Hand Movement Direction Research Articles

Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control.

The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.

Added cognitive demand from increased sensorimotor complexity resulted in better goal-directed movement following stroke: a novel finding in post-stroke recovery

BACKGROUND: Stroke often causes impairments in goal-directed movements, which are commonly assessed using goal-directed reaching tasks. Here we present two individuals post-stroke who performed two robotic reaching tasks: 1. Visually Guided Reaching (VGR); reaching a cursor representing the fingertip to virtual targets, 2. Reverse Visually Guided Reaching (RVGR); where the cursor moves in the opposite direction of hand motion, requiring a novel cognitive rule. Participants are typically more successful at reaching targets in the simple VGR task compared to the more complex RVGR task (~92% of our database). The two cases are notable as they performed better on RVGR compared to VGR. CASE DESCRIPTIONS: Case 1: 80-year-old patient with left hemorrhagic thalamic stroke, with interventricular extension, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40 targets), but were able to reach ~30% of the targets (15/48) in RVGR. Case 2: 76-year-old patient with a left ischemic pontine stroke, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40) but were able to reach ~60% of the targets in RVGR (29/48). INTERPRETATION: Better performance in RVGR compared to VGR may be associated with a “dual-task benefit” and has potential clinical implications, including informing neuro-rehabilitative strategies, potentially by including tasks with added complexity or cognitive components. These findings also highlight the utility of robotic tools to provide novel environments within which to identify unique patterns of impairments and abilities.

Does a concurrent motor process influence representational momentum?

Whether the direction of a hand motion that is congruent or incongruent with a concurrent target motion can influence representational momentum for that target was examined. Participants viewed a leftward or rightward moving target while moving their hand rightward, leftward, or not moving their hand. Prior studies of mental rotation found that congruency or incongruency of the direction of mental rotation and the direction of a concurrent physical rotation of a stimulus influenced mental rotation. As mental rotation and representational momentum each involve extrapolation of target motion, it could be predicted that congruency of the direction of hand motion and the direction of target motion might influence representational momentum of the target. Robust representational momentum occurred in all conditions, but there was no effect of congruency of hand motion and target motion, nor of the presence or absence of hand motion, on representational momentum. The results are consistent with a hypothesis that the generation of representational momentum involves sensory processes rather than motor processes.

Investigating the application of graph theory features in hand movement directions decoding using EEG signals

In recent years, functional analysis of brain networks based on graph theory properties has attracted considerable attention. This approach has usually been exploited for structural and functional brain analysis, while its potential in motor decoding tasks has remained unexplored. This study aimed to investigate the feasibility of using graph-based features in hand direction decoding in movement execution and preparation intervals. Hence, EEG signals were recorded from nine healthy subjects while performing a four-target center-out reaching task. The functional brain network was calculated based on the magnitude-squared coherence (MSC) at six frequency bands. Then, the features based on eight graph theory metrics were extracted from brain networks. The classification was performed with a support vector machine classifier. The results revealed that in four-class direction discrimination, the mean accuracy of the graph-based method surpassed 63% and 53% on movement and pre-movement data, respectively. Additionally, a feature fusion approach that combines the graph theory features with power features was proposed. The fusion method raised the classification accuracy to 70.8% and 61.2% for movement and pre-movement intervals, respectively. This work has verified the feasibility of using graph theory properties and their superiority over band power features in a hand movement decoding task.

Rapid Change in the Direction of Hand Movement to Increase Hand Propulsion During Front Crawl Swimming.

