Kinesthetic Information Research Articles

Reading and Writing Impairment in Persons with Crossed Aphasia: A Comparative Study

Abstract Background Persons with aphasia (PWAs) manifest multifaceted deficits like auditory comprehension, spoken language, reading, and writing to varying degrees. Of these, the most debilitating signs are reading and writing impairment. Of late, PWAs express greater interest to improve their reading and writing skills. The deficits in these skills along with deficits in spoken language limit their participation in the community. Reading and writing may be altered contrarily in various stroke conditions across the left and right hemisphere lesions, just like how linguistic impairments are mutually exclusive. Thus, it must be rational to reconcile the possible role of the right hemisphere to process reading and writing inversely alike spoken abilities. Additionally, investigations on reading and writing deficits in left cerebrovascular atrophy (CVA) aphasics and right CVA aphasics (crossed aphasia) are seldom reported in the literature with relevance to Indian population focusing Indian languages. The study aims to compare reading and writing abilities across PWAs with left CVA and right CVA (crossed aphasia). Methodology The study recruited four native Kannada speaking PWAs (6 months poststroke) aged between 40 and 70 years with impaired linguistic abilities secondary to stroke through convenient sampling. Western Aphasia Battery-Kannada was administered to identify aphasic component, specifically to evaluate the language, reading, and writing aspects in detail and the scores were analyzed based on the scoring. Results Descriptive statistics revealed both left and crossed PWAs performed equipotent on linguistics tasks. However, reading abilities were better in left CVA individuals and writing was better in crossed PWAs on comparing the mean scores. Correlation analysis revealed positive correlation between language and reading-writing abilities of left CVA PWAs. Negative correlation was evinced in crossed PWAs. Hence, reading and writing difficulties in crossed aphasia can be mostly ascribed to impairment in peripheral mechanisms involved in visual and/or kinesthetic information processing as the right hemisphere is most viable to these processes. Conclusion The study highlights the unique pattern of reading and writing abilities in crossed aphasia condition. However, careful examination of dissociations in acquired spoken language and written language abilities across differential neuronal networks through larger samples in the future research is warranted.

Proprioceptive short-term memory in passive motor learning

A physical trainer often physically guides a learner’s limbs to teach an ideal movement, giving the learner proprioceptive information about the movement to be reproduced later. This instruction requires the learner to perceive kinesthetic information and store the instructed information temporarily. Therefore, (1) proprioceptive acuity to accurately perceive the taught kinesthetics and (2) short-term memory to store the perceived information are two critical functions for reproducing the taught movement. While the importance of proprioceptive acuity and short-term memory has been suggested for active motor learning, little is known about passive motor learning. Twenty-one healthy adults (mean age 25.6 years, range 19–38 years) participated in this study to investigate whether individual learning efficiency in passively guided learning is related to these two functions. Consequently, learning efficiency was significantly associated with short-term memory capacity. In particular, individuals who could recall older sensory stimuli showed better learning efficiency. However, no significant relationship was observed between learning efficiency and proprioceptive acuity. A causal graph model found a direct influence of memory on learning and an indirect effect of proprioceptive acuity on learning via memory. Our findings suggest the importance of a learner’s short-term memory for effective passive motor learning.

Mulsemedia data consolidation method

The synchronization of multimodal data is one of the essential tasks related to mulse-media data processing. The concept of mulsemedia (MULtiple SEnsorial MEDIA) involves the registration, storage, processing, transmission and reproduction by computer-based tools of multimodal information about a physical object that humans can perceive through their senses. Such information includes audiovisual information (object's appearance, acoustic properties, etc.), tactile information (surface texture, temperature), kinesthetic information (weight, object's centre of gravity), information about its taste, smell, etc. The perception of mulsemedia information by a person is the process that exists over time. Because of this, the registration of mulsemedia data should be carried out with the fixation of the moments of time when the relevant mulsemedia information existed or its perception made sense for a human who supervises the object as mulsemedia data is temporal. This paper presents a method that enables the consolidation and synchronization of mulsemedia data using the principles of multithreading. The universal method was designed to support combining data of different modalities in parallel threads. The application of the proposed method solves problems associated with integrating data of different modalities and formats in the same time interval. The effectiveness of applying this method increases by us-ing multithreaded distributed computing. This method is designed for use in the development of mulsemedia software systems. The modified JSON format (TJSON – Timeline JSON) was proposed in the paper, as well. TJSON-object is a complex data structure for representing the synchronized mulsemedia data and their further processing. The proposed method can be further extended with other approaches and technologies. For example, artificial intelligence methods can be applied to assess the correlation between data from different modalities. This can help improve the method's accuracy and the output files' quality.

