Estimation Of Kinematics Research Articles

Assessment of Stability of MIMU Probes to Skin-Marker-Based Anatomical Reference Frames During Locomotion Tasks: Effect of Different Locations on the Lower Limb.

Soft tissue artefacts (STAs) undermine the validity of skin-mounted approaches to measure skeletal kinematics. Magneto-inertial measurement units (MIMU) gained popularity due to their low cost and ease of use. Although the reliability of different protocols for marker-based joint kinematics estimation has been widely reported, there are still no indications on where to place MIMU to minimize STA. This study aims to find the most stable positions for MIMU placement, among four positions on the thigh, four on the shank, and three on the foot. Stability was investigated by measuring MIMU movements against an anatomical reference frame, defined according to a standard marker-based approach. To this aim, markers were attached both on the case of each MIMU (technical frame) and on bony landmarks (anatomical frame). For each MIMU, the nine angles between each versor of the technical frame with each versor of the corresponding anatomical frame were computed. The maximum standard deviation of these angles was assumed as the instability index of MIMU-body coupling. Six healthy subjects were asked to perform barefoot gait, step negotiation, and sit-to-stand. Results showed that (1) in the thigh, the frontal position was the most stable in all tasks, especially in gait; (2) in the shank, the proximal position is the least stable, (3) lateral or medial calcaneus and foot dorsum positions showed equivalent stability performances. Further studies should be done before generalizing these conclusions to different motor tasks and MIMU-body fixation methods. The above results are of interest for both MIMU-based gait analysis and rehabilitation approaches using wearable sensors-based biofeedback.

Estimation of Lower Limb Kinematics during Squat Task in Different Loading Using sEMG Activity and Deep Recurrent Neural Networks.

The aim of the present study was to predict the kinematics of the knee and the ankle joints during a squat training task of different intensities. Lower limb surface electromyographic (sEMG) signals and the 3-D kinematics of lower extremity joints were recorded from 19 body builders during squat training at four loading conditions. A long-short term memory (LSTM) was used to estimate the kinematics of the knee and the ankle joints. The accuracy, in terms root-mean-square error (RMSE) metric, of the LSTM network for the knee and ankle joints were 6.774 ± 1.197 and 6.961 ± 1.200, respectively. The LSTM network with inputs processed by cross-correlation (CC) method showed 3.8% and 4.7% better performance in the knee and ankle joints, respectively, compared to when the CC method was not used. Our results showed that in the prediction, regardless of the intensity of movement and inter-subject variability, an off-the-shelf LSTM decoder outperforms conventional fully connected neural networks.

Facial Landmark Tracking in Videos of Individuals with Neurological Impairments: Is There a Trade-off Between Smoothness and Accuracyƒ.

Orofacial kinematics are valuable markers of function and progression in a variety of neurological disorders. Recent advances in facial landmark detection have been used to improve landmark tracking in video, for example by accounting for interframe optical flow. It has been demonstrated that finetuning (a type of transfer learning) can improve the performance of some facial landmark detection systems. Here, we asked whether a neural network model that is pretrained using video data (supervision by registration, SBR) can be finetuned to improve landmark detection and tracking, using data from the Toronto Neuroface Dataset (n=36), which comprises 3 different clinical populations. We finetuned the supervision by registration (SBR) model using data from 3 individuals from each of 3 clinical populations (n=9), with or without neurological impairments. The remaining individuals from our dataset (n=27) were used for evaluation. Finetuning SBR moderately improved the model's accuracy but substantially increased the smoothness of tracked landmarks. This suggests that finetuning on video-trained models, like SBR, could improve the estimation of orofacial kinematics in individuals with neurological impairments. This could be used to improve the detection and characterization of neurological diseases using video data.Clinical Relevance-This work demonstrated that transfer learning applied to video-trained facial landmark detectors could improve the measurement of orofacial kinematics in individuals with neurological impairments.

Estimation of Joint Angle From sEMG and Inertial Measurements Based on Deep Learning Approach.

Continuous kinematics estimation from surface electromyography (sEMG) allows more natural and intuitive human-machine collaboration. Recent research has suggested the use of multimodal inputs (sEMG signals and inertial measurements) to improve estimation performance. This work focused on assessing the use of angular velocity in combination with myoelectric signals to simultaneously and continuously predict 12 joint angles in the hand. Estimation performance was evaluated for five functional and grasping movements in 20 subjects. The proposed method is based on convolutional and recurrent neural networks using transfer learning (TL). A novel aspect was the use of a pretrained deep network model from basic joint hand movements to learn new patterns present in functional motions. A comparison was carried out with the traditional method based solely on sEMG. Although the performance of the algorithm slightly improved with the use of the multimodal combination, both strategies had similar behavior. The results indicated a significant improvement for a single task: opening a bottle with a tripod grasp.

