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Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one's brain and behaviour. Stress is an established risk factor in SUD's development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.

The hypothesis that the central nervous system (CNS) makes use of synergies or movement primitives in achieving simple to complex movements has inspired the investigation of different types of synergies. Kinematic and muscle synergies have been extensively studied in the literature, but only a few studies have compared and combined both types of synergies during the control and coordination of the human hand. In this paper, synergies were extracted first independently (called kinematic and muscle synergies) and then combined through data fusion (called musculoskeletal synergies) from 26 activities of daily living in 22 individuals using principal component analysis (PCA) and independent component analysis (ICA). By a weighted linear combination of musculoskeletal synergies, the recorded kinematics and the recorded muscle activities were reconstructed. The performances of musculoskeletal synergies in reconstructing the movements were compared to the synergies reported previously in the literature by us and others. The results indicate that the musculoskeletal synergies performed better than the synergies extracted without fusion. We attribute this improvement in performance to the musculoskeletal synergies that were generated on the basis of the cross-information between muscle and kinematic activities. Moreover, the synergies extracted using ICA performed better than the synergies extracted using PCA. These musculoskeletal synergies can possibly improve the capabilities of the current methodologies used to control high dimensional prosthetics and exoskeletons.

Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed.

Stress is an established risk factor in the development of addiction and in reinstating drug seeking. Substance use disorder (SUD) is a dangerous epidemic that affects the brain and behavior. Despite this growing epidemic and its subsequent consequences, there are limited management and treatment options, pharmacotherapies and psychosocial treatments available. To this end, there is a need for new and improved personalized devices and treatments for the detection and management of SUD. Based on documented negative effects of stress in SUD, in this paper, our objective was to select a few significant physiological features from a set of 8 features collected by a chest-worn RespiBAN Professional in 15 individuals. We used three machine learning classifiers on these optimal physiological features to detect stress. Our results indicate that best accuracies were achieved when electrodermal activity (EDA), body temperature and chest-worn accelerometer were considered as features for the classification. Challenges, implications and applications were discussed. In the near future, the proposed methods will be replicated in individuals with SUD.

Investigations on how the central nervous system (CNS) effortlessly conducts complex hand movements have led to an extensive study of synergies or movement primitives. Of the different types of hand synergies, kinematic and muscle synergies have been widely studied in literature, but only a few studies have fused both. In this paper kinematic and muscle activities recorded from the activities of daily living were first fused and then dimensionally reduced through principal component analysis (PCA). By using these principal components or musculoskeletal synergies in a weighted linear combination, the recorded kinematics and muscle activities were reconstructed. The performance of these musculoskeletal synergies in reconstructing the movements was compared to the kinematic and muscle synergies reported previously in the literature by us and others. The results from these findings indicate that musculoskeletal synergies perform better than the synergies extracted without fusion. These newly demonstrated musculoskeletal synergies might improve neural control of robotics, prosthetics and exoskeletons. Clinical Relevance- In this paper, musculoskeletal synergies were extracted from the fusion of kinematic and muscle activities recorded from the activities of daily living. These newly demonstrated musculoskeletal synergies might enhance our understanding of neural control of robotics, prosthetics and exoskeletons.

Hand prehension requires a highly coordinated control of contact forces. The high dimensional sensorimotor system of the human hand although operates at ease, poses several challenges when replicated for prosthetic control. This study investigates how the dynamical synergies, coordinated spatial patterns of contact forces, contribute to the contact forces in a grasp, and whether the dynamical synergies could potentially serve as candidates for feedforward and feedback mechanisms. Ten right-handed subjects were recruited to grasp and hold mass-varied objects. The contact forces during this multidigit prehension were recorded using an instrumented grip glove. The dynamical synergies were derived using principal component analysis (PCA). The contact force patterns during the grasps were reconstructed using the first few synergies. The significance of the dynamical synergies and the current challenges and possible applications of the dynamical synergies were discussed along with the integration of the dynamical synergies into prosthetics and exoskeletons that can possibly enable near-natural control. This research presents dynamical synergies observed in contact forces during hand grasps. These dynamical synergies could help in improving feedforward force control and sensory feedback in hand prosthetics and exoskeletons.

Hand prehension requires highly coordinated control of contact forces. The high-dimensional sensorimotor system of the human hand operates at ease, but poses several challenges when replicated in artificial hands. This paper investigates how the dynamical synergies, coordinated spatiotemporal patterns of contact forces, contribute to the hand grasp, and whether they could potentially capture the force primitives in a low-dimensional space. Ten right-handed subjects were recruited to grasp and hold mass-varied objects. The contact forces during this multidigit prehension were recorded using an instrumented grip glove. The dynamical synergies were derived using principal component analysis (PCA). The contact force patterns during the grasps were reconstructed using the first few synergies. The significance of the dynamical synergies, the influence of load forces and task configurations on the synergies were explained. This study also discussed the contribution of biomechanical constraints on the first few synergies and the current challenges and possible applications of the dynamical synergies in the design and control of exoskeletons. The integration of the dynamical synergies into exoskeletons will be realized in the near future.

