Hand Position Research Articles

Abnormal coordination of upper extremity during target reaching in persons post stroke

Understanding abnormal synergy of the upper extremity (UE) in stroke survivors is critical for better identification of motor impairment. Here, we investigated to what extent stroke survivors retain the ability to coordinate multiple joints of the arm during a reaching task. Using an exoskeleton robot, 37 stroke survivors’ arm joint angles (θ) and torques (τ) during hand reaching in the horizontal plane was compared to that of 13 healthy controls. Kinematic and kinetic coordination patterns were quantified as variances of the multiple-joint angles and multiple-joint torques across trials, respectively, that were partitioned into task-irrelevant variance (TIVθ and TIVτ) and task-relevant variance (TRVθ and TRVτ). TIVθ and TRVθ (or TIVτ and TRVτ) led to consistent and inconsistent hand position (or force), respectively. The index of synergy (ISθ and ISτ) was determined as {IS}_{theta }=({TIV}_{theta }-{TRV}_{theta })/({TIV}_{theta }+{TRV}_{theta }) and {IS}_{tau }=({TIV}_{tau }-{TRV}_{tau })/({TIV}_{tau }+{TRV}_{tau }) for kinematic and kinetic coordination patterns, respectively. Both kinematic ISθ and kinetic ISτ in the stroke group were significantly lower than that of the control group, indicating stroke survivors had impaired reaching abilities in utilizing the multiple joints of the UE for successful completion of a reaching task. The reduction of kinematic ISθ in the stroke group was mainly attributed to the lower TIVθ as compared to the control group, while the reduction of kinetic ISτ was mainly due to the higher {TRV}_{tau } as well as lower TIVτ. Our results also indicated that stroke may lead to motor deficits in formation of abnormal kinetic synergistic movement of UE, especially during outward movement. The findings in abnormal synergy patterns provides a better understanding of motor impairment, suggesting that impairment-specific treatment could be identified to help improve UE synergies, focusing on outward movements.

No reduction in motor-evoked potential amplitude during the rubber hand illusion.

In the rubber hand illusion (RHI), touches are applied to a fake hand at the same time as touches are applied to a participant's real hand that is hidden in a congruent position. Synchronous (but not asynchronous) tactile stimulation of the two hands may induce the sensation that the fake hand is the participant's own. As such, the illusion is commonly used to examine the sense of body ownership. Some studies indicate that in addition to the subjective experience of limb ownership reported by participants, the RHI can also reduce corticospinal excitability (e.g., as reflected in motor-evoked potential [MEP] amplitude) and alter parietal-motor cortical connectivity in passive participants. These findings have been taken to support a link between motor cortical processing and the subjective experience of body ownership. In this study, we tried to replicate the reduction in MEP amplitude associated with the RHI and uncover the components of the illusion that might explain these changes. As such, we used single-pulse transcranial magnetic stimulation to probe the excitability of the corticospinal motor system as participants experienced the RHI. Despite participants reporting the presence of the illusion and showing shifts in perceived real hand position towards the fake limb supporting its elicitation, we did not observe any associated reduction in MEP amplitude. We conclude that a reduction in MEP amplitude is not a reliable outcome of the RHI and argue that if such effects do occur, they are unlikely to be large or functionally relevant.

COVID-19 and SMEs Digital Transformation

The purpose of this study is to examine the relationship between digital marketing (DM), digital transformation, and brand position and brand promotion in companies of different sizes in Kosovo. Partial least square was used to analyze the data collected and structural equation modeling was used to assess the relationship between exogenous and endogenous variables used in the model proposed for the research. The results showed that the DM and the digitalization process of companies had an impact on brand promotion and brand position. The results also showed that social media use was prevalent compared with other marketing channels, and that companies that used DM and invested more in technology had better chances of promoting and positioning their brands. This study provides the literature with a better understanding of the relationship between DM and digital transformation on the one hand and brand position and brand promotion on the other. In addition, this research has practical implications for companies and various other organizations in their efforts to digitize their business and marketing activities.

