Joint angle estimation during shoulder abduction exercise using contactless technology.

BACKGROUND: Tele-rehabilitation, also known as tele-rehab, uses communication technologies to provide rehabilitation services from a distance. The COVID-19 pandemic has highlighted the importance of tele-rehab, where the in-person visits declined and the demand for remote healthcare rises. Tele-rehab offers enhanced accessibility, convenience, cost-effectiveness, flexibility, care quality, continuity, and communication. However, the current systems are often not able to perform a comprehensive movement analysis. To address this, we propose and validate a novel approach using depth technology and skeleton tracking algorithms. METHODS: Our data involved 14 participants (8 females, 6 males) performing shoulder abduction exercises. We collected depth videos from an LiDAR camera and motion data from a Motion Capture (Mocap) system as our ground truth. The data were collected at distances of 2 m, 2.5 m, and 3.5 m from the LiDAR sensor for both arms. Our innovative approach integrates LiDAR with the Cubemos and Mediapipe skeleton tracking frameworks, enabling the assessment of 3D joint angles. We validated the system by comparing the estimated joint angles versus Mocap outputs. Personalized calibration was applied using various regression models to enhance the accuracy of the joint angle calculations. RESULTS: The Cubemos skeleton tracking system outperformed Mediapipe in joint angle estimation with higher accuracy and fewer errors. The proposed system showed a strong correlation with Mocap results, although some deviations were present due to noise. Precision decreased as the distance from the camera increased. Calibration significantly improved performance. Linear regression models consistently outperformed nonlinear models, especially at shorter distances. CONCLUSION: This study showcases the potential of a marker-less system, to proficiently track body joints and upper-limb angles. Signals from the proposed system and the Mocap system exhibited robust correlation, with Mean Absolute Errors (MAEs) consistently below [Formula: see text]. LiDAR's depth feature enabled accurate computation of in-depth angles beyond the reach of traditional RGB cameras. Altogether, this emphasizes the depth-based system's potential for precise joint tracking and angle calculation in tele-rehab applications.

Photogrammetric analysis of limb joint angles in cows with normal gait before and after hoof trimming.

The aim of this study was to evaluate the effect of hoof trimming on overall limb movements by comparing the changes in 8 limb joint angles 1 wk before and 1 wk after hoof trimming. Seventeen Holstein-Friesian dairy cows that were able to move freely and had no history of hoof diseases were included in the study. The cows were walked on rubber mats with a high friction coefficient (HFM) and a low friction coefficient (LFM) due to the spraying of sodium polyacrylate. Each cow had 15 reflective markers applied to its right side. A high-speed camera was set to 200 frames per second (fps) on the image analysis software, and the images of the cows were captured while cows walked on the test mat. The tests were conducted 1 wk before and 1 wk after hoof trimming, and the cows were trimmed by the functional hoof trimming method. With image analysis software, video clips of walking cows were confirmed visually and tracked during 1 gait cycle by each reflective marker attached to the hoof of the forelimb and hindlimb, after which the stance phase and swing phase were identified. The durations of the stance phase and swing phase of the forelimb and hindlimb, respectively, and the maximum, minimum, and range of motion (ROM) values of the 8 joint angles (shoulder joint, elbow joint, carpus joint, forelimb fetlock joint, hip joint, stifle joint, hock joint, and hindlimb fetlock joint) during 1 gait cycle were included in the analysis. The maximum and minimum angles of the hip and stifle joints were narrower after hoof trimming than before, although the ROM did not change and was clearer for HFM than for LFM. It was thought that the flexion of the proximal hindlimb would progress smoothly during walking after trimming.

Home-based guidance training system with interactive visual feedback using kinect on stroke survivors with moderate to severe motor impairment.

