Proprioceptive Data Research Articles

ACUTE EFFECT OF ANKLE DYNAMIC TAPING AND KINESIO TAPING ON BALANCE AND LOWER EXTREMITY FUNCTIONAL PERFORMANCE IN HEALTHY INDIVIDUALS

Objective: The ankle-foot complex is integral to maintaining balance as it gathers proprioceptive data. Dynamic taping (DT) and Kinesio taping (KT) are special elastic therapeutic tape applied to the skin. This study aims to investigate the acute effect (2 hours) of DT and KT on dynamic balance and functional performance in healthy individuals. Method: A total of 15 adults (7 males and 8 females) were included in the study. Participants underwent testing under three conditions (No taping, DT, and KT). Balance was assessed with Y Balance Test, while functional performance was evaluated with Single Hop Test, Triple Hop Test, Cross Over Hop Test, and 6 Meter Timed Hop Test. Results: There was a significant difference in Y balance posteromedial test between three conditions (p0.05). Conclusion: The results of this study suggest that ankle KT enhances dynamic balance in healthy individuals.

MARCEL: Mobile active rover chassis for enhanced locomotion

AbstractTo extend planetary exploration beyond the current limitations of wheeled vehicles while preserving reliability, simplicity, and efficiency, actuation can be judiciously incorporated into the locomotion system. Based on a static analysis, we propose a new four‐wheeled chassis concept for planetary rovers that can traverse more challenging terrain with the help of two internal active joints. These joints are arranged as follows: a vertical pivot articulates the chassis around its center while a bogie allows the rear wheels to rotate around the longitudinal axis of the vehicle. We also introduce a control method that uses a two‐stage procedure to produce an interpretable controller based on a policy devised by reinforcement learning. This way, we eliminate the black box made of a neural network and facilitate the transfer from simulation to reality. The resulting controller efficiently harnesses the internal mobility of the chassis to climb over obstacles in a sequenced manner while relying only on proprioceptive data provided by the chassis. A rover prototype named MARCEL has been built and tested experimentally. Contrary to any state‐of‐the‐art six‐wheeled passive chassis, the proposed locomotion system and its associated control has proven to be able to overcome solid step obstacles as tall as the diameter of the wheels with a edge and a friction coefficient as low as 0.5. This simple but capable design will enable future missions to explore more challenging areas while providing better guarantees in the face of unforeseen difficulties that could arise.

Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots

A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras.

Is Ankle Kinesio Taping Effective to Immediately Change Balance, Range of Motion, and Muscle Strength in Healthy Individuals? A Randomized, Sham-Controlled Trial.

BackgroundThe ankle–foot complex plays a key role in maintaining balance because it collects proprioceptive data. Kinesio taping (KT) is a rehabilitative method performed by the cutaneous application of a special elastic tape. The mechanical correction technique of KT was suggested to reposition the joints and alter balance parameters. The aim was to reveal the pure effects of ankle KT on balance, range of motion (ROM), and muscle strength in healthy individuals.MethodsForty healthy students were recruited for this randomized, sham-controlled study at a local university. Participants were divided into two groups—experimental and sham application groups. The primary outcome measures were balance parameters. Athlete Single Leg (ASL), Limits of Stability (LoS), and Clinical Test of Sensory Interaction and Balance (CTSIB) were used to measure single-leg dynamic balance, dynamic postural control, and sensory interaction of balance, respectively. Dorsiflexion ROM and dorsiflexor muscle strength were the secondary outcomes.ResultsThe ASL score showed significant improvement only in the experimental KT group (P=0.02); however, the LoS score increased significantly in both groups (P<0.05). CTSIB scores, dorsiflexion ROM, and dorsiflexor muscle strength for both ankles did not change in any of the groups after intervention (P>0.05). Moreover, there was no superiority of one intervention over the other in improving any of the variables (P>0.05).ConclusionThe mechanical correction technique of KT can be useful in providing immediate improvement in single-leg dynamic balance in healthy individuals. However, it may not be effective to significantly change the sensory interaction of balance, dorsiflexion ROM, and muscle strength.

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A&#x002A; path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths when navigating on unknown terrains.

A Low-Cost Contactless Overhead Micrometer Surface Scanner

The design and implementation of a contactless scanner and its software are proposed. The scanner regards the photographic digitization of planar and approximately planar surfaces and is proposed as a cost-efficient alternative to off-the-shelf solutions. The result is 19.8 Kppi micrometer scans, in the service of several applications. Accurate surface mosaics are obtained based on a novel image acquisition and image registration approach that actively seeks registration cues by acquiring auxiliary images and fusing proprioceptive data in correspondence and registration tasks. The device and operating software are explained, provided as an open prototype, and evaluated qualitatively and quantitatively.