SSVEP-gated EMG-based decoding of elbow angle during goal-directed reaching movement

IntroductionEstimation of reaching movement commands for controlling human–machine interfaces (HMIs) based on electromyogram (EMG) signals, such as prostheses or robotic assistive devices, for the disabled is challenging due to the absence of healthy and synergistic muscle activations. MethodsIn this study, to improve the kinematic estimation of an EMG-based decoder during goal-directed reaching movements, steady-state visual evoked potentials (SSVEP) were considered to identify the motion target. The EMG signal of the shoulder and arm muscles was mapped to the elbow angle with a model selected by SSVEP signals during the reaching movement to a flashing target on display. ResultsThe accuracy of decoding based on the combination of EMG and EEG was significantly different (RMSE = 3.7° and R2 = 92.13%) from the counterpart EMG-based decoder (RMSE = 4.26° and R2 = 91.02%). Indeed, the proposed dual-modality structure outperformed the single-modality structure when the EMG signals altered in permanent conditions, such as spinal cord injury or stroke. Especially in simulated muscle weakness situations where the signal-to-noise ratio declines, the dual-modality framework was more robust than the single-modality, and the determinant coefficient of the decoder was stable above 80%. ConclusionBecause of the additional EEG information to recognize the target of reaching, the performance of the EMG-based decoding improved and could be more robust during EMG signal alterations. The outcomes of this study can be applied to control HMIs or evaluate real-time rehabilitation trials, particularly for severe motor injury patients.

In-vitro validation of inertial-sensor-to-bone alignment

A major shortcoming in kinematic estimation using skin-attached inertial sensors is the alignment of sensor-embedded and segment-embedded coordinate systems. Only a correct alignment results in clinically relevant kinematics. Model-based inertial-sensor-to-bone alignment methods relate inertial sensor measurements with a model of the joint. Therefore, they do not rely on properly executed calibration movements or a correct sensor placement. However, it is unknown how accurate such model-based methods align the sensor axes and the underlying segment-embedded axes, as defined by clinical definitions. Also, validation of the alignment models is challenging, since an optical motion capture ground truth can be prone to disturbances from soft tissue movement, orientation estimation and manual palpation errors. We present an anatomical tibiofemoral ground truth on an unloaded cadaveric measurement set-up that intrinsically overcomes these disturbances. Additionally, we validate existing model-based alignment strategies. Modeling the degrees of freedom leads to the identification of rotation axes. However, there is no reason why these axes would align with the segment-embedded axes. Relative inertial-sensor orientation information and rich arbitrary movements showed to aid in identifying the underlying joint axes. The first dominant sagittal rotation axis aligned sufficiently well with the underlying segment-embedded reference. The estimated axes that relate to secondary kinematics tend to deviate from the underlying segment-embedded axes as much as their expected range of motion around the axes. In order to interpret the secondary kinematics, the alignment model should more closely match the biomechanics of the joint.

Continuous Multi-DoF Wrist Kinematics Estimation Based on a Human–Machine Interface With Electrical-Impedance-Tomography

This study proposed a multiple degree-of-freedom (DoF) continuous wrist angle estimation approach based on an electrical impedance tomography (EIT) interface. The interface can inspect the spatial information of deep muscles with a soft elastic fabric sensing band, extending the measurement scope of the existing muscle-signal-based sensors. The designed estimation algorithm first extracted the mutual correlation of the EIT regions with a kernel function, and second used a regularization procedure to select the optimal coefficients. We evaluated the method with different features and regression models on 12 healthy subjects when they performed six basic wrist joint motions. The average root-mean-square error of the 3-DoF estimation task was 7.62°, and the average R2 was 0.92. The results are comparable to state-of-the-art with sEMG signals in multi-DoF tasks. Future endeavors will be paid in this new direction to get more promising results.

Estimation and Control of Cooperative Aerial Manipulators for a Payload with an Arbitrary Center-of-Mass.

This paper presents an integrated framework that integrates the kinematic and dynamic parameter estimation of an irregular object with non-uniform mass distribution for cooperative aerial manipulators. Unlike existing approaches, including impedance-based control which requires expensive force/torque sensors or the first-order-momentum-based estimator which is weak to noise, this paper suggests a method without such sensor and strong to noise by exploiting the decentralized dynamics and sliding-mode-momentum observer. First, the kinematic estimator estimates the relative distances of multiple aerial manipulators by using translational and angular velocities between aerial robots. By exploiting the distance estimation, the desired trajectories for each aerial manipulator are set. Second, the dynamic parameter estimation is performed for the mass of the common object and the vector between the end-effector frame and the center of mass of the object. Finally, the proposed framework is validated with simulations using aerial manipulators combined with two degrees-of-freedom robotic arms using a noisy measurement. Throughout the simulation, we can decrease the mass estimation error by 60% compared to the existing first-order momentum-based method. In addition, a comparison study shows that the proposed method satisfactorily estimates an arbitrary center-of-mass of an unknown payload in noisy environments.

