Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This article introduces gnssFGO, a global and online trajectory estimator that fuses global navigation satellite systems (GNSS) observations alongside multiple sensor measurements for robust vehicle localization. In gnssFGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process (GP) regression. This enables querying states at arbitrary timestamps without strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multisensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, inertial measurement unit, and LiDAR-odometry. We employed datasets from measurement campaigns in Aachen, Düsseldorf, and Cologne and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas where a classic multisensor fusion method fails due to sensor degradation. In a test sequence containing a 17-km route through Aachen, the proposed method results in a mean 2-D positioning error 0.48 m while fusing raw GNSS observations with LiDAR odometry in a tight coupling.

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Traditional wheelchairs are pushed from behind the occupant, which hinders eye contact and communication. It was proposed that the wheelchair be pushed from the side using a push bar to place the caregiver beside the occupant. However, this method requires the caregiver to exert continuous effort to maintain a straight trajectory because the force at the lateral push location generates a moment. This study explores simple modifications to the front castor wheel of the wheelchair that allow pushing it from the side without additional effort. We used a three-dimensional dynamic model of the castor wheel to predict the effects of altering its dimensions and rake, cant, and bank angle and present a simplified steady-state solution. Experimental results support the model’s predictions, and a proof-of-concept experiment with a wheelchair showed that it is possible to push a wheelchair from the side without increasing forces or moments. The results also confirmed that the lateral ground reaction force generated on the castor wheel is proportional to the normal force and the cant angle, which can counter the moment caused by the lateral push location. The implications of this model extend beyond wheelchair design and can be applied to other applications that use castor wheels, such as robotics, trolleys, and walkers.@en

Angular momentum, kinetics, and energetics, including total mechanical energy and its rate of change in relation to power exchange, are important quantities when analyzing human motion in sports, physical labor, and rehabilitation. Inertial measurement units (IMU)-based motion capture (MOCAP) systems provide a portable solution for the ambulatory analysis of these quantities which optical MOCAP systems do not offer. Yet, evaluating IMU-based estimates of these quantities by referencing optical systems is limited by the fact that these systems only measure positions, not kinetic and energetic quantities. To evaluate the accuracy of an IMU-based method for estimating kinetic and energetic quantities without using any external reference, firstly, we propose an estimation method only using angular velocity and acceleration signals supplied by an IMU, and apply this to a single rigid body with known mass and inertia. Then, we propose a novel experimental validation method against physical conservation and action/reaction laws that apply during ballistic movements, using a suitably designed and reconfigurable rigid body with a structure of three orthogonal dumb-bells. The results demonstrated that we could estimate the angular momentum, kinetics, and energetics of a rigid body by only using angular velocity and acceleration signals of an IMU, and the estimation accuracy was well evaluated by the proposed validation method. However, the results showed that the errors in original IMU measurements under dynamic conditions especially concerning angular velocity, uncertainties in calculating rigid body parameters, and vibration propagation due to limited rigidity of tubes of the rigid body influenced the estimation accuracy.

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People fall more often when their gait stability is reduced. Gait stability can be directly manipulated by exerting forces or moments onto a person, ranging from simple walking sticks to complex wearable robotics. A systematic review of the literature was performed to determine: What is the level of evidence for different types of mechanical manipulations on improving gait stability? The study was registered at PROSPERO (CRD42020180631). Databases Embase, Medline All, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar were searched. The final search was conducted on the 1st of December, 2022. The included studies contained mechanical devices that influence gait stability for both impaired and non-impaired subjects. Studies performed with prosthetic devices, passive orthoses, and analysing post-training effects were excluded. An adapted NIH quality assessment tool was used to assess the study quality and risk of bias. Studies were grouped based on the type of device, point of application, and direction of forces and moments. For each device type, a best-evidence synthesis was performed to quantify the level of evidence based on the type of validity of the reported outcome measures and the study quality assessment score. Impaired and non-impaired study participants were considered separately. From a total of 4701 papers, 53 were included in our analysis. For impaired subjects, indicative evidence was found for medio-lateral pelvis stabilisation for improving gait stability, while limited evidence was found for hip joint assistance and canes. For non-impaired subjects, moderate evidence was found for medio-lateral pelvis stabilisation and limited evidence for body weight support. For all other device types, either indicative or insufficient evidence was found for improving gait stability. Our findings also highlight the lack of consensus on outcome measures amongst studies of devices focused on manipulating gait.

