusion: Regardless of activation level, deep hip muscles contributed little to sagittal plane rotational hip stiffness, which casts doubt on their assumed function as hip stabilizers. Significance: The merit of targeted deep hip muscle strengthening to improve hip stability in rehabilitation programs remains unclear. [ABSTRACT FROM AUTHOR]

This document proposes a control scheme applied to delayed bilateral-by-phases teleoperation of the locomotion and manipulation of a humanoid robot, where each phase works depending on the decision of the human operator. Besides, balance control, adaptive controller, and force feedback are applied. The implementation is described and the results obtained from experiments of intercontinental teleoperation for a pick and place task with a NAO robot-driven at distance with a haptic device, are shown and summarized. [ABSTRACT FROM AUTHOR]

The B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) has shown its effectiveness for developmental dysplasia of the hip (DDH) in infants. In this work, a two-stage meta-learning based deep exclusivity regularized machine (TML-DERM) is proposed for the BUS-based CAD of DDH. TML-DERM integrates deep neural network (DNN) and exclusivity regularized machine into a unified framework to simultaneously improve the feature representation and classification performance. Moreover, the first-stage meta-learning is mainly conducted on the DNN module to alleviate the overfitting issue caused by the significantly increased parameters in DNN, and a random sampling strategy is adopted to self-generate the meta-tasks; while the second-stage meta-learning mainly learns the combination of multiple weak classifiers by a weight vector to improve the classification performance, and also optimizes the unified framework again. The experimental results on a DDH ultrasound dataset show the proposed TML-DERM algorithm achieves the superior classification performance with the mean accuracy of 85.89%, sensitivity of 86.54%, and specificity of 85.23%. [ABSTRACT FROM AUTHOR]

The ultrasound (US) screening of the infant hip is vital for the early diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH refers to measuring alpha and beta angles that quantify hip joint development. These two angles are calculated from key anatomical landmarks and structures of the hip. However, this measurement process is not trivial for sonographers and usually requires a thorough understanding of complex anatomical structures. In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH. Our multi-task networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as the basic framework to detect and segment key anatomical structures and add one landmark detection branch to form a new multi-task framework. Secondly, we propose a novel shape similarity loss to refine the incomplete anatomical structure prediction robustly and accurately. Thirdly, we further incorporate the landmark-structure consistent prior to ensure the consistency of the bony rim estimated from the segmented structure and the detected landmark. In our experiments, 1231 US images of the infant hip from 632 patients are collected, of which 247 images from 126 patients are tested. The average errors in alpha and beta angles are 2.221 ${}^{\circ }$ and 2.899 ${}^{\circ }$. About 93% and 85% estimates of alpha and beta angles have errors less than 5 degrees, respectively. Experimental results demonstrate that the proposed method can accurately and robustly realize the automatic evaluation of DDH, showing great potential for clinical application. [ABSTRACT FROM AUTHOR]

Dynamic control of robotic exoskeletons is paramount to ensuring safe, synergistic assistive action of functional benefit to users. To date, exoskeleton controllers have excelled in rhythmic and quasi-rhythmic tasks, whereas control methods for assisting discrete movements remain limited by their task-specificity. Inspired by neurophysiological dynamic movement primitives (DMPs), we formulated a novel controller that facilitated a variety of lifting movements using a single adaptive DMP (aDMP), for wearable robotic assistance of discrete movements. For a variety of load lifting tasks, we first benchmarked our method's trajectory prediction accuracy against the state-of-the-art DMP using passively recorded exoskeleton sensor data (offline), followed by a functional validation of online aDMP trajectory estimates. Finally, we assessed the functional effects of aDMP-based exoskeletal assistance on joint kinematics and muscular activity during repetitive lifting. The new aDMP method accurately predicted and smoothly synchronized robotic assistance with variable movement trajectories, resulting in reduced muscular activation of the erector spinae muscles (up to 47.6%) while preserving lower-limb joint kinematics and reducing the extension time by 15.5% compared to unassisted conditions. This method holds promise for use in a wide range of wearable robotic applications, including both clinical rehabilitation and user assistance in activities of daily living and/or manual labor. [ABSTRACT FROM AUTHOR]