This study aims to investigate the difference in hand acceleration induced by rapid changes in hand movement directions and propulsion between fast and slow groups of swimmers during front crawl swimming. Twenty-two participants, consisting of 11 fast and 11 slow swimmers, performed front crawl swimming at their maximal effort. Hand acceleration and velocity and the angle of attack were measured using a motion capture system. The dynamic pressure approach was used to estimate hand propulsion. In the insweep phase, the fast group attained significantly higher hand acceleration than the slow group in the lateral and vertical directions (15.31 [3.44]m·s-2 vs 12.23 [2.60]m·s-2 and 14.37 [1.70]m·s-2 vs 12.15 [1.21]m·s-2), and the fast group exerted significantly larger hand propulsion than the slow group (53 [5]N vs 44 [7]N). Although the fast group attained large hand acceleration and propulsion during the insweep phase, the hand velocity and the angle of attack were not significantly different in the 2 groups. The rapid change in hand movement direction could be considered in the technique of underwater arm stroke, particularly in the vertical direction, to increase hand propulsion during front crawl swimming.

Does hand proximity enhance letter identification?

Adam et al. (2012) found that letters were identified more accurately when presented near, compared to away from, the hands. Participants performed the task in two conditions: with their hands held stationary and with their hands moving towards and away from the target letters. The near-hands effect included the contribution of both static and dynamic trials. Further studies showed that accuracy in letter discrimination was higher when hands were away from a target (a far-hands effect) and moving toward it, suggesting an interaction between hand position and movement direction. The present study aimed to test whether hand proximity affects letter identification when the hands are stationary, as it remains unclear if this effect can be reliably observed. Participants viewed strings of three consonants, briefly presented and masked, and had to verbally report their identity. Stimuli were presented under two different hand conditions: proximal and distal. The predicted effects of letter position and stimulus duration were all statistically significant and robust; however, we did not observe a hand proximity effect.

Distinct neural representations of hand movement direction between motor imagery and execution in the presupplementary motor area

Motor simulation theory proposes a functional equivalence between motor execution (ME) and its simulation, suggesting that motor imagery (MI) is the self-intentioned simulation of one’s actions. This study used functional magnetic resonance imaging (fMRI) with multivoxel pattern analysis to test whether the direction of hand movement is represented with a similar neural code between ME and MI. In our study, participants used their right hand to move an on-screen cursor in the left–right direction with a joystick or imagined the same movement without execution. The results indicated that the left–right direction as well as their modality (ME or MI) could be decoded significantly above the chance level in the presupplementary motor area (pre-SMA) and primary visual cortex (V1). Next, we used activation patterns of ME as inputs to the decoder to predict hand move directions in MI sessions and found a significantly higher-than-chance accuracy only in V1, not in pre-SMA. Moreover, the representational similarity analysis showed similar activation patterns for the same directions between ME and MI in V1 but not in pre-SMA. This study’s finding indicates distinct spatial activation patterns for movement directions between ME and MI in pre-SMA.

Movement Time for Pointing Tasks in Real and Augmented Reality Environments

Human–virtual target interactions are becoming more and more common due to the emergence and application of augmented reality (AR) devices. They are different from interacting with real objects. Quantification of movement time (MT) for human–virtual target interactions is essential for AR-based interface/environment design. This study aims to investigate the motion time when people interact with virtual targets and to compare the differences in motion time between real and AR environments. An experiment was conducted to measure the MT of pointing tasks on the basis of both a physical and a virtual calculator panel. A total of 30 healthy adults, 15 male and 15 female, joined. Each participant performed pointing tasks on both physical and virtual panels with an inclined angle of the panel, hand movement direction, target key, and handedness conditions. The participants wore an AR head piece (Microsoft Hololens 2) when they pointed on the virtual panel. When pointing on the physical panel, the participants pointed on a panel drawn on board. The results showed that the type of panel, inclined angle, gender, and handedness had significant (p < 0.0001) effects on the MT. A new finding of this study was that the MT of the pointing task on the virtual panel was significantly (p < 0.0001) higher than that of the physical one. Users using a Hololens 2 AR device had inferior performance in pointing tasks than on a physical panel. A revised Fitts’s model was proposed to incorporate both the physical–virtual component and inclined angle of the panel in estimating the MT. This model is novel. The index of difficulty and throughput of the pointing tasks between using the physical and virtual panels were compared and discussed. The information in this paper is beneficial to AR designers in promoting the usability of their designs so as to improve the user experience of their products.