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Background: Pneumatic actuators are widely used in applications like (medical) robots, or prostheses. Pneumatic actuators require a complex manufacturing process and are produced in standardized dimensions to reduce costs. Over the last decade 3D-printing has emerged as a cost-effective and efficient production method in medical applications. 3D-printing can also function as a cost-efficient alternative production method for pneumatic actuators. Objective: The goal of this research is to study the possibility of creating a pneumatic linear actuator with 3D-printing. Furthermore, the aim is to use the advantage of 3D-printing to create pneumatic actuators with non-circular cross-sections. Methodology: To evaluate the performance of a 3D-printed pneumatic actuator, a test setup was designed and built to measure the leakage and sliding friction force. Furthermore, two pneumatic actuators with a non-conventional cross-sectional shape were designed and their performance was tested and compared with a 3D-printed cylindrical pneumatic actuator, since these tests only ran once, the results are more a guideline. During the manufacturing of the cylinders, no post-processing techniques were used. Results: The functioning of a 3D-printed circular pneumatic actuator was proven with low static leakage rates of 2.5%, low dynamic leakage rates of approximately 1%, and a maximum friction force of [Formula presented]. Furthermore, the results show that it is possible to print functioning pneumatic cylinders with a non-cylindrical concave cross-section. The non-conventional cylinders were tested up to [Formula presented] with maximum dynamic leakage of [Formula presented]. Conclusion: This study demonstrates a method to create functional pneumatic linear actuators with 3D-printing. It was possible to create 3D-printed actuators with a conventional shape, e.g. circular and unconventional shapes e.g. stadium/oval shape and a kidney shape. The leak rates for conventional and unconventional shapes were in the same range. This opens up the world for more design freedom in pneumatic actuators.

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Falling is a major cause of morbidity, and is often caused by a decrease in postural stability. A key component of postural stability is whole-body centroidal angular momentum, which can be influenced by control moment gyroscopes. In this proof-of-concept study, we explore the influence of our wearable robotic gyroscopic actuator “GyroPack” on the balance performance and gait characteristics of non-impaired individuals (seven female/eight male, 30 ± 7 years, 68.8 ± 8.4 kg). Participants performed a series of balance and walking tasks with and without wearing the GyroPack. The device displayed various control modes, which were hypothesised to positively, negatively, or neutrally impact postural control. When configured as a damper, the GyroPack increased mediolateral standing time and walking distance, on a balance beam, and decreased trunk angular velocity variability, while walking on a treadmill. When configured as a negative damper, both peak trunk angular rate and trunk angular velocity variability increased during treadmill walking. This exploratory study shows that gyroscopic actuators can influence balance and gait kinematics. Our results mirror the findings of our earlier studies; though, with more than 50% mass reduction of the device, practical and clinical applicability now appears within reach.@en

Balance recovery after tripping often requires an active adaptation of foot placement. Thus far, few attempts have been made to actively assist forward foot placement for balance recovery employing wearable devices. This study aims to explore the possibilities of active forward foot placement through two paradigms of actuation: assistive moments exerted with the reaction moments either internal or external to the human body, namely 'joint' moments and 'free' moments, respectively. Both paradigms can be applied to manipulate the motion of segments of the body (e.g., the shank or thigh), but joint actuators also exert opposing reaction moments on neighbouring body segments, altering posture and potentially inhibiting tripping recovery. We therefore hypothesised that a free moment paradigm is more effective in assisting balance recovery following tripping. The simulation software SCONE was used to simulate gait and tripping over various ground-fixed obstacles during the early swing phase. To aid forward foot placement, joint moments and free moments were applied either on the thigh to augment hip flexion or on the shank to augment knee extension. Two realizations of joint moments on the hip were simulated, with the reaction moment applied to either the pelvis or the contralateral thigh. The simulation results show that assisting hip flexion with either actuation paradigm on the thigh can result in full recovery of gait with a margin of stability and leg kinematics closely matching the unperturbed case. However, when assisting knee extension with moments on the shank, free moment effectively assist balance but joint moments with the reaction moment on the thigh do not. For joint moments assisting hip flexion, placement of the reaction moment on the contralateral thigh was more effective in achieving the desired limb dynamics than placing the reaction on the pelvis. Poor choice of placement of reaction moments may therefore have detrimental consequences for balance recovery, and removing them entirely (i.e., free moment) could be a more effective and reliable alternative. These results challenge conventional assumptions and may inform the design and development of a new generation of minimalistic wearable devices to promote balance during gait.

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Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.