Bio-Inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception.

In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger has a flexible skeleton and skin that adapts to different objects, and the skeleton deformations during interaction provide contact information obtained by the image from the inner camera. The proposed framework is an end-to-end method that uses raw images from soft fingers to estimate in-hand object pose. It consists of an encoder for kinesthetic information processing and an object pose and category estimator. The framework was tested on seven objects, achieving an impressive error of 2.02 mm and 11.34 degrees for pose error and 99.05% for classification.

Differential design in homogenous sensors for classification and decoupling kinesthetic information through machine learning

To accurately reflect the movement of humans or robots, multi-sensor integration strategy is necessary to decouple complex deformations caused by motion in the wearable artificial kinesthetic perception system. The multi-sensor integration strategy could collect multi-dimension information, making up for the deficiency of robustness and accuracy of single sensor in complex motion scenes and extending the sensing capability of the system. However, the inevitable inconsistency between multiple sensors causes difficulties in fabrication, integration, and perception, limiting the development of artificial kinesthetic perception systems toward the direction of compact integration, large-area sensing, and high-resolution decoupling. Here, we propose a strategy to build an artificial kinesthetic perception system based on the differential design of homogeneous sensors. The strategy aims at guiding system design to avoid the inconsistency in multi-sensor integration by revealing the explicit relationship among structure, signal, and movements from the perspective of the physics model. According to the strategy, we presented a simple fabrication method of the kinesthetic perception prototype. The prototype with two homogenous sensors (0.019 56 residual on average) shows three differential signal modes to three deformations. With the help of machine learning, it realized the decoupling task to 25 kinds of complex deformations. The accuracy remains at 95% even though the decoupling resolution is up to 0.2 mm. With more than one prototype arrayed, complex deformation composed with more kinds of basic deformation (shear and twist) could be further decoupled. We believe that the strategy described in this paper will contribute to the development of a compact and programmable kinesthetic perception system.

Virtual reality interaction based on visual attention and kinesthetic information

Virtual reality interaction based on visual attention and kinesthetic information

Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning.

There are physical Human-Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of the human. Computer vision methods provide pre-grasp information with strong constraints imposed by the field environments. Force-based compliant control, after grasping, limits the amount of applied strength. On the other hand, valuable tactile and proprioceptive information can be obtained from the pHRI gripper, which can be used to better know the features of the human and the contact state between the human and the robot. This paper presents a novel dataset of tactile and kinesthetic data obtained from a robot gripper that grabs a human forearm. The dataset is collected with a three-fingered gripper with two underactuated fingers and a fixed finger with a high-resolution tactile sensor. A palpation procedure is performed to record the shape of the forearm and to recognize the bones and muscles in different sections. Moreover, an application for the use of the database is included. In particular, a fusion approach is used to estimate the actual grasped forearm section using both kinesthetic and tactile information on a regression deep-learning neural network. First, tactile and kinesthetic data are trained separately with Long Short-Term Memory (LSTM) neural networks, considering the data are sequential. Then, the outputs are fed to a Fusion neural network to enhance the estimation. The experiments conducted show good results in training both sources separately, with superior performance when the fusion approach is considered.

The effect of movement frequency on perceptual-motor learning of a novel bimanual coordination pattern.

The most widely known studies of rhythmic limb coordination showed that frequency strongly affects the stability of some coordinations (e.g. 180° relative phase) but not others (e.g. 0°). The coupling of such rhythmic limb movements was then shown to be perceptual. Frequency affected the stability of perceptual information. We now investigated whether frequency would impact the pickup of information for learning a novel bimanual coordination pattern (e.g. 90°) and the ability to sustain the coordination at various frequencies. Twenty participants were recruited and assessed on their performance of bimanual coordination at 0°, 180°, and 90° at five scanning frequencies before and after training at 90°, during which they were assigned to practice with either a high (2.5Hz) or low (0.5Hz) frequency until attaining proficiency. The results showed that learning was frequency specific. The best post-training performance occurred at the trained frequency. Although the coordination could be acquired through high frequency training, it was at the cost of a greater amount of training and most surprisingly, did not yield improved performance at lower frequencies that are normally thought to be easier. The findings suggest that movement frequency may determine whether visual or kinesthetic information is used for learning and control of bimanual coordination.

