Extraction of sources of tremor in hand movements of patients with movement disorders

A review and consideration on the kinematics of reach-to-grasp movements in macaque monkeys

The bases for understanding the neuronal mechanisms that underlie the control of reach-to-grasp movements among nonhuman primates, particularly macaques, has been widely studied. However, only a few kinematic descriptions of their prehensile actions are available. A thorough understanding of macaques' prehensile movements is manifestly critical, in light of their role in biomedical research as valuable models for studying neuromotor disorders and brain mechanisms, as well as for developing brain-machine interfaces to facilitate arm control. This article aims to review the current state of knowledge on the kinematics of grasping movements that macaques perform in naturalistic, seminaturalistic, and laboratory settings, to answer the following questions: Are kinematic signatures affected by the context within which the movement is performed? In what ways are kinematics of humans' and macaques' prehensile actions similar/dissimilar? Our analysis reflects the challenges involved in making comparisons across settings and species due to the heterogeneous picture in terms of the number of subjects, stimuli, conditions, and hands used. The kinematics of free-ranging macaques are characterized by distinctive features that are exhibited neither by macaques in laboratory setting nor by human subjects. The temporal incidence of key kinematic landmarks diverges significantly between species, indicating disparities in the overall organization of movement. Given such complexities, we attempt a synthesis of the extant body of evidence, intending to generate some significant implications for directions that future research might take to recognize the remaining gaps and pursue the insights and resolutions to generate an interpretation of movement kinematics that accounts for all settings and subjects.

Data glove embedded with 9-axis IMU and force sensing sensors for evaluation of hand function

A hand injury can greatly affect a person's daily life. Physicians must evaluate the state of recovery of a patient's injured hand. However, current manual evaluations of hand functions are imprecise and inconvenient. In this paper, a data glove embedded with 9-axis inertial sensors and force sensitive resistors is proposed. The proposed data glove system enables hand movement to be tracked in real-time. In addition, the system can be used to obtain useful parameters for physicians, is an efficient tool for evaluating the hand function of patients, and can improve the quality of hand rehabilitation.

Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson disease

Nowadays, the increasing old population 65+ as well as the pace imposed by work activities lead to a high number of people that have particular injuries for limbs. In addition to persistent or temporary disabilities related to accidental injuries we must take into account that part of the population suffers from motor deficits of the hands due to stroke or diseases of various clinical nature. The most recurrent technological solutions to measure the rehabilitation or skill motor performance of the hand are glove-based devices, able to faithfully capture the movements of the hand and fingers. This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients' hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of Parkinson's disease.

Comparison of Baseline Tremor Under Various Microsurgical Conditions

This paper presents the characterization and comparison of physiological tremor for pointing tasks in multiple environments, as a baseline for performance evaluation of microsurgical robotics. Previous studies have examined the characteristics of physiological tremor under laboratory settings as well as different operating conditions. However, different test methods make the comparison of results across trials and conditions difficult. Two vitroretinal microsurgeons were evaluated while performing a pointing task with no entry-point constraint, constrained by an artificial eye model, and constrained by a rabbit eye in vivo. For the three respective conditions the 3D RMS positioning error was 144 μm, 258 μm, and 285 μm, and maximum 3D error was 349 μm, 647 μm, and 696 μm. A spectral analysis was also performed, confirming a distinct peak near in the 6-12 Hz frequency range, characteristic of hand tremor during tasks in all three environments.

Dynamic variability of isometric action tremor in precision pinching

Evolutionary development of isometric force impulse frequencies, power, and the directional concordance of changes in oscillatory tremor during performance of a two-digit force regulation task was examined. Analyses compared a patient group having tremor confounding volitional force regulation with a control group having no neuropathological diagnosis. Dependent variables for tremor varied temporally and spatially, both within individual trials and across trials, across individuals, across groups, and between digits. Particularly striking findings were magnitude increases during approaches to cue markers and shifts in the concordance phase from pinching toward rigid sway patterns as the magnitude increased. Magnitudes were significantly different among trace line segments of the task and were characterized by differences in relative force required and by the task progress with respect to cue markers for beginning, reversing force change direction, or task termination. The main systematic differences occurred during cue marker approach and were independent of trial sequence order.