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Standing up using one leg is a challenging task for those with a transfemoral amputation, particularly for elderly users with a low activity level. Active prostheses are generally not accessible to this group and available passive prostheses do not support standing up. This article presents the design and evaluation of the “Energy Restoring Intelligent Knee” (ERiK), which stores energy during sit-down in a pneumatic cylinder and returns it during stand-up. We hypothesized that the system would reduce the time needed to perform transitions and also enable higher load sharing by the prosthetic leg. However, the results of an experimental study with seven participants with transfemoral amputation contradict these hypotheses: the participants could neither move faster nor make more use of the prosthetic leg to share their body weight during transitions. We observed that a major obstacle to the useful functionality of the leg was the absence of ankle dorsiflexion – the foot tended to slip during stand-up initiation, such that only low pre-pressures and therefore support levels could be set. The rather binary action of the pneumatics also complicated movement initiation. The lessons learned from this study may be helpful to those seeking to create better designs in the future.@en

The global navigation satellite systems (GNSS) play a vital role in transport systems for accurate and consistent vehicle localization. However, GNSS observations can be distorted due to multipath effects and non-line-of-sight (NLOS) receptions in challenging environments such as urban canyons. In such cases, traditional methods to classify and exclude faulty GNSS observations may fail, leading to unreliable state estimation and unsafe system operations. This work proposes a deep-learning-based method to detect NLOS receptions and predict GNSS pseudorange errors by analyzing GNSS observations as a spatio-temporal modeling problem. Compared to previous works, we construct a transformer-like attention mechanism to enhance the long short-term memory (LSTM) networks, improving model performance and generalization. For the training and evaluation of the proposed network, we used labeled datasets from the cities of Hong Kong and Aachen. We also introduce a dataset generation process to label the GNSS observations using lidar maps. In experimental studies, we compare the proposed network with a deep-learning-based model and classical machine-learning models. Furthermore, we conduct ablation studies of our network components and integrate the NLOS detection with data out-of-distribution in a state estimator. As a result, our network presents improved precision and recall ratios compared to other models. Additionally, we show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.

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Climate-controlled cabins have for decades been standard in vehicles. Model Predictive Controllers (MPCs) have shown promising results in achieving temperature tracking in vehicle cabins and may improve upon model-free control performance. However, for the multi-zone climate control case, proper controller tuning is challenging, as externally, e.g., passenger-triggered changes in compressor setting and thus mass flow lead to degraded control performance. This paper presents a tuning method to automatically determine robust MPC parameters, as a function of the blower mass flow. Constrained contextual Bayesian optimization (BO) is used to derive policies minimizing a high-level cost function subject to constraints in a defined scenario. The proposed method leverages random disturbances and model-plant mismatch within the training episodes to generate controller parameters achieving robust disturbance rejection. The method contains a postprocessing step to achieve smooth policies that can be utilized in real-world applications. First, simulation results show that the mass flow-dependent policy outperforms a constant parametrization, while achieving the desired closed-loop behavior. Second, the robust tuning method greatly reduces worst-case overshoot and produces consistent closed-loop behavior under varying operating conditions.

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Trunk motor control is essential for the proper functioning of the upper extremities and is an important predictor of gait capacity in children with delayed development. Early diagnosis and intervention could increase the trunk motor capabilities in later life, but current tools used to assess the level of trunk motor control are largely subjective and many lack the sensitivity to accurately monitor development and the effects of therapy. Inertial measurement units could yield an objective quantitative assessment that is inexpensive and easy-to-implement. We hypothesized that root mean square of jerk, a proxy for movement smoothness, could be used to distinguish age and thereby presumed motor development. We attached a sensor to the trunks of six young children with no known developmental deficits. Root mean square of jerk decreases with age, up to 24 months, and is correlated to a more established method, i.e., center-of-pressure velocity, as well as other standard inertial measurement unit outputs. This metric therefore shows potential as a method to differentiate trunk motor control levels.

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Background: Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments. To address this gap, we investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population. Methods: We collected data from a wearable airbag’s inertial measurement units (IMUs) from individuals with (n = 20 stroke) and without (n = 15 control) history of stroke while performing a series of falls (842 falls total) and non-falls (961 non-falls total) in a laboratory setting. A leave-one-subject-out crossvalidation was used to compare the performance of two identical machine learned models (adaptive boosting classifier) trained on cohort-dependent data (control or stroke) to pre-detect falls in the stroke cohort. Results: The average performance of the model trained on stroke data (recall = 0.905, precision = 0.900) had statistically significantly better recall (P = 0.0035) than the model trained on control data (recall = 0.800, precision = 0.944), while precision was not statistically significantly different. Stratifying models trained on specific fall types revealed differences in pre-detecting anterior–posterior (AP) falls (stroke-trained model’s F₁-score was 35% higher, P = 0.019). Using activities of daily living as non-falls training data (compared to near-falls) significantly increased the AUC (Area under the receiver operating characteristic) for classifying AP falls for both models (P < 0.04). Preliminary analysis suggests that users with more severe stroke impairments benefit further from a stroke-trained model. The optimal lead time (time interval pre-impact to detect falls) differed between control- and stroke-trained models. Conclusions: These results demonstrate the importance of population sensitivity, non-falls data, and optimal lead time for machine learned pre-impact fall detection specific to stroke. Existing fall mitigation technologies should be challenged to include data of neurologically impaired individuals in model development to adequately detect falls in other high fall risk populations. Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT05076565; Unique Identifier: NCT05076565. Retrospectively registered on 13 October 2021