Effects of Cognitive Distraction on Upper Limb Movement Decoding From EEG Signals.

Hand movement decoding from electroencephalograms (EEG) signals is vital to the rehabilitation and assistance of upper limb-impaired patients. Few existing studies on hand movement decoding from EEG signals consider any distractions. However, in practice, patients can be distracted while using the hand movement decoding systems in real life. In this paper, we aim to investigate the effects of cognitive distraction on movement decoding performance. We first propose a robust decoding method of hand movement directions to cognitive distraction from EEG signals by using the Riemannian Manifold to extract affine invariant features and Gaussian Naive Bayes classifier (named RM-GNBC). Then, we use the experimental and simulated EEG data under conditions without and with distraction to compare the decoding performance of three decoding methods (including the proposed method, tangent space linear discriminant analysis (TSLDA), and baseline method)). The simulation and experimental results show that the Riemannian-based methods (i.e., RM-GNBC and TSLDA) have higher accuracy under the conditions without and with cognitive distraction and smaller decreases in decoding accuracy between the conditions without and with cognitive distraction than the baseline method. Furthermore, the RM-GNBC method has 6% (paired t-test, p = 0.026) and 5% (paired t-test, p = 0.137) higher accuracies than the TSLDA method under the conditions without and with cognitive distraction, respectively. The results show that the Riemannian-based methods have higher robustness to cognitive distraction. This work contributes to developing a brain-computer interface (BCI) to improve the rehabilitation and assistance of hand-impaired patients in real life and open an avenue to the studies on the effects of distraction on other BCI paradigms.

A Non-Invasive, Soft Robotic Wearable Glove to Attenuate Hand Tremors

Hand tremors are a widespread medical condition that decreases a patient’s quality of life. Due to uncontrollable hand shaking, patients have difficulty completing essential daily tasks. Many also experience embarrassment and anxiety in social settings, which negatively affects many areas of their lives. Current treatments are often invasive, ineffective, and have unwanted side effects. Some non-invasive solutions excessively limit hand motion, making them ineffective in helping the wearer complete tasks, while others are highly visible and bulky. This paper presents a minimalistic, non-invasive, wearable glove that uses active feedback control and miniature, soft robotic pneumatic actuators to attenuate hand tremors while allowing freedom of movement in other directions. We 3D-modelled the hand and wrist and designed our glove and actuators using Fusion 360. These models were imported into Matlab and Simulink, where we simulated the hand tremor, actuation, and control system using spatial contact forces. To actively reduce the tremor amplitude, we used an accelerometer to detect the onset of tremor motion and create a real-time estimate of the hand’s position. This estimate drove a closed-loop PI controller that produced force commands for the pneumatic actuators, which applied a counterforce opposing the direction of hand motion. Based on our simulation results, our active glove control system design successfully reduced the hand tremor amplitude by 75%, from a baseline of ±8.0mm to a significantly improved amplitude of ±2.0mm. These results indicate that our proposed solution could effectively attenuate hand tremors to a level that would increase the wearer’s quality of life.

Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement.

Decoding human hand movement from electroencephalograms (EEG) signals is essential for developing an active human augmentation system. Although existing studies have contributed much to decoding single-hand movement direction from EEG signals, decoding primary hand movement direction under the opposite hand movement condition remains open. In this paper, we investigated the neural signatures of the primary hand movement direction from EEG signals under the opposite hand movement and developed a novel decoding method based on non-linear dynamics parameters of movement-related cortical potentials (MRCPs). Experimental results showed significant differences in MRCPs between hand movement directions under an opposite hand movement. Furthermore, the proposed method performed well with an average binary decoding accuracy of 89.48 ± 5.92% under the condition of the opposite hand movement. This study may lay a foundation for the future development of EEG-based human augmentation systems for upper limbs impaired patients and healthy people and open a new avenue to decode other hand movement parameters (e.g., velocity and position) from EEG signals.