Asynchronous Sensor Fusion of GPS, IMU and CAN-Based Odometry for Heavy-Duty Vehicles

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work.

Subject-Specific Spino-Pelvic Models Reliably Measure Spinal Kinematics During Seated Forward Bending in Adult Spinal Deformity.

Image-based subject-specific models and simulations are recently being introduced to complement current state-of-the-art mostly static insights of the adult spinal deformity (ASD) pathology and improve the often poor surgical outcomes. Although the accuracy of a recently developed subject-specific modeling and simulation framework has already been quantified, its reliability to perform marker-driven kinematic analyses has not yet been investigated. The aim of this work was to evaluate the reliability of this subject-specific framework to measure spine kinematics in ASD patients, in terms of 1) the overall test-retest repeatability; 2) the inter-operator agreement of spine kinematic estimates; and, 3) the uncertainty of those spine kinematics to operator-dependent parameters of the framework. To evaluate the overall repeatability 1], four ASD subjects and one control subject participated in a test-retest study with a 2-week interval. At both time instances, subject-specific spino-pelvic models were created by one operator to simulate a recorded forward trunk flexion motion. Next, to evaluate inter-operator agreement 2], three trained operators each created a model for three ASD subjects to simulate the same forward trunk flexion motion. Intraclass correlation coefficients (ICC’s) of the range of motion (ROM) of conventional spino-pelvic parameters [lumbar lordosis (LL), sagittal vertical axis (SVA), thoracic kyphosis (TK), pelvic tilt (PT), T1-and T9-spino-pelvic inclination (T1/T9-SPI)] were used to evaluate kinematic reliability 1] and inter-operator agreement 2]. Lastly, a Monte-Carlo probabilistic simulation was used to evaluate the uncertainty of the intervertebral joint kinematics to operator variability in the framework, for three ASD subjects 3]. LL, SVA, and T1/T9-SPI had an excellent test-retest reliability for the ROM, while TK and PT did not. Inter-operator agreement was excellent, with ICC values higher than test-retest reliability. These results indicate that operator-induced uncertainty has a limited impact on kinematic simulations of spine flexion, while test-retest reliability has a much higher variability. The definition of the intervertebral joints in the framework was identified as the most sensitive operator-dependent parameter. Nevertheless, intervertebral joint estimations had small mean 90% confidence intervals (1.04°–1.75°). This work will contribute to understanding the limitations of kinematic simulations in ASD patients, thus leading to a better evaluation of future hypotheses.

Kinematic Zenith Tropospheric Delay Estimation with GNSS PPP in Mountainous Areas

The use of global navigation satellite systems (GNSS) precise point positioning (PPP) to estimate zenith tropospheric delay (ZTD) profiles in kinematic vehicular mode in mountainous areas is investigated. Car-mounted multi-constellation GNSS receivers are employed. The Natural Resources Canada Canadian Spatial Reference System PPP (CSRS-PPP) online service that currently processes dual-frequency global positioning system (GPS) and Global’naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) measurements and is now capable of GPS integer ambiguity resolution is used. An offline version that can process the above and Galileo measurements simultaneously, including Galileo integer ambiguity resolution is also tested to evaluate the advantage of three constellations. A multi-day static data set observed under open sky is first tested to determine performance under ideal conditions. Two long road profile tests conducted in kinematic mode are then analyzed to assess the capability of the approach. The challenges of ZTD kinematic profiling are numerous, namely shorter data sets, signal shading due to topography and forests of conifers along roads, and frequent losses of phase lock requiring numerous but not always successful integer ambiguity re-initialization. ZTD profiles are therefore often only available with float ambiguities, reducing system observability. Occasional total interruption of measurement availability results in profile discontinuities. CSRS-PPP outputs separately the zenith hydrostatic or dry delay (ZHD) and water vapour content or zenith wet delay (ZWD). The two delays are analyzed separately, with emphasis on the more unpredictable and highly variable ZWD, especially in mountainous areas. The estimated delays are compared with the Vienna Mapping Function 1 (VMF1), which proves to be highly effective to model the large-scale profile variations in the Canadian Rockies, the main contribution of GNSS PPP being the estimation of higher frequency ZWD components. Of the many conclusions drawn from the field experiments, it is estimated that kinematic profiles are generally determined with accuracy of 10 to 20 mm, depending on the signal harshness of the environment.