Developmental dysplasia of the hip (DDH) is a common and serious disease in infants. Hip landmark detection plays a critical role in diagnosing the development of neonatal hip in the ultrasound image. However, the local confusion and the regional weakening make this task challenging. To solve these challenges, we explore the stable hip structure and the distinguishable local features to provide dependencies for hip landmark detection. In this paper, we propose a novel architecture named Dependency Mining ResNet (DM-ResNet), which investigates end-to-end dependency mining for more accurate and much faster hip landmark detection. First of all, we convert the landmark detection to the heatmap estimation by ResNet to build a strong baseline architecture for fast and accurate detection. Secondly, a dependency mining module is explored to mine the dependencies and leverage both the local and global information to decline the local confusion and strengthen the weakening region. Thirdly, we propose a simple but effective local voting algorithm (LVA) that seeks trade-off between long-range and short-range dependencies in the hip ultrasound image. Besides, a dataset with 2000 annotated hip ultrasound images is constructed in our work. It is the first public hip ultrasound dataset for open research. Experimental results show that our method achieves excellent precision in hip landmark detection (average point error of 0.719mm and successful detection rate within 1mm of 79.9%). [ABSTRACT FROM AUTHOR]

Ultrasound is a widely used technology for diagnosing developmental dysplasia of the hip (DDH) because it does not use radiation. Due to its low cost and convenience, 2-D ultrasound is still the most common examination in DDH diagnosis. In clinical usage, the complexity of both ultrasound image standardization and measurement leads to a high error rate for sonographers. The automatic segmentation results of key structures in the hip joint can be used to develop a standard plane detection method that helps sonographers decrease the error rate. However, current automatic segmentation methods still face challenges in robustness and accuracy. Thus, we propose a neonatal hip bone segmentation network (NHBS-Net) for the first time for the segmentation of seven key structures. We design three improvements, an enhanced dual attention module, a two-class feature fusion module, and a coordinate convolution output head, to help segment different structures. Compared with current state-of-the-art networks, NHBS-Net gains outstanding performance accuracy and generalizability, as shown in the experiments. Additionally, image standardization is a common need in ultrasonography. The ability of segmentation-based standard plane detection is tested on a 50-image standard dataset. The experiments show that our method can help healthcare workers decrease their error rate from 6%-10% to 2%. In addition, the segmentation performance in another ultrasound dataset (fetal heart) demonstrates the ability of our network. [ABSTRACT FROM AUTHOR]

The reasonable selection of sensors is essential for sensor fusion for gait recognition, and this paper proposes a reduction method based on an improved greedy algorithm, which can screen a suitable sensor combination with strong distinguishing ability. This novel reduces the multi-sensor system containing 17 sensors by optimizing the neighborhood rough set model, and uses grey relational analysis (GRA) for post-processing to get the optimal reduction. In addition, these reductions under three different terrains are restructured and extended to form the optimal combination. In order to verify the feasibility of the algorithm, we detect the gait phases and compare the accuracy. Results reveal that the number of sensors is reduced from 17 to 8, and the accuracies of three different terrains are increased by 1.05%, 2.633% and 5.934% respectively compared with previous sensors, and increased by 0.533%, 5.950% and 3.834% respectively compared with sensors using principal component analysis (PCA). The Wilcoxon rank sum test is carried out and the results show the algorithm has good performance. The experiments show that this method can screen a few sensors while maintaining or improving the classification ability, and it has high engineering practical significance in wearable robotics field and many other sensor fields. [ABSTRACT FROM AUTHOR]