Future Challenges in the Assessment of Proprioception in Exercise Sciences: Is Imitation an Alternative?

Future Challenges in the Assessment of Proprioception in Exercise Sciences: Is Imitation an Alternative?

Upper-Limb Kinematic Parameter Estimation and Localization Using a Compliant Robotic Manipulator

Assistive and rehabilitation robotics have gained momentum over the past decade and are expected to progress significantly in the coming years. Although relevant and promising research advances have contributed to these fields, challenges regarding intentional physical contact with humans remain. Despite being a fundamental component of assistive and rehabilitation tasks, there is an evident lack of work related to robotic manipulators that intentionally manipulate human body parts. Moreover, existing solutions involving end-effector robots are not based on accurate knowledge of human limb dimensions and their current configuration. This knowledge, which is essential for safe human-limb manipulation, depends on the grasping location and human kinematic parameters. This paper addresses the upper-limb manipulation challenge and proposes a pose estimation method using a compliant robotic manipulator. To the best of our knowledge, this is the first attempt to address this challenge. A kinesthetic-based approach enables estimation of the kinematic parameters of the human arm without integrating external sensors. The estimation method relies only on proprioceptive data obtained from a collaborative robot with a Cartesian impedance-based controller to follow a compliant trajectory that depends on human arm kinodynamics. The human arm model is a 2-degree of freedom (DoF) kinematic chain. Thus, prior knowledge of the arm's behavior and an estimation method enables estimation of the kinematic parameters. Two estimation methods are implemented and compared: i) Hough transform (HT); ii) least squares (LS). Furthermore, a resizable, sensorized dummy arm is designed for experimental validation of the proposed approach. Outcomes from six experiments with different arm lengths demonstrate the repeatability and effectiveness of the proposed methodology, which can be used in several rehabilitation robotic applications.

Motion Cost Characterisation of an Omnidirectional WMR on Uneven Terrains

Abstract Electrically powered robots are seeing increased adoption for field robotics applications. These applications typically require the platforms be deployed for extended periods, or traverse large distances. Power modelling therefore becomes important, as knowing the energy costs of operation is necessary for the estimation of maximum range and operating time, and the generation of energy-efficient plans. In this paper, we present a physics-based power model for the Swagbot platform--an omnidirectional electric field robot--which can be calibrated from a combination of GPS and proprioceptive data gathered throughout the course of normal operations. This model is then experimentally verified via endurance testing performed on cattle properties featuring uneven terrain. The model was demonstrated to predict the energy consumption for a pasture survey and a weed detection task to within 5% of the measured value.

A multi-sensor robotic platform for ground mapping and estimation beyond the visible spectrum

Accurate soil mapping is critical for a highly-automated agricultural vehicle to successfully accomplish important tasks including seeding, ploughing, fertilising and controlled traffic, with limited human supervision, ensuring at the same time high safety standards. In this research, a multi-sensor ground mapping and characterisation approach is proposed, whereby data coming from heterogeneous but complementary sensors, mounted on-board an unmanned rover, are combined to generate a multi-layer map of the environment and specifically of the supporting ground. The sensor suite comprises both exteroceptive and proprioceptive devices. Exteroceptive sensors include a stereo camera, a visible and near infrared camera and a thermal imager. Proprioceptive data consist of the vertical acceleration of the vehicle sprung mass as acquired by an inertial measurement unit. The paper details the steps for the integration of the different sensor data into a unique multi-layer map and discusses a set of exteroceptive and proprioceptive features for soil characterisation and change detection. Experimental results obtained with an all-terrain vehicle operating on different ground surfaces are presented. It is shown that the proposed technologies could be potentially used to develop all-terrain self-driving systems in agriculture. In addition, multi-modal soil maps could be useful to feed farm management systems that would present to the user various soil layers incorporating colour, geometric, spectral and mechanical properties.