Evaluating the validity and reliability of inertial measurement units for determining knee and trunk kinematics during athletic landing and cutting movements

Inertial Measurement Units (IMUs) are promising alternatives to laboratory-based motion capture methods in biomechanical assessment of athletic movements. The aim of this study was to investigate the validity of an IMU system for determining knee and trunk kinematics during landing and cutting tasks for clinical and research applications in sporting populations. Twenty-seven participants performed five cutting and landing tasks while being recorded using a gold-standard optoelectronic motion capture system and an IMU system. Intra-class coefficients, Pearson’s r, root-mean-square error (RMSE), bias, and Bland-Altman limits of agreements between the motion capture and IMU systems were quantified for knee and trunk sagittal- and frontal-plane range-of-motion (ROM) and peak angles. Our results indicate that IMU validity was task-, joint-, and plane-dependent. Based on good-to-excellent (ICC) correlation, reasonable accuracy (RMSE < 5°), bias within 2°, and limits of agreements within 10°, we recommend the use of this IMU system for knee sagittal-plane ROM estimations during cutting, trunk sagittal-plane peak angle estimation during the double-leg landing task, trunk sagittal-plane ROM estimation for almost all tasks, and trunk frontal-plane peak angle estimation for the right single-leg landing task. Due to poor comparisons with the optoelectronic system, we do not recommend this IMU system for knee frontal-plane kinematic estimations.

Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics

Surface Electromyography (sEMG) plays a key role in many applications such as control of Human-Machine Interfaces (HMI) and neuromusculoskeletal modeling. It has strongly nonlinear relations to joint kinematics and reflects the subjects’ intention in moving their limbs. Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches. However, these methods have drawbacks that limit their usability. Different from them, joint kinematics can be continuously reconstructed from sEMG via estimation approaches, for instance, the Artificial Neural Networks (ANNs). The Comparison of different ANNs used in different studies is difficult, and in many cases, impossible. The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals. The presented ANNs are Feedforward, Cascade-Forward, Radial Basis Function (RBFNN), and Generalized Regression (GRNN). Each ANN is applied to its special parametric study. All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system. The RBFNN has the best performance over the others with a 79.80% mean correlation coefficient over all joints, and its accuracy reaches as high as 92.67% in some joints. Interestingly, the highest accuracy over individual joints is 93.46%, which is achieved via the GRNN. The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.

Closed-loop multibody kinematic optimization coupled with double calibration improves scapular kinematic estimates in asymptomatic population

Non-invasive methods still need to better estimate scapular kinematics because of soft tissue artifact issue. This study aimed to develop and assess new procedures to estimate scapular kinematics by combining closed kinematic chain optimization and double calibration. Sixteen healthy volunteers performed static postures mimicking analytical and daily living movements. Scapulo-thoracic angles were computed either with a scapula locator (Ref), or with a closed-loop multibody kinematic optimization (Ell) or with double calibration involving linear (DClin), exponential (DCexp) or logarithmic (DClog) correction. Double calibration corrections enforced scapulo-thoracic angles to be the same than those measured with Ref at the end of the movement performed. DClin and DClog significantly (p < 0.01) reduced scapulo-thoracic misorientation for at least the second third of the movement with averaged improvement ranging from 9° to 32°. Moreover, for arm elevation in the sagittal plane, internal rotations and mimicking hair combing, the beneficial effect of DClin and DClog propagates up to half of the movement. To conclude, when a kinematic chain is required, coupling double calibration (using either linear or logarithmic correction), to a closed-loop multibody kinematic optimization is an efficient and fast method in regard with improvement in scapular kinematic estimates in healthy population.