Specific motor learning induced by space-variant visual feedback distortion of the hand position

Specific motor learning induced by space-variant visual feedback distortion of the hand position

Embodied action in remote online interaction

Abstract In this paper I use a conversation analytic approach to investigate how participants in a meeting held remotely via Zoom use embodied action to solicit selection as next speaker. When hand raising is not immediately successful, participants use embodied actions to withdraw, modify, upgrade, downgrade or reissue gestures in pursuit of selection as next speaker. Due to the technological affordances and limitations of the remote meeting environment, participants’ gestures and hand positions differ from what would typically occur in face-to-face interaction, resulting in frequent gestures near the face that provide for both visibility to the Zoom audience and easy transition to a raised hand position when necessary. I discuss these results in terms of our understanding of how technologically mediated virtual interaction through the internet impacts the use of embodied action, and how participants coordinate their embodied action and responses to it with turn taking and sequence completion.

Dream to posture: visual posturing of a tendon-driven hand using world model and muscle synergies

Although tendon-driven anthropomorphic robot hands have the potential to achieve human-level dexterity, controlling them is a great challenge owing to their mechanical complexities. Therefore, investigating human-hand control strategies is of the utmost importance. An important skill that enables the versatile manipulation ability of humans is visual posturing, i.e. the skill to make arbitrary hand postures based solely on visual observation. Visual posturing facilitates manipulation learning by enabling visual imitation learning and reusing visually similar past experiences. Therefore, this study investigates a method to replicate visual posturing in anthropomorphic robotic hands. Visual posturing in tendon-driven hands is challenging because of the hysteresis in tendon systems, the partial observability of the problem, and the presence of many actuators owing to the antagonistic tendon arrangement. To address these challenges, we propose a method that combines a model predictive path integral, a world model, and bio-inspired muscle synergies. The evaluation in a physical tendon-driven anthropomorphic robot hand showed that the proposed method achieved better visual posturing performance than a naive regression model. We anticipate that our visual posturing method will lay the foundation for versatile manipulation controllers that can adaptively learn manipulation tasks, similar to humans.

Mechanisms and neuroanatomy of response selection in tool and non-tool action tasks: Evidence from left-hemisphere stroke

The ability to select between potential actions is central to the complex process of tool use. After left hemisphere stroke, individuals with limb apraxia make more hand action errors when gesturing the use of tools with conflicting hand actions for grasping-to-move and use (e.g., screwdriver) relative to tools that are grasped-to-move and used with the same hand action (e.g., hammer). Prior research indicates that this grasp-use interference effect is driven by abnormalities in the competitive action selection process. The goal of this project was to determine whether common mechanisms and neural substrates support the competitive selection of task-appropriate responses in both tool and non-tool domains. If so, the grasp-use interference effect in a tool use gesturing task should be correlated with response interference effects in the classic Eriksen flanker and Simon tasks, and at least partly overlapping neural regions should subserve the 3 tasks. Sixty-four left hemisphere stroke survivors (33 with apraxia) participated in the tool- and non-tool interference tasks and underwent T1 anatomical MRI. There were robust grasp-use interference effects (grasp-use conflict test) and response interference effects (Eriksen flanker and Simon tasks), but these effects were not correlated. Lesion-symptom mapping analyses showed that lesions to the left inferior parietal lobule, ventral premotor cortex, and insula were associated with grasp-use interference. Lesions to the left inferior parietal lobule, postcentral gyrus, insula, caudate, and putamen were associated with response interference in the Eriksen flanker task. Lesions to the left caudate and putamen were also associated with response interference in the Simon task. Our results suggest that the selection of hand posture for tool use is mediated by distinct cognitive mechanisms and partly distinct neuroanatomic substrates from those mapping a stimulus to an appropriate motor response in non-tool domains.