ICLAP: shape recognition by combining proprioception and touch sensing

For humans, both the proprioception and touch sensing are highly utilized when performing haptic perception. However, most approaches in robotics use only either proprioceptive data or touch data in haptic object recognition. In this paper, we present a novel method named Iterative Closest Labeled Point (iCLAP) to link the kinesthetic cues and tactile patterns fundamentally and also introduce its extensions to recognize object shapes. In the training phase, the iCLAP first clusters the features of tactile readings into a codebook and assigns these features with distinct label numbers. A 4D point cloud of the object is then formed by taking the label numbers of the tactile features as an additional dimension to the 3D sensor positions; hence, the two sensing modalities are merged to achieve a synthesized perception of the touched object. Furthermore, we developed and validated hybrid fusion strategies, product based and weighted sum based, to combine decisions obtained from iCLAP and single sensing modalities. Extensive experimentation demonstrates a dramatic improvement of object recognition using the proposed methods and it shows great potential to enhance robot perception ability.

Improved Outdoor Localization Based on Weighted Kullback-Leibler Divergence for Measurements Diagnosis

Global Navigation Satellite System (GNSS) is growing in usefulness for navigation in outdoor environment. However, the measurements from the satellites (pseudorange, Doppler) could be obscured or degraded due to different phenomena such as the multipath and interference. Therefore, in order to ensure the continuity and the integrity of localization, the fusion of these measurements with proprioceptive data is necessary. Adding also, the erroneous measurements should be detected and excluded from the fusion procedure.

Swarm Underwater Acoustic 3D Localization: Kalman vs Monte Carlo

Two three-dimensional localization algorithms for a swarm of underwater vehicles are presented. The first is grounded on an extended Kalman filter (EKF) scheme used to fuse some proprioceptive data such as the vessel's speed and some exteroceptive measurements such as the time of flight (TOF) sonar distance of the companion vessels. The second is a Monte Carlo particle filter localization processing the same sensory data suite. The results of several simulations using the two approaches are presented, with comparison. The case of a supporting surface vessel is also considered. An analysis of the robustness of the two approaches against some system parameters is given.

From passive to interactive object learning and recognition through self-identification on a humanoid robot

Service robots, working in evolving human environments, need the ability to continuously learn to recognize new objects. Ideally, they should act as humans do, by observing their environment and interacting with objects, without specific supervision. Taking inspiration from infant development, we propose a developmental approach that enables a robot to progressively learn objects appearances in a social environment: first, only through observation, then through active object manipulation. We focus on incremental, continuous, and unsupervised learning that does not require prior knowledge about the environment or the robot. In the first phase, we analyse the visual space and detect proto-objects as units of attention that are learned and recognized as possible physical entities. The appearance of each entity is represented as a multi-view model based on complementary visual features. In the second phase, entities are classified into three categories: parts of the body of the robot, parts of a human partner, and manipulable objects. The categorization approach is based on mutual information between the visual and proprioceptive data, and on motion behaviour of entities. The ability to categorize entities is then used during interactive object exploration to improve the previously acquired objects models. The proposed system is implemented and evaluated with an iCub and a Meka robot learning 20 objects. The system is able to recognize objects with 88.5 % success and create coherent representation models that are further improved by interactive learning.

Global estimation of an object’s pose using tactile sensing

It is essential for a successful completion of a robot object grasping and manipulation task to accurately sense the manipulated object’s pose. Typically, computer vision is used to obtain this information, but it may not be available or be reliable in certain situations. This paper presents a global optimisation method where tactile and force sensing together with the robot’s proprioceptive data are used to find an object’s pose. This method is used to either improve an estimate of the object’s pose given by vision or globally estimating it when no vision is available. Results show that the proposed method consistently improves an initial estimate (e.g. from vision) and is also able to find the object’s pose without any prior estimate. To demonstrate the performance of the algorithm, an experiment is carried out where the robot is handed a small object (a pencil) and inserts it into a narrow hole without any use of vision.

Out of my real body: cognitive neuroscience meets eating disorders.

Clinical psychology is starting to explain eating disorders (ED) as the outcome of the interaction among cognitive, socio-emotional and interpersonal elements. In particular two influential models—the revised cognitive-interpersonal maintenance model and the transdiagnostic cognitive behavioral theory—identified possible key predisposing and maintaining factors. These models, even if very influential and able to provide clear suggestions for therapy, still are not able to provide answers to several critical questions: why do not all the individuals with obsessive compulsive features, anxious avoidance or with a dysfunctional scheme for self-evaluation develop an ED? What is the role of the body experience in the etiology of these disorders? In this paper we suggest that the path to a meaningful answer requires the integration of these models with the recent outcomes of cognitive neuroscience. First, our bodily representations are not just a way to map an external space but the main tool we use to generate meaning, organize our experience, and shape our social identity. In particular, we will argue that our bodily experience evolves over time by integrating six different representations of the body characterized by specific pathologies—body schema (phantom limb), spatial body (unilateral hemi-neglect), active body (alien hand syndrome), personal body (autoscopic phenomena), objectified body (xenomelia) and body image (body dysmorphia). Second, these representations include either schematic (allocentric) or perceptual (egocentric) contents that interact within the working memory of the individual through the alignment between the retrieved contents from long-term memory and the ongoing egocentric contents from perception. In this view EDs may be the outcome of an impairment in the ability of updating a negative body representation stored in autobiographical memory (allocentric) with real-time sensorimotor and proprioceptive data (egocentric).