Haptic Zoom: An Interaction Model for Desktop Haptic Devices with Limited Workspace

Haptic devices can be used to feel through the sense of touch what the user is watching in a virtual scene. Force feedback devices provide kinesthetic information enabling the user to touch virtual objects. However, the most reasonably priced devices of this type are the desktop ones, which have a limited workspace that does not allow a natural and convenient interaction with virtual scenes due to the difference in size between them and the workspace. In this paper, a brand-new interaction model addressing this problem is proposed. It is called Haptic Zoom and it is based on performing visual and haptic amplifications of regions of interest. These amplifications allow the user to decide whether s/he wants more freedom in movements or an accurate interaction with a specific element inside the scene. An evaluation has been carried out comparing this technique and two well-known desktop haptic device techniques. Preliminary results showed that haptic zoom can be more useful than other techniques at accuracy tasks.

Training 90° bimanual coordination at high frequency yields dependence on kinesthetic information and poor performance of dyadic unimanual coordination.

Two groups of participants were trained to be proficient at performing bimanual 90° coordination either at a high (2.5Hz) or low (0.5Hz) frequency with both kinesthetic and visual information available. At high frequency, participants trained for twice as long to achieve performance comparable to participants training at low frequency. Participants were then paired within (low-low or high-high) or between (low-high) frequency groups to perform a visually coupled dyadic unimanual 90° coordination task, during which they were free to settle at any jointly determined frequency to synchronize their rhythmic movements. The results showed that the coordination skill was frequency-specific. For dyads with one or both members who had learned the 90° bimanual coordination at low frequency, the performance settled at a low frequency (≈0.5Hz) with more successfully synchronized trials. For dyads with both members who had learned the 90° bimanual coordination at high frequency, they struggled with the task and performed poorly. The dyadic coordination settled at a higher frequency (≈1.5Hz) on average, but with twice the variability in settling frequency and significantly fewer synchronized trials. The difference between the dyadic coordination and bimanual tasks was that only visual information was available to couple the movements in the former while both kinesthetic and visual information were available in the latter. Therefore, the high frequency group must have relied on kinesthetic information to perform both coordination tasks while the low frequency group was well able to use visual information for both. In the mixed training pairs, the low frequency trained member of the pair was likely responsible for the better performance. These conclusions were consistent with results of previous studies.

ANALYSIS OF REQUIREMENTS FOR THE DEVELOPMENT OF A TACTILE AND KINESTHETIC CODEC FOR THE NETWORKS 2030

An important research area in the field of telecommunications is the transmission of tactile and kinesthetic information over communication networks, including Networks 2030. The subject of the research is the analysis of the requirements for the transmission of tactile and kinesthetic information. The research method is the collection and analysis of available research on the topic of the article. Core results of the work are generalized data on the existing requirements for communication networks for the transmission of haptic sensations. Practical relevance of the work lies in the possibility of using the obtained results to create a universal codec for transmitting tactile and kinesthetic information over the Networks 2030.

Static Weight Perception Through Skin Stretch and Kinesthetic Information: Detection Thresholds, JNDs, and PSEs.

We examined the contributions of kinesthetic and skin stretch cues to static weight perception. In three psychophysical experiments, several aspects of static weight perception were assessed by asking participants either to detect on which hand a weight was presented or to compare between two weight cues. Two closed-loop controlled haptic devices were used to present cutaneous and kinesthetic weights, in isolation and together, with a precision of 0.05 g. Our results show that combining skin stretch and kinesthetic information leads to better weight detection thresholds than presenting uni-sensory cues does. For supra-threshold stimuli, Weber fractions were 22-44%. Kinesthetic information was less reliable for lighter weights, while both sources of information were equally reliable for weights up to 300 g. Weight was perceived as equally heavy regardless of whether skin stretch and kinesthetic cues were presented together or alone. Data for lighter weights complied with an Optimal Integration model, while for heavier weights, measurements were closer to predictions from a Sensory Capture model. The presence of correlated noise might explain this discrepancy, since that would shift predictions from the Optimal Integration model towards our measurements. Our experiments provide device-independent perceptual measures, and can be used to inform, for instance, skin stretch device design.

A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments

Real-time tele-haptic applications require capturing, compressing, transmitting, and displaying haptic information, which includes tactile and kinesthetic information. To achieve a high quality of service (QoS), real-time haptic data stream synchronization between local and remote environments is required. However, transmission of data over a computer network is often affected by network impairments, such as network delay, jitter, and packet loss, thus leading to system instability and poor performance. Current prediction algorithms for networked haptics comprise perceptual data reduction, traffic prioritization approaches, congestion control approaches, and radio resource allocation. However, the mentioned prediction algorithms either do not consider packet loss and time-varying delays (i.e., jitter) in their experimental setup, or only consider packet loss or delays. In real-world network environments, both packet loss and delays often occur simultaneously. In this work, a network adaptive Trust Strategy Prediction (TSP) algorithm was modified to work under both network impairments. The objective of the TSP is to maintain real-time haptic synchronization (haptic data stream synchronization) between the haptic interactive environments, by compensating network impairments using selective and specific prediction strategies, according to changes in the network’s characteristics. The experimental results demonstrate that TSP offers greater accuracy and smaller inconsistencies in terms of the predicted position, compared to the dead reckoning prediction and velocity estimation, which is often employed with filtering techniques.