Approaches for Hybrid Coregistration of Marker-Based and Markerless Coordinates Describing Complex Body/Object Interactions.

Full-body motion capture is essential for the study of body movement. Video-based, markerless, mocap systems are, in some cases, replacing marker-based systems, but hybrid systems are less explored. We develop methods for coregistration between 2D video and 3D marker positions when precise spatial relationships are not known a priori. We illustrate these methods on three-ball cascade juggling in which it was not possible to use marker-based tracking of the balls, and no tracking of the hands was possible due to occlusion. Using recorded video and motion capture, we aimed to transform 2D ball coordinates into 3D body space as well as recover details of hand motion. We proposed four linear coregistration methods that differ in how they optimize ball-motion constraints during hold and flight phases, using an initial estimate of hand position based on arm and wrist markers. We found that minimizing the error between ball and hand estimate was globally suboptimal, distorting ball flight trajectories. The best-performing method used gravitational constraints to transform vertical coordinates and ball-hold constraints to transform lateral coordinates. This method enabled an accurate description of ball flight as well as a reconstruction of wrist movements. We discuss these findings in the broader context of video/motion capture coregistration.

CentiTrack: Toward Centimeter-Level Passive Gesture Tracking With Commodity WiFi

Gesture awareness plays a crucial role in promoting human-computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability and reliability. In this paper, we propose CentiTrack, the first centimeter-level passive gesture tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the Channel State Information (CSI) measurement error sources in the physical layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal Component Analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of dynamic path reflected off the hand. In addition, we adopt the Multiple Signal Classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range and device cost, compared with the state-of-the-arts.

Conveying product weight in digital media using a hand image

The present research examines the role of the image of a hand in an online product evaluation. Through eleven studies (i.e., six in the manuscript and five in the Web Appendix), we show that, when a hand image is depicted in an unsupported holding position (i.e., lifting a product), consumers’ confidence in their judgment of the product's weight is enhanced. This outcome increases product evaluation, purchase intention, and product choice. The hand image effect is mediated by a mental representation of a haptic experience (i.e., haptic imagery). Additionally, we show that an image of a hand in an unsupported holding position increases consumers’ confidence in the product's weight, while other hand positions or their absence do not have a similar impact. Online retailers can use our findings to employ hand images to influence the evaluations and purchase intentions of products in which weight is a salient haptic attribute.

Advancements and Future Directions in Human Activity Recognition

Abstract: Activity recognition deals with the automation of recognizing various activities by identifying the subject and its interactions with the environment. Human activity recognition deals with identifying different activities like sitting, walking, laying moving, running, jogging, various hand movements, posture and behavior prediction and recognition. Activity recognition deals with the automation of recognizing various activities by identifying the subject and its interactions with the environment. The wide variety of environments are incorporated with Human Activity Recognition. It assists with the vast variety of problems related to people, living, lifestyle, security, monitoring, which may or may not be related to computer science.

Hand Gesture Recognition and Cursor Control

Abstract: Convolutional neural network in this process to overcome the issue, use the network AutoGesNet for the gesture recognition task that creating effectiveneural network architecture is challenging. To be more precise, we first combine and preprocess three sets of gesture recognition data. The AutoGesNet search space and general architecture are then designed. Additionally, we employ transfer learning and reinforcement learning techniques to automatically create the intricate AutoGesNet architecture. Finally, the searched neural network is adjusted and retrained for two alternative input sizes. The retrained model performs accurately on both our data set and the NUS Hand Posture Dataset II, according to experiments. network that performs wellin terms of recognition accuracy. We will contrast andmerge AutoGesNet in further work

Grip selection without tool knowledge: end-state comfort effect in familiar and novel tool use.