The Origin and Fate of Subantarctic Mode Water in the Southern Ocean

AbstractSubantarctic Mode Water (SAMW) forms in deep mixed layers just north of the Antarctic Circumpolar Current in winter, playing a fundamental role in the ocean uptake of heat and carbon. Using a gridded Argo product and the ERA-Interim reanalysis for years 2004-2018, the seasonal evolution of the SAMW volume is analyzed using both a kinematic estimate of the subduction rate and a thermodynamic estimate of the air-sea formation rate. The seasonal SAMW volume changes are separately estimated within the monthly mixed layer and in the interior below it. We find that the variability of SAMW volume is dominated by changes in SAMW volume in the mixed layer. The seasonal variability of SAMW volume in the mixed layer is governed by formation due to air-sea buoyancy fluxes (45%, lasting from July to August), entrainment (35%), and northward Ekman transport across the Subantarctic Front (10%). The interior SAMW formation is entirely controlled by exchanges between the mixed layer and the interior (i.e. instantaneous subduction), which occurs mainly during August-October. The annual mean subduction estimate from a Lagrangian approach shows strong regional variability with hotspots of large SAMW subduction. The SAMW subduction hotspots are consistent with the distribution and export pathways of SAMW over the central and eastern parts of the south Indian and Pacific Oceans. Hotspots in the south Indian Ocean produce strong subduction of 8 and 9 Sv for the light and southeast Indian SAMW, respectively, while SAMW subduction of 6 and 4 Sv occurs for the central and southeast Pacific SAMW, respectively.

Unravelling the evolution of regional-scale shear zones with the aid of microstructures and textural analysis: an example from the central Himalaya

Microstructure and texture data on the Main Central Thrust zone (MCTz) in central Himalaya, on two nearly N-S oriented valleys in the Manaslu range (Western Nepal), are provided. Kinematic indicators at the meso and microscale support a south-directed flow. From south to north, greenschist facies paragneiss in the garnet-zone pass to amphibolite facies rocks in the kyanite-zone, suggesting an up-section metamorphic temperature increase. Combining microstructures and data from two different texture analysers (X-ray Texture goniometry and crystal-fabric microanalyzer) allowed to link the temperature path (from 460°C to 600°C up-section) and kinematic vorticity estimates. The “orientation of oblique grain shape fabrics” method was applied, pointing out a general shear flow (40-56% of pure shear) at the MCTz top. Results are in line with a “decelerating strain-path” model, as relatively high simple shear components are recorded at deeper structural levels. This work highlights that the combination of X-ray texture goniometry and crystal-fabric microanalyzer techniques is a good strategy to ensure quartz full-fabric.

Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm.

Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely the side, rear side, and rear views, to obtain the kinematic data and EE estimation. Support vector machine was used for physical activity classification. Three EE estimation models, namely linear regression, multilayer perceptron (MLP), and convolutional neural network (CNN) models, were compared and determined the model with optimal performance in different experimental settings. The results have shown that if only one depth camera is available, optimal EE estimation can be obtained using the side view and MLP model. The mean absolute error (MAE), mean square error (MSE), and root MSE (RMSE) of the classification results under the aforementioned settings were 0.55, 0.66, and 0.81, respectively. If higher accuracy is required, two depth cameras can be set at the side and rear views, the CNN model can be used for light-to-moderate activities, and the MLP model can be used for vigorous activities. The RMSEs for estimating the EEs of standing, walking, and running were 0.19, 0.57, and 0.96, respectively. By applying the different models on different amounts of cameras, the optimal performance can be obtained, and this is also the first study to discuss the issue.

EM-based extended object tracking without a priori extension evolution model

This paper proposes a novel extended object tracking (EOT) problem, in which the three quantities of object extension (OE), including semi-axis lengths and orientation, are depicted more realistic, rather than assuming prior evolution models as in the traditional methods. By the fact that the semi-axis lengths are intrinsic parameters of object size and the orientation is a state-dependent parameter, OE quantities are treated as parameters to be identified. Such consideration is not only more consistent with the OE practical meaning, but also reduces the uncertainty induced by prior parameter settings. The resultant EOT problem brings out the new challenge: deep coupling between kinematic state and OE, which is difficult to directly derive an Bayesian solution. In the expectation maximization framework, an optimization scheme is developed for joint OE identification and kinematic state estimation. Simulation results show the superiority of the proposed method compared with the state-of-the-art ones.

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists.

The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive assistance. Smart walkers are a particular type of these devices since they integrate navigation systems, path-following algorithms, and user interaction modules to ensure natural and intuitive interaction. Although these functionalities are often implemented in rehabilitation scenarios, there is a need to actively involve the healthcare professionals in the interaction loop while guaranteeing safety for them and patients. This work presents the validation of two visual feedback strategies for the teleoperation of a simulated robotic walker during an assisted navigation task. For this purpose, a group of 14 clinicians from the rehabilitation area formed the validation group. A simple path-following task was proposed, and the feedback strategies were assessed through the kinematic estimation error (KTE) and a usability survey. A KTE of 0.28 m was obtained for the feedback strategy on the joystick. Additionally, significant differences were found through a Mann–Whitney–Wilcoxon test for the perception of behavior and confidence towards the joystick according to the modes of interaction (p-values of 0.04 and 0.01, respectively). The use of visual feedback with this tool contributes to research areas such as remote management of therapies and monitoring rehabilitation of people’s mobility.