The MIT Stata Center dataset

This paper presents a large scale dataset of vision (stereo and RGB-D), laser and proprioceptive data collected over an extended duration by a Willow Garage PR2 robot in the 10 story MIT Stata Center. As of September 2012 the dataset comprises over 2.3 TB, 38 h and 42 km (the length of a marathon). The dataset is of particular interest to robotics and computer vision researchers interested in long-term autonomy. It is expected to be useful in a variety of research areas—robotic mapping (long-term, visual, RGB-D or laser), change detection in indoor environments, human pattern analysis, long-term path planning. For ease of use the original ROS ‘bag’ log files are provided and also a derivative version combining human readable data and imagery in standard formats. Of particular importance, this dataset also includes ground-truth position estimates of the robot at every instance (to typical accuracy of 2 cm) using as-built floor-plans—which were carefully extracted using our software tools. The provision of ground-truth for such a large dataset enables more meaningful comparison between algorithms than has previously been possible.

Localization Confidence Domains via Set Inversion on Short-Term Trajectory

The knowledge of localization uncertainties is of prime importance when the navigation of intelligent vehicles has to deal with safety issues. This paper presents a robust estimation method that is able to quantify the localization confidence based on interval analysis and constraint propagation. First, tightly coupled position domains are computed by constraint propagation on Global Positioning System (GPS) measurements and a precise 3-D map of the drivable area. Since GPS is prone to satellite masking and wrong measurements in urban areas, a second stage provides localization integrity and information availability by the use of a position and proprioceptive data history. A robust constraint propagation algorithm is employed to compute the current vehicle pose. It is able to handle erroneous positions with a chosen integrity risk. Experiments carried out in urban canyons illustrate the performance of the method in comparison with a particle filter. Despite bad satellite visibility, full positioning availability is obtained, and errors are less than 5.1 m during 95% of the trial. In opposition to the particle filter, confidence domains are consistent with ground truth, which confirms the high integrity of the method.

A leg proprioception based 6 DOF odometry for statically stable walking robots

This article presents a 3D odometry algorithm for statically stable walking robots that only uses proprioceptive data delivered by joint angle and joint torque sensors embedded within the legs. The algorithm intrinsically handles each kind of emerging statically stable gait and is independent of predefined gait patterns. Additionally, the algorithm can be equally applied to stiff robots as well as to robots with compliant joints. Based on the proprioceptive information a 6 degrees of freedom (DOF) pose estimate is calculated in three steps. First, point clouds, represented by the foot positions with respect to the body frame at two consecutive time steps, are matched and provide a 6 DOF estimate for the relative body motion. The obtained relative motion estimates are summed up over time giving a 6 DOF pose estimate with respect to the start frame. Second, joint torque measurement based pitch and roll angle estimates are determined. Finally in a third step, these estimates are used to stabilize the orientation angles calculated in the first step by data fusion employing an error state Kalman filter. The algorithm is implemented and tested on the DLR Crawler, an actively compliant six-legged walking robot. For this specific robot, experimental data is provided and the performance is evaluated in flat terrain and on gravel, at different joint stiffness settings and for various emerging gaits. Based on this data, problems associated with the odometry of statically stable walking robots are identified and discussed. Further, some results for crossing slopes and edges in a complete 3D scenario are presented.

Sub-goal based Robot Visual Navigation through Sensorial Space Tesselation

In this paper, we propose an evolutionary cognitive architecture to enable a mobile robot to cope with the task of visual navigation. Initially a graph based world representation is used to build a map, prior to navigation, through an appearance based scheme using only features associated with color information. During the next step, a genetic algorithm evolves a navigation controller that the robot uses for visual servoing, driving through a set of nodes on the topological map. Experiments in simulation show that an evolved robot, adapted to both exteroceptive and proprioceptive data, is able to successfully drive through a list of sub-goals minimizing the problem of local minima in which evolutionary process can sometimes get trapped. We also show that this approach is more expressive for defining a simplistic fitness formula yet descriptive enough for targeting specific goals.