In this paper, hand synergies were derived using independent component analysis (ICA) and compared against synergies derived from our previous methods using principal component analysis (PCA). For ICA, we used two algorithms - Infomax and entropy bound minimization (EBM). For all the methods, the synergies were extracted from rapid hand grasps. The extracted synergies were then tested for generalizability in reconstructing natural hand grasps and American Sign Language (ASL) postures that were different from rapid grasps. The results indicate that the synergies derived from ICA were able to generalize only marginally better when compared to those from PCA. Among the two ICA methods, Infomax performed slightly better in yielding lower reconstruction error while EBM performed better in sparse selection of synergies. The implications and future scope were discussed.

Human hands are versatile biomechanical architectures that can perform simple movements such as grasping to complicated movements such as playing a musical instrument. Such extremely dependable and useful parts of the human body can be debilitated due to movement disorders such as Parkinson's disease, stroke, spinal cord injury, multiple sclerosis and cerebral palsy. In such cases, precisely measuring the residual or abnormal hand function becomes a critical assessment to help clinicians and physical therapists in diagnosis, treatment and in prescribing appropriate prosthetics or rehabilitation therapies. The current methodologies used to measure abnormal or residual hand function are either paperbased scales that are prone to human error or expensive motion tracking systems. The cost and complexity restrict the usability of these methods in clinical environments. In this paper we present a low-cost instrumented glove that can measure kinematics and dynamics of human hand, by leveraging the recent advances in 3D printing technologies and flexible sensors.

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.

This paper outlines the construction, current state, and future goals of HERCULES, a three degree-of-freedom (DoF) pneumatically actuated exoskeleton for stroke rehabilitation. The exoskeleton arm is capable of joint-angle control at the elbow in flexion and extension, at the shoulder in flexion and extension, and at the shoulder in abduction and adduction. In the near future we plan to embed kinematic synergies into the control system architecture of this arm to gain dexterous and near-natural movements.Clinical Relevance- This device can be used as an upper limb rehabilitation testbed for individuals with complete or partial upper limb paralysis. In the future, this system can be used to train individuals on synergy-based rehabilitation protocols.

Soft hand exoskeletons offer a lightweight, low-profile alternative to rigid rehabilitative robotic systems, enabling their use to restore activities of daily living (ADL) in those with hand paresis due to stroke or other conditions. The hand exoskeleton with embedded synergies (HEXOES) is a soft cable-driven hand exoskeleton capable of independently actuating and sensing 10 degrees of freedom (DoF) of the hand. Control of the 10 DoF exoskeleton is dimensionally reduced using three manually defined synergies in software corresponding to thumb, index, and 3-finger flexion and extension. In this paper, five healthy subjects control HEXOES using a neural network which decodes synergy weights from contralateral electromyography (EMG) activity. The three synergies are manipulated in real time to grasp and lift 15 ADL objects of various sizes and weights. The neural network's training and validation mean squared error, object grasp time, and grasp success rate were measured for five healthy subjects. The final training error of the neural network was 4.8 ± 1.8% averaged across subjects and tasks, with 8.3 ± 3.4% validation error. The time to reach, grasp, and lift an object was 11.15 ± 4.35 s on average, with an average success rate of 66.7% across all objects. The complete system demonstrates real time use of biosignals and machine learning to allow subjects to operate kinematic synergies to grasp objects using a wearable hand exoskeleton. Future work and applications are further discussed, including possible design improvements and enrollment of individuals with stroke.

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.

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.

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.

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.

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 l 1 -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.

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.

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.

After spinal cord injury (SCI), motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation (AO), in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, AO can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI) even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment. Previous studies have reported congruent motor cortical activity during observed and overt movements using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Recent single-unit studies using intracortical microelectrodes also demonstrated that a large number of motor cortical neurons had similar firing rate patterns between overt and observed movements. Given the increasing interest in electrocorticography (ECoG)-based BCIs, our goal was to identify whether action observation-related cortical activity could be recorded using ECoG during grasping tasks. Specifically, we aimed to identify congruent neural activity during observed and executed movements in both the sensorimotor rhythm (10-40 Hz) and the high-gamma band (65-115 Hz) which contains significant movement-related information. We observed significant motor-related high-gamma band activity during AO in both able-bodied individuals and one participant with a complete C4 SCI. Furthermore, in able-bodied participants, both the low and high frequency bands demonstrated congruent activity between action execution and observation. Our results suggest that AO could be an effective and critical procedure for deriving the mapping from ECoG signals to intended movement for an ECoG-based BCI system for individuals with paralysis.

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.

This paper demonstrates a synergy-based brain-machine interface that uses low-dimensional command signals to control a high dimensional virtual hand. First, temporal postural synergies were extracted from the angular velocities of finger joints of five healthy subjects when they performed hand movements that were similar to activities of daily living. Two synergies inspired from the extracted synergies, namely, two-finger pinch and whole-hand grasp, were used in real-time brain control, where a virtual hand with 10 degrees of freedom was controlled to grasp or pinch virtual objects. These two synergies were controlled by electrocorticographic (ECoG) signals recorded from two electrodes of an electrode array that spanned motor and speech areas of an individual with intractable epilepsy, thus demonstrating closed loop control of a synergy-based brain-machine interface.