Lateralization and Model Transference in a Bilateral Cursor Task.

Post-stroke rehabilitation, occupational and physical therapy, and training for use of assistive prosthetics leverages our current understanding of bilateral motor control to better train individuals. In this study, we examine upper limb lateralization and model transference using a bimanual joystick cursor task with orthogonal controls. Two groups of healthy subjects are recruited into a 2-session study spaced seven days apart. One group uses their left and right hands to control cursor position and rotation respectively, while the other uses their right and left hands. The groups switch control methods in the second session, and a rotational perturbation is applied to the positional controls in the latter half of each session. We find agreement with current lateralization theories when comparing robustness to feedforward perturbations in feedback and feedforward measures. We find no evidence of a transferable model after seven days, and evidence that the brain does not synchronize task completion between the hands.

Physiological Monitoring During Augmented Reality Exercise Confirms Advantages to Health and Well-Being.

Augmented reality (AR) is a versatile tool that is changing the way we interact with the world. One way is through exergaming, integrating AR, and gaming with traditional exercise programs. This study aimed to test three different AR systems to determine their ability to elicit physiological responses similar to those experienced while exercising or performing rehabilitation exercises. The systems tested included a head-mounted display experience (Teapots), a Sony EyeToy game (Slap Stream), and a prototype of a PC-camera-based system (Drums). Physiological recordings show that Slap Stream and Drums produced significant increases (p < 0.05) in heart rate (HR) and skin conductance, and all three games produced nonsignificant increase in respiratory rate. Of the three games, Slap Stream produced the highest rates of low- (∼28 percent), moderate- (17 percent), and high-intensity (∼22 percent) HRs. This study successfully showed that inexpensive easy-to-use AR systems can effectively contribute to exercise programs. Future game design recommendations, including feedback, mixed reality, and stress tracking, are discussed.

Decoding Synergy-Based Hand Movements using Electroencephalography.

In this paper, scalp electroencephalographic (EEG) signals were recorded from 10 subjects during hand grasping. Six objects that span different grasp types were used. Grasp kinematics were recorded using CyberGlove. From a training subset of the data, kinematic synergies were determined and their reconstruction weights in these grasps were calculated. EEG features (power spectral densities in four low and high frequency bands) were trained on kinematic synergy weights using multivariate linear regression. Using this model, kinematics from testing subset of data were decoded from EEG with 3-fold cross validation. Results are compared to chance level to determine if reconstruction weights are related to EEG features. Results indicate that EEG features can decode synergy-based movement generation. Study implications and future implementations were discussed.

Towards a wearable hand exoskeleton with embedded synergies.

Numerous hand exoskeletons have been proposed in the literature with the aim of assisting or rehabilitating victims of stroke, brain/spinal cord injury, or other causes of hand paralysis. In this paper a new 3D printed soft hand exoskeleton, HEXOES (Hand Exoskeleton with Embedded Synergies), is introduced and mechanically characterized. Metacarpophalangeal (MCP) and proximal interphalangeal/interphalangeal (PIP/IP) joints had measured maximum flexion angles of 53.7 ± 16.9° and 39.9 ± 13.4°, respectively; and maximum MCP and PIP angular velocities of 94.5 ± 41.9 degrees/s and 74.6 ± 67.3 degrees/s, respectively. These estimates indicate that the mechanical design has range of motion and angular velocity characteristics that meet the requirements for synergy-based control. When coupled with the proposed control loop, HEXOES can be used in the future as a test-bed for synergy-based clinical hand rehabilitation.

Hand Grasping Synergies As Biometrics.

Recently, the need for more secure identity verification systems has driven researchers to explore other sources of biometrics. This includes iris patterns, palm print, hand geometry, facial recognition, and movement patterns (hand motion, gait, and eye movements). Identity verification systems may benefit from the complexity of human movement that integrates multiple levels of control (neural, muscular, and kinematic). Using principal component analysis, we extracted spatiotemporal hand synergies (movement synergies) from an object grasping dataset to explore their use as a potential biometric. These movement synergies are in the form of joint angular velocity profiles of 10 joints. We explored the effect of joint type, digit, number of objects, and grasp type. In its best configuration, movement synergies achieved an equal error rate of 8.19%. While movement synergies can be integrated into an identity verification system with motion capture ability, we also explored a camera-ready version of hand synergies-postural synergies. In this proof of concept system, postural synergies performed well, but only when specific postures were chosen. Based on these results, hand synergies show promise as a potential biometric that can be combined with other hand-based biometrics for improved security.

Synergy Repetition Training versus Task Repetition Training in Acquiring New Skill.

Traditionally, repetitive practice of a task is used to learn a new skill, exhibiting as immediately improved performance. Research suggests, however, that a more experience-based rather than exposure-based training protocol may allow for better transference of the skill to related tasks. In synergy-based motor control theory, fundamental motor skills, such as hand grasping, are represented with a synergy subspace that captures essential motor patterns. In this study, we propose that motor-skill learning through synergy-based mechanisms may provide advantages over traditional task repetition learning. A new task was designed to highlight the range of motion and dexterity of the human hand. Two separate training strategies were tested in healthy subjects: task repetition training and synergy training versus a control. All three groups showed improvements when retested on the same task. When tested on a similar, but different set of tasks, only the synergy group showed improvements in accuracy (9.27% increase) compared to the repetition (3.24% decline) and control (3.22% decline) groups. A kinematic analysis revealed that although joint angular peak velocities decreased, timing benefits stemmed from the initial feed-forward portion of the task (reaction time). Accuracy improvements may have derived from general improved coordination among the four involved fingers. These preliminary results warrant further investigation of synergy-based motor training in healthy individuals, as well as in individuals undergoing hand-based rehabilitative therapy.