The home-based training approach benefits stroke survivors by providing them with an increased amount of training time and greater feasibility in terms of their training schedule, particularly for those with severe motor impairment. Computer-guided training systems provide visual feedback with correct movement patterns during home-based training. This study aimed to investigate the improvement in motor performance among stroke survivors with moderate to severe motor impairment after 800 min of training using a home-based guidance training system with interactive visual feedback. Twelve patients with moderate to severe stroke underwent home-based training, totaling 800 min (20-40 min per session, with a frequency of 3 sessions per week). The home-based guidance training system uses Kinect to reconstruct the 3D human body skeletal model and provides real-time motor feedback during training. The training exercises consisted of six core exercises and eleven optional exercises, including joint exercises, balance control, and coordination. Pre-training and post-training assessments were conducted using the Fugl-Meyer Assessment-Upper Limb (FMA-UE), Fugl-Meyer Assessment-Lower Limb (FMA-LE), Functional Ambulation Categories (FAC), Berg Balance Scale (BBS), Barthel Index (BI), Modified Ashworth Scale (MAS), as well as kinematic data of joint angles and center of mass (COM). The results indicated that motor training led to the attainment of the upper limit of functional range of motion (FROM) in hip abduction, shoulder flexion, and shoulder abduction. However, there was no improvement in the active range of motion (AROM) in the upper extremity (U/E) and lower extremity (L/E) joints, reaching the level of the older healthy population. Significant improvements were observed in both left/right and superior/inferior displacements, as well as body sway in the mediolateral axis of the COM, after 800 min of training. In conclusion, the home-based guidance system using Kinect aids in improving joint kinematics performance at the level of FROM and balance control, accompanied by increased mediolateral body sway of the COM for stroke survivors with moderate to severe stroke. Additionally, spasticity was reduced in both the upper and lower extremities after 800 min of home-based training.

Biomechanics of step-off drop landings are affected by limb dominance and lead limb in task initiation.

This study examines the effects of limb dominance and lead limb in task initiation on the kinetics and kinematics of step-off drop landings. Nineteen male participants performed drop landings led by the dominant and non-dominant limbs at 45-cm and 60-cm drop heights. Ground reaction force (GRF) and lower body kinematic data were collected. Between-limb time differences at the initial ground contact were calculated to indicate temporal asymmetry. Statistical Parametric Mapping (SPM) was applied for waveform analysis while two-way repeated measures ANOVA was used for discrete parameters. SPM results revealed greater GRF and lesser ankle dorsiflexion in the lead limb compared to the trail limb in 3 out of 4 landing conditions. The dominant limb displayed a greater forefoot loading rate (45 cm: p=.009, ηp2 = 0.438; 60 cm: p=.035, ηp2 = 0.225) and greater ankle joint quasi-stiffness (45 cm: p < .001, ηp2 = 0.360; 60 cm: p < .001, ηp2 = 0.597) than the non-dominant limb. Not all 380 trials were lead-limb first landings, with a smaller between-limb time difference (p=.009, d = 0.60) at 60 cm (4.1 ± 2.3 ms) than 45 cm (5.6 ± 2.7 ms). In conclusion, the step-off drop landing is not an ideal protocol for examining bilateral asymmetry in lower limb biomechanics due to potential biases introduced by limb dominance and the step-off limb.

Errors in Estimating Lower-Limb Joint Angles and Moments during Walking Based on Pelvic Accelerations: Influence of Virtual Inertial Measurement Unit's Frontal Plane Misalignment.

The accurate estimation of lower-limb joint angles and moments is crucial for assessing the progression of orthopedic diseases, with continuous monitoring during daily walking being essential. An inertial measurement unit (IMU) attached to the lower back has been used for this purpose, but the effect of IMU misalignment in the frontal plane on estimation accuracy remains unclear. This study investigated the impact of virtual IMU misalignment in the frontal plane on estimation errors of lower-limb joint angles and moments during walking. Motion capture data were recorded from 278 healthy adults walking at a comfortable speed. An estimation model was developed using principal component analysis and linear regression, with pelvic accelerations as independent variables and lower-limb joint angles and moments as dependent variables. Virtual IMU misalignments of -20°, -10°, 0°, 10°, and 20° in the frontal plane (five conditions) were simulated. The joint angles and moments were estimated and compared across these conditions. The results indicated that increasing virtual IMU misalignment in the frontal plane led to greater errors in the estimation of pelvis and hip angles, particularly in the frontal plane. For misalignments of ±20°, the errors in pelvis and hip angles were significantly amplified compared to well-aligned conditions. These findings underscore the importance of accounting for IMU misalignment when estimating these variables.

Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles.

Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.

Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor.

Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor's trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model.

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis.