Computing has changed science and engineering since the introduction of the first general-purpose scientific computer in 1946, the Electronic Numerical Integrator and Computer (ENIAC), produced by the University of Pennsylvania for the Army. The ENIAC was created for studying the science of ballistics and for the development, testing, and fielding of Army ordnance. Since that time computing has provided access to larger parameter spaces for design, more cases analyzed in a given unit of time or budget, and access to phenomena not measurable or to conditions not accessible to experimentation. Science and engineering continue to change computing, creating demand for ever larger and faster platforms, illuminating new phenomena that lead to new materials, components, fabrication techniques, architectures, and entirely new types of computing. We describe the Department of Defense High Performance Computing Modernization Program, its impact on problem solving, and on our primary customer, the Warfighter. [ABSTRACT FROM AUTHOR]

Action recognition is a critical task for social robots to meaningfully engage with their environment. 3-D human skeleton-based action recognition has been an attractive research area in recent years. Although the existing approaches are good at action recognition, it is a great challenge to recognize a group of actions in an activity scene. To tackle this problem, at first, we partition the scene into several primitive actions (PAs)-based upon motion attention mechanism. Then, the PAs are described by the trajectory vectors of the corresponding joints. After that, motivated by text classification based on word embedding, we employ a convolutional neural network (CNN) to recognize activity scenes by considering motion of joints as “word” of activity. The experimental results on the dataset of human activity scenes show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Exoskeletons are increasingly used to assist humans in military, industry, and healthcare applications, thereby enabling individuals to gain increased strength and endurance. This article proposes a novel human-in-the-loop control framework for a fully actuated lower limb exoskeleton with high degree-of-freedoms (DoFs), allowing users to walk without crutches or other external stabilization tools. To imitate the natural lower limb motion of users, a novel barrier energy function is utilized for the design of the control strategy, where the human-robot manipulation space is reformulated as a human-voluntary and a robot-constrained region. The variations in the barrier energy function are based on the distance between the center of mass and zero moment point of the walking exoskeleton, thereby constraining the lower limb motion of the user to a compliant region around various desired trajectories. Based on varying regional functions, the proposed strategy is designed to control the exoskeleton to follow appropriate ergonomic trajectories. For such a purpose, an adaptive controller is exploited considering the functions of the human effort and the robot's capabilities simultaneously, and a smooth motion transition can be achieved between the human and robot regions. Finally, physical experiments are conducted on a ten-DoFs walking exoskeleton to validate the stability and robustness of the proposed control framework with subjects performing flat walking, turning, and obstacle avoidance movements. [ABSTRACT FROM AUTHOR]

Paralysis of the trunk results in seated instability leading to difficulties performing activities of daily living. Functional neuromuscular stimulation (FNS) combined with control systems have the potential to restore some dynamic functions of the trunk. However, design of multi-joint, multi-muscle control systems requires characterization of the stimulation-driven muscles responsible for movement. Objective: This study characterizes the input-output properties of paralyzed trunk muscles activated by FNS, and explores co-activation of muscles. Methods: Four participants with various spinal cord injuries (C7 AIS-B, T4 AIS-B, T5 AIS-A, C5 AIS-C) were constrained so lumbar forces were transmitted to a load cell while an implanted neuroprosthesis activated otherwise paralyzed hip and paraspinal muscles. Isometric force recruitment curves in the nominal seated position were generated by inputting the level of stimulation (pulse width modulation) while measuring the resulting muscle force. Two participants returned for a second experiment where muscles were co-activated to determine if their actions combined linearly. Results: Recruitment curves of most trunk and hip muscles fit sigmoid shaped curves with a regression coefficient above 0.75, and co-activation of the muscles combined linearly across the hip and lumbar joint. Subject specific perturbation plots showed one subject is capable of resisting up to a 300N perturbation anteriorly and 125N laterally; with some subjects falling considerably below these values. Conclusion: Development of a trunk stability control system can use sigmoid recruitment dynamics and assume muscle forces combine linearly. Significance: This study informs future designs of multi-muscle, and multi-dimensional FNS systems to maintain seated posture and stability. [ABSTRACT FROM AUTHOR]