A well-known phenomenon for the study of movement planning is the end-state comfort (ESC) effect: When they reach and grasp tools, individuals tend to adopt uncomfortable initial hand postures if that allows a subsequent comfortable final posture. In the context of tool use, this effect is modulated by tool orientation, task goal, and cooperation. However, the cognitive bases of the ESC effect remain unclear. The goal of this study was to determine the contribution of semantic tool knowledge and technical reasoning to movement planning, by testing whether the ESC effect typically observed with familiar tools would also be observed with novel tools. Twenty-six participants were asked to reach and grasp familiar and novel tools under varying conditions (i.e., tool's handle downward vs. upward; tool transport vs. use; solo vs. cooperation). In our findings, the effects of tool orientation, task goal and cooperation were replicated with novel tools. It follows that semantic tool knowledge is not critical for the ESC effect to occur. In fact, we found an "habitual" effect: Participant adopted uncomfortable grips with familiar tools even when it was not necessary (i.e., to transport them), probably because of the interference of habitual movement programming with actual movement programming. A cognitive view of movement planning is proposed, according to which goal comprehension (1) may rely on semantic tool knowledge, technical reasoning, and/or social skills, (2) defines end-state configuration, which in turn (3) calibrates beginning-state comfort and hence the occurrence of the ESC effect.

Congruent visual cues speed dynamic motor adaptation

We demonstrate that adaptation to novel dynamics is stronger when additional online visual cues that are congruent with the dynamics are presented during adaptation, compared with either a constant or incongruent visual cue. This effect was found regardless of whether a bidirectional or unidirectional velocity-dependent force field was presented to the participants. We propose that this effect might arise through the inclusion of this additional visual cue information within the state estimation process.

Cortical bone distribution of the proximal phalanges in great apes: implications for reconstructing manual behaviours.

Primate fingers are typically in direct contact with the environment during both locomotion and manipulation, and aspects of external phalangeal morphology are known to reflect differences in hand use. Since bone is a living tissue that can adapt in response to loading through life, the internal bone architecture of the manual phalanges should also reflect differences in manual behaviours. Here, we use the R package Morphomap to analyse high-resolution microCT scans of hominid proximal phalanges of digits 2-5 to determine whether cortical bone structure reflects variation in manual behaviours between bipedal (Homo), knuckle-walking (Gorilla, Pan) and suspensory (Pongo) taxa. We test the hypothesis that relative cortical bone distribution patterns and cross-sectional geometric properties will differ both among extant great apes and across the four digits due to locomotor and postural differences. Results indicate that cortical bone structure reflects the varied hand postures employed by each taxon. The phalangeal cortices of Pongo are significantly thinner and have weaker cross-sectional properties relative to the African apes, yet thick cortical bone under their flexor sheath ridges corresponds with predicted loading during flexed finger grips. Knuckle-walking African apes have even thicker cortical bone under the flexor sheath ridges, as well as in the region proximal to the trochlea, but Pan also has thicker diaphyseal cortices than Gorilla. Humans display a distinct pattern of distodorsal thickening, as well as relatively thin cortices, which may reflect the lack of phalangeal curvature combined with frequent use of flexed fingered hand grips during manipulation. Within each taxon, digits 2-5 have a similar cortical distribution in Pongo, Gorilla and, unexpectedly, Homo, which suggest similar loading of all fingers during habitual locomotion or hand use. In Pan, however, cortical thickness differs between the fingers, potentially reflecting differential loading during knuckle-walking. Inter- and intra-generic variation in phalangeal cortical bone structure reflects differences in manual behaviours, offering a comparative framework for reconstructing hand use in fossil hominins.