Eye-hand decoupling deficits in young adults with concussion history from adolescence: Issues with task novelty or task demand?

The present study expands previous work that reported eye-hand decoupling (EHD) deficits in young adults with concussion history from adolescence. Here, we examine whether these deficits in movement planning (i.e., larger initial direction error) can be linked to difficulties with adapting to new task constraints or meeting ongoing task demands. Data from 21 young adults with concussion history from adolescence (CH) and 20 no history controls (NoH) were analyzed. All participants (Mean = 21 yrs.) performed two eye-hand coordination tasks, a standard task with vision and motor action in alignment, and an EHD task with eye- and hand movement direction spatially decoupled, with twenty trials per task condition. The re-analysis focused on the EHD condition as deficits were found for this task only in prior work. Trials were split into 5 blocks (trial 1–4, 5–8, 9–12, 13–16, 17–20), which included one movement to each of four target directions, and were analyzed across blocks and groups. The CH group had a larger initial direction error during blocks 1–2 but not in blocks 3–5. Our findings suggest that young adults with concussion history from adolescence can have difficulty with adapting to new task constraints associated with complex skill performance during a short series of trials.

Band Pull-Apart Exercise: Effects of Movement Direction and Hand Position on Shoulder Muscle Activity.

Background/PurposeThe Elastic band pull-apart exercise is commonly used in rehabilitation. It involves pulling an elastic resistance band with both hands in horizontal abduction or diagonal arm movements. The extent of muscle activation during this exercise is unknown. The purpose of this study was to measure the electromyographic (EMG) activity of shoulder-girdle muscles during the pull-apart exercise using resistance bands and to determine the effects of arm position and movement direction on shoulder-girdle muscle activity.Materials/MethodsSurface EMG activity was measured on the infraspinatus, upper trapezius, middle trapezius, lower trapezius and posterior deltoid of the dominant shoulder. After measurement of maximal voluntary contraction (MVC) for each muscle, subjects performed the band pull-apart exercise in three hand positions (palm up, neutral, palm down) and three movement directions (diagonal up, horizontal, diagonal down). Elastic band resistance was chosen to elicit moderate exertion (5/10 on the Borg CR10 scale). The order of the exercises was randomized and three repetitions of each exercise were performed. Mean peak EMG activity in each muscle across the repetitions was calculated and expressed as a percentage of MVC. Peak normalized EMG activity in each muscle was compared in two-way (hand position x direction) repeated-measures ANOVA.ResultsData were collected from 10 healthy subjects (all males, age 36±12 years). Peak muscle activity ranged from 15.3% to 72.6% of MVC across muscles and exercise conditions. There was a significant main effect of hand position for the infraspinatus and lower trapezius, where muscle activity was highest with the palm up hand position (p < 0.001), and for the upper trapezius and posterior deltoid, where muscle activity was highest with the palm down position (p-value range < 0.001-0.004). There was a significant main effect of movement direction, where the diagonal up direction demonstrated the highest muscle activity for the infraspinatus, upper trapezius, lower trapezius, and posterior deltoid (p-value range < 0.001-0.02).ConclusionAltering hand position and movement direction during performance of an elastic band pull-apart exercise can affect magnitudes of shoulder-girdle muscle activity. Clinicians may alter a patient’s hand position and movement direction while performing the band pull-apart exercise in order to increase muscle activity in target muscles or diminish muscle activity in other muscles.Level of Evidence2b

Eye-hand decoupling decreases visually guided reaching independently of posture but reduces sway while standing: Evidence for supra-postural control