An exoskeleton needs reference joint angle profiles at various speeds and slopes for its application in real-world scenarios. Recording these profiles and their implementation in the control system of an exoskeleton are time-consuming and complex processes. Therefore, there is a need for an artificial system that can predict subject-specific joint angle profiles for various real-world scenarios from a minimum amount of input data. This study aims to propose a predictive neural network (NN) called MoveNet, for joints angle profile prediction across variable walking speeds and slopes. MoveNet consists of three NN modules, namely, encoder, mapper, and decoder. The encoder module is trained to convert the knee joint angle profile into its 6-D latent representation. The mapper module is trained to map the latent representation of angle profile at zero degrees angle of inclination (AOI) to latent representation of angle profile at the target AOI. The decoder module is trained to predict an angle profile from its given latent representation. The proposed model successfully predicted knee joint angle profiles at three walking speeds (0.8, 1, and 1.2 m/s) and nine AOI ranging from −10° to 10°. MoveNet obtained root mean squared error of 3.24 ± 1.19° and mean absolute error of 2.66 ± 1.00° from tenfold cross validation. These results suggest that artificial intelligence can predict subject-specific knee joint angle profiles for variable slopes at a given walking speed from the data of knee joint angle profiles recorded at a flat surface. [ABSTRACT FROM AUTHOR]

Streak artifacts in kilovoltage computed tomography (kVCT), originated from metallic implants, are reduced by megavoltage CT (MVCT). This study aimed to establish the image value-to-density table (IVDT) of MVCT and validate dose accuracy. The IVDT of MVCT was established with different physical densities (0.290–19.427 g/cm3) and compared with kVCT for doses. The Acuros external beam (AXB) algorithm was compared with the analytical anisotropic algorithm (AAA) for heterogeneity correction in MVCT. Based on the experimental results, the IVDT of MVCT showed good linearity. Dose overestimations for the metallic hip prosthesis in a water phantom were reduced from 2.6%/2.8% in kVCT_AAA, 1.8%/−0.7% in MVCT_AAA and to −0.7%/−0.3% in MVCT_AXB with 6-MV/10-MV photons, respectively. The predictive formulas were validated and highly correlated with the measured doses ($R^{2}= 0.9933$ /0.9862 for 6-/10-MV photons, respectively). On average, the dose differences with a metallic eye shield on the Alderson radiation therapy phantom using 6-MV photon were reduced from 3.6% in kVCT_AAA to −1% in MVCT_AAA. In patient studies, the reductions of dose overestimations were from 28.6% and 7.4% to −3.9% with dental fillings, from 13.6% and −9% to −9.1% with the unilateral hip prosthesis, and from −25.3% and −5.9% to −4.3% with bilateral hip prostheses in kVCT_AAA, MVCT_AAA, and MVCT_AXB, respectively. In conclusion, MVCT with the AXB algorithm reduces metal artifacts on kVCT and plans accurate doses. [ABSTRACT FROM AUTHOR]

The efficiency of the electrostatic separation of granular plastics wastes depends on the electric charge acquired by the particles in appropriate tribocharging devices. The objective of the present study was the development and functional optimization of new tribocharger design consisting of an assembly of six cylinders, made of polyvinyl chloride (PVC), acryl-methyl butadiene styrene (ABS), or polycarbonate (PC) that can rotate at variable speed and be inclined at a certain angle with respect to the horizontal plane. The performances of the triboelectric charger were evaluated by measuring the charge per mass ratio of the granules collected at the outlet of the device. A first set of experiments involved eight granular polymers, size 2 to 6 mm, originating from waste electric and electronic equipment. The polymers could be arranged in a triboelectric series with respect to the amount of charge acquired in contact to the PVC, ABS, and PC cylinders. A second set of experiments was carried out with polystyrene granules and enabled the choice of the limits of variation of the two factors that were expected to affect the tribocharging: the inclination of the six-PVC-cylinder assembly and its rotation speed. Finally, design of experiments methodology was used for modeling and optimization of the process. The best results were obtained for high rotation speeds and low inclination angles. [ABSTRACT FROM AUTHOR]