The concepts of muscle activity generation driven by upper limb kinematics

BackgroundThe underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.MethodsWe develop a novel approach utilizing recurrent neural networks that are able to predict muscle activity of the upper limbs associated with complex 3D human arm motions. Therefore, motion parameters such as joint angle, velocity, acceleration, hand position, and orientation, serve as input for the models. In addition, these models are trained on multiple subjects (n=5 including , 3 male in the age of 26±2 years) and thus can generalize across individuals. In particular, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific fine-tuned model using a transfer learning approach to adapt the model to a new subject. Estimators such as mean square error MSE, correlation coefficient r, and coefficient of determination R2 are used to evaluate the goodness of fit. We additionally assess performance by developing a new score called the zero-line score. The present approach was compared with multiple other architectures.ResultsThe presented approach predicts the muscle activity for previously through different subjects with remarkable high precision and generalizing nicely for new motions that have not been trained before. In an exhausting comparison, our recurrent network outperformed all other architectures. In addition, the high inter-subject variation of the recorded muscle activity was successfully handled using a transfer learning approach, resulting in a good fit for the muscle activity for a new subject.ConclusionsThe ability of this approach to efficiently predict muscle activity contributes to the fundamental understanding of motion control. Furthermore, this approach has great potential for use in rehabilitation contexts, both as a therapeutic approach and as an assistive device. The predicted muscle activity can be utilized to guide functional electrical stimulation, allowing specific muscles to be targeted and potentially improving overall rehabilitation outcomes.

Virtual Mouse – Hand Gesture Recognition System

The purpose of this research is to enhance the identification of hand gestures in a Human-Computer Interaction scenario, while also minimizing the computational time required and enhancing user comfort. To achieve this goal, the authors created a computer mouse control application that employs an algorithm and selected hand features, resulting in efficient performance. The integration of voice assistant with the recommended hand postures further improves user experience and facilitates system operation.

Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task

Electroencephalogram (EEG) based motor trajectory decoding for efficient control of brain–computer interface (BCI) systems has been an active area of research. The systems include prosthesis, rehabilitation and human-power augmenting devices. In this work, three-dimensional (3D) hand kinematics is estimated using pre-movement EEG signals during grasp and lift motion. Twelve subjects’ data from the publicly available database WAY-EEG-GAL is utilized for this purpose. Multi-layer perceptron (MLP) and convolutional neural network-long short-term memory (CNN-LSTM) based deep learning frameworks are proposed that utilize the motor-neural information encoded in the EEG data preceding the actual movement execution. Frequency band features are analyzed for hand kinematics decoding using EEG data filtered in seven distinct ranges. The best performing frequency band features is taken for further analysis with different EEG window sizes and lag windows. Additionally, inter-subject hand trajectory decoding analysis is performed using leave-one-subject-out (LOSO) approach. The Pearson correlation coefficient along with hand trajectory are taken to evaluate decoding performance for the proposed neural decoders. This study explores the feasibility of inter-subject 3D hand trajectory decoding using EEG signals during reach and grasp task. The proposed CNN-LSTM decoder is able to achieve the grand correlation in three axes upto 0.730 and 0.627 in intra-subject and inter-subject settings, respectively, thus providing viable information regarding decoding hand position from pre-movement EEG signals for practical BCI applications.

Hubungan Penolong Persalinan Terhadap Kejadian Ruptur Perenium Spontan pada Persalinan Kala II

Perineal rupture is one of the causes of bleeding, which occurs because the perineum is not strong enough to withstand the stretch when the fetus passes. The purpose of this study was to determine the effect of birth attendants on the incidence of spontaneous perineal rupture in the second stage of labour. The research method used in this study is observation where research is conducted without intervening in research subjects. The population in this study were all midwives who assisted spot deliveries at the Asy Safira and Mother clinics, with the sample used as a total sampling which was taken using a consecutive sampling technique based on a predetermined time. The results of the study showed that most of the position of the second stage birth attendant's hands was not in accordance with the APN of 21 people (55.3%). 50% of cases of deliveries performed with normal birth assistance resulted in grade II perineal tears. There is an effect of the position of the second stage birth attendant's hand on perineal laceration with p = 0.020. Birth attendants are expected to continue to improve their knowledge and skills in performing childbirth assistance

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.