We investigated whether visually guided reaching differs for sitting and standing postures while the eyes and hand are coupled to move in the same direction or decoupled to move in opposite directions. We also investigated how coupled and decoupled reaching tasks influenced standing postural control. Eighteen healthy young adults (M = 21 years) moved a cursor using finger movements along a vertical touchscreen while sitting or standing. In an eye-hand coupling (EH) task, participants moved their finger/cursor from a central target to a peripheral target located either up, down, left, or right. In an eye-hand decoupling (EHD) task, participant’s finger movement moved the cursor in the opposite direction. Sway measures during the standing condition and kinematic variables for the cursor offered insight into whole-body control. Performances in EH revealed smaller errors and faster movements than EHD regardless of postural condition. Similar hand movements existed between sitting and standing when accounting for task, while greater variability in absolute endpoint errors existed for standing than sitting when task was ignored. Less postural sway existed for EHD than EH when standing. These data provide evidence that when participants decoupled the eyes and hand movement direction while standing, they attenuated sway to support control of this complex, cognitively demanding, visuomotor task.

The effect of active hand movement on visually perceived depth direction.

This study investigated the effect of active hand movement on the perception of 3-D depth change. In Experiment 1, the 3-D height of an object synchronously changed with the participant’s hand movement, but the 3-D height of the object was incongruent with the distance moved by the hand. The results showed no effect of active hand movement on perceived depth. This was inconsistent with the results of a previous study conducted in a similar setting with passive hand movement. It was speculated that this contradiction appeared because the conflict between the distance moved by the hand and visual depth changes were more easily detected in the active movement situation. Therefore, it was assumed that in a condition where this conflict was hard to detect, active hand movement might affect visual depth perception. To examine this hypothesis, Experiment 2 examined whether information from hand movement would resolve the ambiguity in the depth direction of a shaded visual shape. In this experiment, the distance moved by the hand could (logically) accord with either of two depth directions (concave or convex). Moreover, the discrepancy in the distances between visual and haptic perception could be ambiguous because shading cues are unreliable in estimating absolute depth. The results showed that perceived depth directions were affected by the direction of active hand movement, thus supporting the hypothesis. Based on these results, simulations based on a causal inference model were performed, and it was found that these simulations could replicate the qualitative aspects of the experimental results.

Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation.

Motor Imagery (MI)-based Brain Computer Interface (BCI) system is a potential technology for active neurorehabilitation of stroke patients by complementing the conventional passive rehabilitation methods. Research to date mainly focused on classifying left vs. right hand/foot MI of stroke patients. Though a very few studies have reported decoding imagined hand movement directions using electroencephalogram (EEG)-based BCI, the experiments were conducted on healthy subjects. Our work analyzes MI-based brain cortical activity from EEG signals and decodes the imagined hand movement directions in stroke patients. The decoded direction (left vs. right) of hand movement imagination is used to provide control commands to a motorized arm support on which patient's affected (paralyzed) arm is placed. This enables the patient to move his/her stroke-affected hand towards the intended (imagined) direction that aids neuroplasticity in the brain. The synchronization measure called Phase Locking Value (PLV), extracted from EEG, is the neuronal signature used to decode the directional movement of the MI task. Event-related desynchronization/synchronization (ERD/ERS) analysis on Mu and Beta frequency bands of EEG is done to select the time bin corresponding to the MI task. The dissimilarities between the two directions of MI tasks are identified by selecting the most significant channel pairs that provided maximum difference in PLV features. The training protocol has an initial calibration session followed by a feedback session with 50 trials of MI task in each session. The feedback session extracts PLV features corresponding to most significant channel pairs which are identified in the calibration session and is used to predict the direction of MI task in left/right direction. An average MI direction classification accuracy of 74.44% is obtained in performing the training protocol and 68.63% from the prediction protocol during feedback session on 16 stroke patients.

Consistent Hand Dynamics Are Achieved by Controlling Variabilities Among Joint Movements During Fastball Pitching.