Subcortical neuronal ensembles: an analysis of motor task association, tremor, oscillations, and synchrony in human patients

Deep brain stimulation (DBS) has expanded as an effective treatment for motor disorders, providing a valuable opportunity for intraoperative recording of the spiking activity of subcortical neurons. The properties of these neurons and their potential utility in neuroprosthetic applications are not completely understood. During DBS surgeries in 25 human patients with either essential tremor or Parkinson's disease, we acutely recorded the single-unit activity of 274 ventral intermediate/ventral oralis posterior motor thalamus (Vim/Vop) neurons and 123 subthalamic nucleus (STN) neurons. These subcortical neuronal ensembles (up to 23 neurons sampled simultaneously) were recorded while the patients performed a target-tracking motor task using a cursor controlled by a haptic glove. We observed that modulations in firing rate of a substantial number of neurons in both Vim/Vop and STN represented target onset, movement onset/direction, and hand tremor. Neurons in both areas exhibited rhythmic oscillations and pairwise synchrony. Notably, all tremor-associated neurons exhibited synchrony within the ensemble. The data further indicate that oscillatory (likely pathological) neurons and behaviorally tuned neurons are not distinct but rather form overlapping sets. Whereas previous studies have reported a linear relationship between power spectra of neuronal oscillations and hand tremor, we report a nonlinear relationship suggestive of complex encoding schemes. Even in the presence of this pathological activity, linear models were able to extract motor parameters from ensemble discharges. Based on these findings, we propose that chronic multielectrode recordings from Vim/Vop and STN could prove useful for further studying, monitoring, and even treating motor disorders.

Toward synergy-based brain-machine interfaces

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.

Estimation of physiological tremor from accelerometers for real-time applications

Accurate filtering of physiological tremor is extremely important in robotics assisted surgical instruments and procedures. This paper focuses on developing single stage robust algorithms for accurate tremor filtering with accelerometers for real-time applications. Existing methods rely on estimating the tremor under the assumption that it has a single dominant frequency. Our time-frequency analysis on physiological tremor data revealed that tremor contains multiple dominant frequencies over the entire duration rather than a single dominant frequency. In this paper, the existing methods for tremor filtering are reviewed and two improved algorithms are presented. A comparative study is conducted on all the estimation methods with tremor data from microsurgeons and novice subjects under different conditions. Our results showed that the new improved algorithms performed better than the existing algorithms for tremor estimation. A procedure to separate the intended motion/drift from the tremor component is formulated.

Real-time estimation of pathological tremor parameters from gyroscope data

This paper presents a two stage algorithm for real-time estimation of instantaneous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading.

Temporal postural synergies of the hand in rapid grasping tasks

Postural synergies of the hand have been widely proposed in the literature, but only a few attempts were made to visualize temporal postural synergies, i.e., profiles of postural synergies varying over time. This paper aims to derive temporal postural synergies from kinematic synergies extracted from joint angular velocity profiles of rapid grasping movements. The rapid movements constrain the kinematic synergies to combine instantaneously, and thus, the movements can be approximated by a weighted summation of synchronous synergies. After being extracted by using singular value decomposition, the synchronous kinematic synergies were translated into temporal postural synergies, which revealed strategies of enslaving, metacarpal flexion for larger movements, and hierarchical recruitment of joints, adapted by subjects while grasping.

Dimensionality reduction in control and coordination of the human hand

The concept of kinematic synergies is proposed to address the dimensionality reduction problem in control and coordination of the human hand. This paper develops a method for extracting kinematic synergies from joint-angular-velocity profiles of hand movements. Decomposition of a limited set of synergies from numerous movements is a complex optimization problem. This paper splits the decomposition process into two stages. The first stage is to extract synergies from rapid movement tasks using singular value decomposition (SVD). A bank of template functions is then created from shifted versions of the extracted synergies. The second stage is to find weights and onset times of the synergies based on l(1) -minimization, whose solutions provide sparse representations of hand movements using synergies.