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications.

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

Dynamics of the suprapatellar bursa during knee joint extension.

PURPOSE: The suprapatellar bursa is located in the proximal deep layer of the patella and is thought to reduce tissue friction by changing from a single-membrane structure to a double-membrane structure during knee joint motion. However, the dynamics of the suprapatellar bursa have only been inferred from positional relationships, and the actual dynamics have not been confirmed. METHODS: Dynamics of the suprapatellar bursa during knee joint motion were observed in eight knees of four Thiel-fixed cadavers and the angle at which the bursa begins to show a double membrane was revealed. The flexion angles of knee joints were measured when the double-membrane structure of the suprapatellar bursa began to appear during knee joint extension. RESULTS: The suprapatellar bursa changes from a single membrane to a double-membrane structure at 91 ± 4° of flexion, when the knee joint is moved from a flexed position to an extended position. CONCLUSION: The suprapatellar bursa may be involved in limitations to knee joint range of motion and pain at an angle of approximately 90°. Further studies are needed to verify whether the same dynamics are observed in living subjects.

Motor Point as an Alternative to Femoral Nerve Stimulation for the Assessment of Quadriceps Muscle Inhibition in Healthy Women.

CONTEXT: The evaluation of quadriceps muscle inhibition with the interpolated twitch technique is usually performed by stimulating the femoral nerve (FN). However, there are some problems related to the use of this stimulation site, which may be partially overcome by delivering the stimulation over the motor point (MP). This study sought to compare MP to FN stimulation at different joint angles for the evaluation of quadriceps muscle inhibition, resting peak torque, and discomfort in healthy women. DESIGN: Cross-sectional study. METHODS: Sixteen healthy women (age: 28 [4] y; body mass: 60 [5] kg; height: 162 [5] cm) participated in this study. Supramaximal paired stimuli were delivered to the FN and to the rectus femoris MP before and during maximal voluntary contractions at different knee angles (15°, 30°, 45°, 60°, and 90° of knee flexion) to assess muscle inhibition and resting peak torque. Discomfort was also recorded for each stimulation site and knee angle. RESULTS: Muscle inhibition was similar between the 2 stimulation sites (P > .05) and was higher at 45° than at 90° (P = .03). MP stimulation evoked lower resting peak torque at 30° (P = .004), 60° (P = .006), and 90° (P = .006) and higher discomfort at 30° (P = .008) and 90° (P = .027) compared to FN stimulation. CONCLUSIONS: Despite lower resting peak torque and higher discomfort at some angles, MP stimulation provided similar muscle inhibition to FN stimulation at all knee angles and is therefore a valid method to evaluate quadriceps muscle inhibition in healthy women. MP stimulation can be used as an alternative to FN stimulation for the evaluation of quadriceps muscle inhibition with no added discomfort at the angles where muscle inhibition is the highest.

Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression.

In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human-robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R2 of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer's motion intentions in human-robot collaboration control.

Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography.

Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body's movement intention. In this paper, the OpenSim software is used to determine the muscle sites to be measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, lateral gastrocnemius, and tibial anterior. The surface electromyography (sEMG) signals and inertial data are collected from the lower limbs while the human body is walking, going upstairs, and going uphill. The sEMG noise is reduced by a wavelet-threshold-based complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) reduction algorithm, and the time-domain features are extracted from the noise-reduced sEMG signals. Knee and hip angles during motion are calculated using quaternions through coordinate transformations. The random forest (RF) regression algorithm optimized by cuckoo search (CS), shortened as CS-RF, is used to establish the prediction model of lower limb joint angles by sEMG signals. Finally, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics to compare the prediction performance of the RF, support vector machine (SVM), back propagation (BP) neural network, and CS-RF. The evaluation results of CS-RF are superior to other algorithms under the three motion scenarios, with optimal metric values of 1.9167, 1.3893, and 0.9815, respectively.

EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network.

One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average R2 scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained.

Measurement of Trunk Movement during Sit-to-Stand Motion Using Laser Range Finders: A Preliminary Study.