Elaboration of Neuropsychological Evaluation of Children: Structural Analysis of Test Results.

Modern neuropsychology is discussing the possibility of combining qualitative and quantitative approaches in the evaluation of cognitive functions. In Russia a battery of tests called "Methods of neuropsychological assessment for children 6-9 years old" (Akhutina et al., 2016) has been proposed; it is based on the Lurian approach to diagnosis and combines qualitative and quantitative approaches to testing. The present paper describes the development of this combined qualitative and quantitative assessment of various groups of cognitive functions in preschool and primary school children. Structural modeling enables us to analyze a possible combination of integral indices of functions that includes the results of both a face-to-face neuropsychological assessment and computerized testing. To develop a combined qualitative and quantitative neuropsychological assessment of children, in order to 1) check the structural reliability of integral indicators of various cognitive functions; and 2) confirm the correctness of combining the results of face-to-face and computerized tests. A sample of 299 children between the ages of 6 and 9 years old (111 preschoolers, 82 first graders, and 106 second graders) underwent a Lurian face-to-face neuropsychological examination adapted for 6-to-9 year-old children, and five tests from the Computerized Neuropsychological Assessment for 6-9 Year-old Children. The five were the "Dots" test, the Schulte Tables, the Cancellation test, the Corsi Tapping Block test, and the Understanding of Similar Sounding Words test. In each of the tests (face-to-face and computerized), key parameters were identified to evaluate various cognitive functions. A confirmatory factor analysis verified the composition of the neuropsychological indices that were based on the results of the face-to-face neuropsychological assessment. At the same time, when the computer test data were added to the model, the fit indices of the model considerably improved. The confirmatory factor analysis confirmed the validity of the identification of eight neuropsychological indices that indicate the component processes underlying complex cognitive functions in children: 1) programming and control of voluntary actions (executive functions); 2) serial organization of movements and speech; 3) the processing of kinesthetic information; 4) the processing of auditory information; 5) the processing of visual information; 6) the processing of visual-spatial information; 7) hyperactivity/impulsivity; and 8) fatigue/slowness.

Bayesian and Neural Inference on LSTM-Based Object Recognition From Tactile and Kinesthetic Information

Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes analytical and data-driven methodologies to fuse tactile- and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-and-release Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through Bayesian and Neural inference approaches. An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems. The results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach.

The impact of motion gain on egocentric distance judgments from motion parallax

For self-generated motion parallax, a sense of head velocity is needed to estimate distance from object motion. This information can be obtained from proprioceptive and visual sources. If visual and kinesthetic information are incongruent, the visual motion of objects will not match the sensed physical velocity of the head, resulting in a distortion of perceived distances. We assessed this prediction by varying the gain between physical observer head motion and the simulated motion. Given that the relative and absolute motion parallax would be greater than expected from head motion when gain was greater than 1.0, we anticipated that this manipulation would result in objects appearing closer to the observer. Using an HMD, we presented targets 1 to 3 meters away from the observer within a cue rich environment with textured walls and floors. Participants stood and swayed laterally at a rate of 0.5 Hz paced using a metronome. Lateral gain was applied by amplifying their real position by factors of 1.0 to 3.0, then using that to set the instantaneous viewpoint within the virtual environment. After presentation, the target disappeared and the participant performed a blind walk and reached for it. Their hand position was recorded and we computed positional errors relative to the target. We found no effect of motion parallax gain manipulation on binocular reaching accuracy. In a second study we evaluated the role of stereopsis in counteracting the anticipated distortion in perceived space by testing observers monocularly. In this case, distances were perceived as nearer as gain increased, but the effects were relatively small. Taken together our results suggest that observers are flexible in their interpretation of observer produced motion parallax during active head movement. This provides considerable tolerance of spatial perception to mismatches between physical and virtual motion in rich virtual environments.

Eyes wide shut: How visual cues affect brain patterns of simulated gait.