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During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation.

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Building upon recent advancements in linear electroadhesive clutch materials and performance, this paper examines the feasibility of a self-reinforcing electroadhesive rotational clutch using a simple model. The design aims to deliver improvements in applications where performance is limited by the torque-to-power and torque-to-mass ratios offered by conventional electromagnetic or magnetorheological clutches. The performance of the self-reinforcing design is related to the device's geometric parameters and hence the robustness of clutch configurations is examined by modeling the system parameters as having stochastic properties. A design example based on the clutch requirements of a gyroscopic balance assistance device is analyzed. The analysis predicts that substantial improvements in torque-to-power and torque-to-mass ratios are possible with the presented design compared to industry-leading rotational clutches.

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To affect functional relevant task-space gait parameters such as foot placement or progression angle, conventional lower-limb robotic gait rehabilitation devices require active control and synchronization of their actuators. As an alternative, we propose the use of gyroscopic actuators, portable actuators that have the ability to generate torques that are caused by and therefore intrinsically synchronized with the swing motion of the legs. Here we investigate the kinematic and kinetic effects at hip-joint level of self-induced gyroscopic torques of a shank-worn gyroscopic actuator. Preliminary results show the wearer’s swing leg motion can induce gyroscopic effects that significantly alter the kinematics of the hip-joint (p&lt; 0.05 ) for both tested conditions in hip-joint endo/exo rotation and ab/ad-duction.

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Inspired by phenomena in the plant world, a meteoro-sensitive rotational actuator is developed. The design uses a hygro-active shell, whose water-based swelling is restricted at selective locations to form a helicoid structure. The influence of geometrical parameters on the performance is investigated using a numerical analysis of various geometries, by looking at resulting rotation and torque during this rotation. Prototypes are built of five key geometries in the design space, to validate the simulations and to investigate the behaviour of the design experimentally. These prototypes are submerged in water to investigate their deformation, after which they are placed in a torsion machine to investigate the torque during rotation. The experiments result in similar rotations and torques as the simulations. The designed Hygromorphic Rotational Actuator is capable of passively rotating its own structure, thereby expanding the possibilities of engineers and designers when designing passive autonomous systems.

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One important aspect of gait stability is the control of whole-body centroidal angular momentum H. We recently showed that if sensory-motor impairments affect a person's balance control, control of H can be assisted by control moment gyroscopes (CMGs). However, the effect of CMG technology inherently depends on the size and weight of these actuators, and on the speed of the flywheels they contain. These factors all pose challenges for wearable applications. Here, we show that it is possible to design CMGs light enough for wearable applications, while generating meaningful output torques. Our CMG, weighing 1.187 kg, can exert a peak torque of 15 N m with a torque-tracking bandwidth of 18 Hz. These results are partly due to an integrated model of components and partly to advancements in flywheel velocity control, allowing the speed to safely reach 20 000 rpm. These actuators open up new pathways of building wearable assistive devices for clinical applications.

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Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution@en

Sensory-motor impairments due to age or neurological diseases can influence a person's ability to maintain balance, and increase the risk of falls. Recently, wearable Control Moment Gyroscopes (CMGs) have proven to provide effective balance support. Here, we show a new design of a Series-Elastic Control Moment Gyroscope (SECMG) enhanced by an additional passive degree of freedom, namely a second, orthogonal gimbal that is supported by a (visco)elastic element. The design mainly aims to reject disturbances originating from human movement and render a low remaining impedance, as well as to provide more accurate torque sensing, based on angular deflection of the compliant element. Evaluation of the torque tracking performance with regards to a classic rigid Single-Gimbal Control Moment Gyroscope (SGCMG) showed that the device equally exceeds the bandwidth requirements for its application in human augmentation. However, characterization of our current compliant construction also revealed some backlash occluding the torque-deflection relation. In the future, the SECMG could be evaluated in experiments with humans, to validate its predicted low remaining impedance.

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