This study aimed to determine whether covariations among joint movements are utilized to stabilize hand orientation and movement and to determine which of the upper or lower extremities make effective use of the covariation. Joint angles during pitching were measured in 12 skilled baseball pitchers, using a motion capture system. The joint angles in 10 successful trials were used for the reconstructed motions. The reconstructed motion in the first condition was the same as for the measured motion. In the second condition, the reconstructed motion was generated with joint angles that were pseudo-randomly selected to artificially break off covariation in the measured joint-angle combination. In the third and fourth conditions, the reconstructed motions were generated with the same joint-angle combinations as the measured angles in the throwing arm and the stride leg, respectively, but pseudo-randomly selected in the other joint angles. Ten reconstructed motions were generated for each condition. Standard deviations (SDs) of hand orientation and movement direction were calculated and compared among the conditions. All SDs for the first condition were the smallest among the conditions, indicating that the movements in the measured condition used the covariation in joint angles to make the hand movement stable. The results also illustrated that some SDs in the fourth condition were smaller than those in the third condition, suggesting that the lower extremity made effective use of the covariation. These results imply that it is necessary not only to reduce variability in each joint but also to regulate joint movements to stabilize hand orientation and movement.

Decoding Single-Hand and Both-Hand Movement Directions From Noninvasive Neural Signals.

Decoding human movement parameters from electroencephalograms (EEG) signals is of great value for human-machine collaboration. However, existing studies on hand movement direction decoding concentrate on the decoding of a single-hand movement direction from EEG signals given the opposite hand is maintained still. In practice, the cooperative movement of both hands is common. In this paper, we investigated the neural signatures and decoding of single-hand and both-hand movement directions from EEG signals. The potentials of EEG signals and power sums in the low frequency band of EEG signals from 24 channels were used as decoding features. The linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used for decoding. Experimental results showed a significant difference in the negative offset maximums of movement-related cortical potentials (MRCPs) at electrode Cz between single-hand and both-hand movements. The recognition accuracies for six-class classification, including two single-hand and four both-hand movement directions, reached 70.29%± 10.85% by using EEG potentials as features with the SVM classifier. These findings showed the feasibility of decoding single-hand and both-hand movement directions. This work can lay a foundation for the future development of an active human-machine collaboration system based on EEG signals and open a new research direction in the field of decoding hand movement parameters from EEG signals.

The effect of hand motion and object orientation on the automatic detection of orientation: A visual mismatch negativity study.

We investigated the effects of voluntary hand movements and continuously present objects on the automatic detection of deviant stimuli in a passive oddball paradigm. The visual mismatch negativity (vMMN) component of event-related potentials (ERPs) was measured as the index of automatic deviant detection. The stimuli were textures consisting of parallel, oblique bars with frequent (standard) and infrequent (deviant) orientation. Traditional vMMN was measured by the difference between ERPs to frequent (standard) and infrequent (deviant) textures. Additionally, we measured ‘genuine’ vMMN by comparing the ERPs to deviant and control textures in the equal probability procedure. Compatible and incompatible hand movement directions to the standard texture had no influence on ‘traditional’ vMMN and elicited no ‘genuine’ vMMN. However, the deviant texture elicited ‘genuine’ vMMN if the orientation of a continuously present rectangle was different from the standard (and identical to the deviant) texture orientation. Our results suggest that the direction of voluntary hand movement and the orientation of task-irrelevant visual patterns do not acquire common memory representation, but a continuously present object contributes to the detection of sequential regularity violation.

A Recognition Method for Hand Motion Direction Based on Charge Induction

To develop a cost-efficient technique of hand control, one novel method based on charge induction is proposed to recognize hand motion direction by applying three charge/potential electrodes. Firstly, the theoretical derivation of charge detection for moving target is introduced and experiment verification of hand motion is given. Then, the direction recognition model is established and the accuracy analysis is illustrated, which is found that the detection error is decreased with the decrease of the time measurement error and the motion velocity. Finally, the experiments for hand motion direction recognition are carried out under conditions of full occlusion, darkness and body motion interference. Without the need of wearing device on the hand, the proposed method can achieve non-contact recognition for hand motion direction under the conditions of full occlusion and darkness, which is one outstanding characteristic, especially for control applications like in smart home.