In the last 20 years, motor imagery (MI) has been extensively used to train motor abilities in sport and in rehabilitation. However, MI procedures are not all alike as much as their potential beneficiaries. Here we assessed whether the addition of visual cues could make MI performance more comparable with explicit motor performance in gait tasks. With fMRI we also explored the neural correlates of these experimental manipulations. We did this in elderly subjects who are known to rely less on kinesthetic information while favoring visual strategies during motor performance. Contrary to expectations, we found that the temporal coupling between execution and imagery times, an index of the quality of MI, was less precise when participants were allowed to visually explore the environment. While the brain activation patterns of the gait motor circuits were very similar in both an open‐eyed and eye‐shut virtual walking MI task, these differed for a vast temporo‐occipito‐parietal additional activation for open‐eyed MI. Crucially, the higher was the activity in this posterior network, the less accurate was the MI performance with eyes open at a clinical test of gait. We conclude that both visually‐cued and internally‐cued MI are associated with the neurofunctional activation of a gait specific motor system. The less precise behavioral coupling between imagined and executed gait while keeping eyes open may be attributed to the processing load implied in visual monitoring and scanning of the environment. The implications of these observations for rehabilitation of gait with MI are discussed.

Stretching the skin immediately enhances perceived stiffness and gradually enhances the predictive control of grip force.

When manipulating objects, we use kinesthetic and tactile information to form an internal representation of their mechanical properties for cognitive perception and for preventing their slippage using predictive control of grip force. A major challenge in understanding the dissociable contributions of tactile and kinesthetic information to perception and action is the natural coupling between them. Unlike previous studies that addressed this question either by focusing on impaired sensory processing in patients or using local anesthesia, we used a behavioral study with a programmable mechatronic device that stretches the skin of the fingertips to address this issue in the intact sensorimotor system. We found that artificial skin-stretch increases the predictive grip force modulation in anticipation of the load force. Moreover, the stretch causes an immediate illusion of touching a harder object that does not depend on the gradual development of the predictive modulation of grip force.

Development of Haptic Stylus for Manipulating Virtual Objects in Mobile Devices

In mobile devices, the screen size limits conveyance of immersive experiences; haptic feedback coupled with visual feedback is expected to have a better effect to maximize the level of immersion. Therefore, this paper presents a miniature tunable haptic stylus based on magnetorheological (MR) fluids to provide kinesthetic information to users. The designed stylus has a force generation, force transmission, and housing part; moreover, in the stylus, all three operating modes of MR fluids contribute to the haptic actuation to produce a wide range of resistive force generated by MR fluids in a limited size, thereby providing a variety of pressing sensations to users. A universal testing machine was constructed to evaluate haptic performance of the proposed haptic stylus, whose resistive force was measured with the constructed setup as a function of pressed depth and input current, and by varying the pressed depth and pressing speed. Under maximum input voltage, the stylus generates a wide range of resistive force from 2.33 N to 27.47 N, whereas under maximum pressed depth it varied from 1.08 N to 27.47 N with a corresponding change in voltage input from 0 V to 3.3 V. Therefore, the proposed haptic stylus can create varied haptic sensations.

A compact and compliant electrorheological actuator for generating a wide range of haptic sensations

Robust haptic devices that can convey the entire spectrum of human touch perception are necessary to afford realistic haptic experiences. For vivid and immersive interaction, a combination of both tactile and kinesthetic information must be presented to users. While vibrotactile feedback has become ubiquitous in today’s handheld devices, traditional kinesthetic actuators present significant challenges to miniaturization. Moreover, only limited success has been achieved in developing haptic actuators capable of conveying both tactile and kinesthetic sensations for small consumer electronics. Therefore, this study presents a compact actuator based on electrorheological (ER) fluid for generating a wide range of concurrent kinesthetic and tactile feedback. The design focus for the proposed actuator is to activate multiple operating modes of ER fluid to maximize the force generated by the actuator within a given small size constraint. To this end, the design incorporated two ground electrodes (a stationary ring electrode and a movable electrode attached to a spring element) for tuning the fluid’s yield stress in both flow and squeeze modes. After fabricating a prototype actuator, testing was performed with a dynamic mechanical analyzer (DMA) and an accelerometer to evaluate its ability to produce a wide range of kinesthetic feedback, as well as distinct vibrotactile feedback up to the limit of human perception. The results of kinesthetic testing indicate that the actuator can generate large forces (6.2 N maximum at 4 kV) at rates greater than the just-noticeable difference, indicating that the actuator can convey a wide range of kinesthetic sensations. Tactile evaluation using DMA and the processed acceleration response demonstrated that the actuator can generate both low and high frequency (up to 300 Hz) vibrotactile sensations at perceivably high intensity.