Low-Dimensional Synergistic Representation of Bilateral Reaching Movements.

Kinematic and neuromuscular synergies have been found in numerous aspects of human motion. This study aims to determine how effectively kinematic synergies in bilateral upper arm movements can be used to replicate complex activities of daily living (ADL) tasks using a sparse optimization algorithm. Ten right-handed subjects executed 18 rapid and 11 natural-paced ADL tasks requiring bimanual coordination while sitting at a table. A position tracking system was used to track the subjects' arms in space, and angular velocities over time for shoulder abduction, shoulder flexion, shoulder internal rotation, and elbow flexion for each arm were computed. Principal component analysis (PCA) was used to generate kinematic synergies from the rapid-paced task set for each subject. The first three synergies accounted for 80.3 ± 3.8% of variance, while the first eight accounted for 94.8 ± 0.85%. The first and second synergies appeared to encode symmetric reaching motions which were highly correlated across subjects. The first three synergies were correlated between left and right arms within subjects, whereas synergies four through eight were not, indicating asymmetries between left and right arms in only the higher order synergies. The synergies were then used to reconstruct each natural-paced task using the l1-norm minimization algorithm. Temporal dilations of the synergies were introduced in order to model the temporal scaling of movement patterns achieved by the cerebellum and basal ganglia as reported previously in the literature. Reconstruction error was reduced by introducing synergy dilations, and cumulative recruitment of several synergies was significantly reduced in the first 10% of training task time by introducing temporal dilations. The outcomes of this work could open new scenarios for the applications of postural synergies to the control of robotic systems, with potential applications in rehabilitation. These synergies not only help in providing near-natural control but also provide simplified strategies for design and control of artificial limbs. Potential applications of these bilateral synergies were discussed and future directions were proposed.

QAPD: an integrated system to quantify symptoms of Parkinson's disease.

The complex prevalence of Parkinson's disease (PD) symptoms has pushed research towards assessment tools that can assist in their quantification. There remains a need for a system capable of measuring symptoms during various tasks at multiple motor levels (kinematics and electromyography). In this paper, we present the development and initial validation of a quantitative assessment tool for Parkinson's disease (QAPD), a system designed to assist researchers and clinicians in the study of PD. The system integrates motion tracking, data gloves, and electromyography to collect movement related data from multiple body parts. As part of the system, a custom MATLAB® based toolbox has been designed to quantify bradykinesia, tremor, micrographia, and muscle rigidity using both standard and contemporary data analysis techniques. We believe this system can be a useful assessment tool to assist clinicians and researchers in diagnosing and estimating movement dysfunction in individuals with PD.

Effect of visual and tactile feedback on kinematic synergies in the grasping hand.

The human hand uses a combination of feedforward and feedback mechanisms to accomplish high degree of freedom in grasp control efficiently. In this study, we used a synergy-based control model to determine the effect of sensory feedback on kinematic synergies in the grasping hand. Ten subjects performed two types of grasps: one that included feedback (real) and one without feedback (memory-guided), at two different speeds (rapid and natural). Kinematic synergies were extracted from rapid real and rapid memory-guided grasps using principal component analysis. Synergies extracted from memory-guided grasps revealed greater preservation of natural inter-finger relationships than those found in corresponding synergies extracted from real grasps. Reconstruction of natural real and natural memory-guided grasps was used to test performance and generalizability of synergies. A temporal analysis of reconstruction patterns revealed the differing contribution of individual synergies in real grasps versus memory-guided grasps. Finally, the results showed that memory-guided synergies could not reconstruct real grasps as accurately as real synergies could reconstruct memory-guided grasps. These results demonstrate how visual and tactile feedback affects a closed-loop synergy-based motor control system.

Candidates for synergies: linear discriminants versus principal components.

Movement primitives or synergies have been extracted from human hand movements using several matrix factorization, dimensionality reduction, and classification methods. Principal component analysis (PCA) is widely used to obtain the first few significant eigenvectors of covariance that explain most of the variance of the data. Linear discriminant analysis (LDA) is also used as a supervised learning method to classify the hand postures corresponding to the objects grasped. Synergies obtained using PCA are principal component vectors aligned with dominant variances. On the other hand, synergies obtained using LDA are linear discriminant vectors that separate the groups of variances. In this paper, time varying kinematic synergies in the human hand grasping movements were extracted using these two diametrically opposite methods and were evaluated in reconstructing natural and American sign language (ASL) postural movements. We used an unsupervised LDA (ULDA) to extract linear discriminants. The results suggest that PCA outperformed LDA. The uniqueness, advantages, and disadvantages of each of these methods in representing high-dimensional hand movements in reduced dimensions were discussed.