The sit-to-stand (STS) motion evaluates physical functions in frail older adults. Mounting sensors or using a camera is necessary to measure trunk movement during STS motion. Therefore, we developed a simple measurement method by embedding laser range finders in the backrests and seats of chairs that can be used in daily life situations. The objective of this study was to validate the performance of the proposed measurement method in comparison with that of the optical motion capture (MoCap) system during STS motion. The STS motions of three healthy young adults were simultaneously measured under seven conditions using a chair with embedded sensors and the optical MoCap system. We evaluated the waveform similarity, absolute error, and relationship of the trunk joint angular excursions between these measurement methods. The experimental results indicated high waveform similarity in the trunk flexion phase regardless of STS conditions. Furthermore, a strong relationship was observed between the two measurement methods with respect to the angular excursion of the trunk flexion. Although the angular excursion of the trunk extension exhibited a large error, the developed chair with embedded sensors evaluated trunk flexion during the STS motion, which is a characteristic of frail older adults.

Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.

Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.

Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control.

To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb's structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.

Utility and Usability of Two Forms of Supplemental Vibrotactile Kinesthetic Feedback for Enhancing Movement Accuracy and Efficiency in Goal-Directed Reaching.

Recent advances in wearable sensors and computing have made possible the development of novel sensory augmentation technologies that promise to enhance human motor performance and quality of life in a wide range of applications. We compared the objective utility and subjective user experience for two biologically inspired ways to encode movement-related information into supplemental feedback for the real-time control of goal-directed reaching in healthy, neurologically intact adults. One encoding scheme mimicked visual feedback encoding by converting real-time hand position in a Cartesian frame of reference into supplemental kinesthetic feedback provided by a vibrotactile display attached to the non-moving arm and hand. The other approach mimicked proprioceptive encoding by providing real-time arm joint angle information via the vibrotactile display. We found that both encoding schemes had objective utility in that after a brief training period, both forms of supplemental feedback promoted improved reach accuracy in the absence of concurrent visual feedback over performance levels achieved using proprioception alone. Cartesian encoding promoted greater reductions in target capture errors in the absence of visual feedback (Cartesian: 59% improvement; Joint Angle: 21% improvement). Accuracy gains promoted by both encoding schemes came at a cost in terms of temporal efficiency; target capture times were considerably longer (1.5 s longer) when reaching with supplemental kinesthetic feedback than without. Furthermore, neither encoding scheme yielded movements that were particularly smooth, although movements made with joint angle encoding were smoother than movements with Cartesian encoding. Participant responses on user experience surveys indicate that both encoding schemes were motivating and that both yielded passable user satisfaction scores. However, only Cartesian endpoint encoding was found to have passable usability; participants felt more competent using Cartesian encoding than joint angle encoding. These results are expected to inform future efforts to develop wearable technology to enhance the accuracy and efficiency of goal-directed actions using continuous supplemental kinesthetic feedback.

Dimple of Venus Is Associated with the Increased Pelvic Incidence Angle and More Sagittally Oriented Facet Joint.

OBJECTIVES: The present study aims to investigate whether the dimple of Venus affects the anatomy of spinopelvic junction. SUBJECTS AND METHODS: Inclusion criteria were having a lumbar MRI examination in the last 1 year, being older than 18 years of age and being able to radiologically evaluate the whole vertebral colon and pelvic girdle. Exclusion criteria were having congenital diseases of the pelvic girdle/hip/vertebral column and history of fracture or previous surgery in the same anatomic regions. The patients' demographic data and low back pain were noted. At radiological examination, the pelvic incidence angle was measured by lateral lumbar X-ray. The facet joint angle, tropism, facet joint degeneration, intervertebral disc degeneration, and intervertebral disc herniation at the level of L5-S1 were examined on lumbar MRIs. RESULTS: We included 134 male and 236 female patients with a mean age of 47.86 ± 14.50 years and 48.49 ± 13.49 years, respectively. We found that the patients with the dimple of Venus had higher pelvic incidence angle (p < 0.001) and more sagittally oriented facet joint (right facet joint p = 0.017, left facet joint p = 0.001) compared to those without the dimple of Venus. There was no statistically significant relationship between low back pain and the presence of the dimple of Venus. CONCLUSIONS: The dimple of Venus affects the anatomy of the spinopelvic junction and is associated with an increased pelvic incidence angle and a more sagittally oriented facet joint angle.