Transient Response of Basal Ganglia Network in Healthy and Low-Dopamine State.

The basal ganglia (BG) are crucial for a variety of motor and cognitive functions. Changes induced by persistent low-dopamine (e.g., in Parkinson's disease; PD) result in aberrant changes in steady-state population activity (ß band oscillations) and the transient response of the BG. Typically, a brief cortical stimulation results in a triphasic response in the substantia nigra pars reticulata (SNr; an output of the BG). The properties of the triphasic responses are shaped by dopamine levels. While mechanisms underlying aberrant steady state activity are well studied, it is still unclear which BG interactions are crucial for the aberrant transient responses in the BG. Moreover, it is also unclear whether mechanisms underlying the aberrant changes in steady-state activity and transient response are the same. Here, we used numerical simulations of a network model of BG to identify the key factors that determine the shape of the transient responses. We show that an aberrant transient response of the SNr in the low-dopamine state involves changes in the direct pathway and the recurrent interactions within the globus pallidus externa (GPe) and between GPe and subthalamic nucleus (STN). However, the connections from D2-type spiny projection neurons (D2-SPN) to GPe are most crucial in shaping the transient response and by restoring them to their healthy level, we could restore the shape of transient response even in low-dopamine state. Finally, we show that the changes in BG that result in aberrant transient response are also sufficient to generate pathologic oscillatory activity in the steady state.

Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal.

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.

Musculoskeletal modeling to predict and reduce Antetrior Cruciate Ligament injury during single leg drop jump activity: Synergistic muscle co-activation approach.

This paper presents a `drop jump' modeling to study the effect of synergistic muscle activation on controlling Anterior Cruciate Ligament (ACL) injury. ACL injuries are mostly caused during high impact loading. A full body musculoskeletal model with knee ligaments have been developed in `OpenSim platform' to simulate ACL injury during drop jump activity. The model is used to quantify the effect of change in muscle activation on different kinetic and kinematic parameters, which are associated with ACL injury. A neuromusculoskeletal controller have been designed which selects optimal muscle activation of Quadriceps, Hamstrings, Gastrocnemius and Tibilias anterior muscle group so as to reduce the chance of ACL injury and ankle inversion risk while jumping from elevated platforms. The OpenSim model along with the neuro-muscular controller forms an injury `predict-adapt' system, which can be useful in designing specific training sessions for athletics or for planning personalized rehabilitation therapy.

Evaluating Age-Related Variations of Gaze Behavior for a Novel Digitized-Digit Symbol Substitution Test.

Analysis of cognitive functioning from gaze behavior might serve as an early indicator of age related decline of cognitive functions. Standard psychological tests like the digit-symbol substitution test or the symbol-digit modalities test is used exclusively in this regard. In this paper, we have designed and developed a digitized version of the digit symbol substitution test. Three different versions have been designed in order to derive deeper insights of the user behavior. The test-retest validation of the versions reveals good correlation across sessions. Further, the difference in gaze behavior which might be used as an indicator of cognitive functions is tested for two different age groups (13 participants <30 years and 11 participants >40 years). It is seen that the designed digitized version along with the usage of physiological markers like eye tracking bestows additional information and is sensitive to age related factors which might be used for the assessment as well as for the training purpose in rehabilitation systems. Results show that the performance can be analyzed using gaze and pupillometric features in addition to the conventional test performance metrics. We derived an index to measure the performance related to visuo-spatial functioning on one of the designed versions of the test. Results of this index on the number of fixations for two age groups are found to be separated in a statistically significant (p<0.05) manner. The age related difference (p<0.05) is also evident in the pupillometric responses obtained.

Eye movements - an early marker of cognitive dysfunctions.

Cognitive impairments or dysfunctions are one of the major issues of aging population and medical conditions like brain damage, stroke etc. Assessment of cognitive functioning is usually done by medical practitioners using various standard psychological tests which require expert interventions. In the present study we have tried to use eye tracking as a possible option for assessment of cognitive functions while executing a digitized digit symbol substitution task. The standard task has been modified so that more detail insights of one's cognitive state can be obtained without any manual intervention. Results show that it is possible to get more information like attention, perception etc. It also gives insight about the performance of an individual and can be used for analyzing the executive functions like processing speed, memory etc.

Eigen Posture Based Fall Risk Assessment System Using Kinect.

Postural Instability (PI) is a major reason for fall in geriatric population as well as for people with diseases or disorders like Parkinson's, stroke etc. Conventional stability indicators like Berg Balance Scale (BBS) require clinical settings with skilled personnel's interventions to detect PI and finally classify the person into low, mid or high fall risk categories. Moreover these tests demand a number of functional tasks to be performed by the patient for proper assessment. In this paper a machine learning based approach is developed to determine fall risk with minimal human intervention using only Single Limb Stance exercise. The analysis is done based on the spatiotemporal dynamics of skeleton joint positions obtained from Kinect sensor. A novel posture modeling method has been applied for feature extraction along with some traditional time domain and metadata features to successfully predict the fall risk category. The proposed unobstrusive, affordable system is tested over 224 subjects and is able to achieve 75% mean accuracy on the geriatric and patient population.

Enhancing the usability of low-cost eye trackers for rehabilitation applications.

Eye tracking is one of the most widely used technique for assessment, screening and human-machine interaction related applications. There are certain issues which limit the usage of eye trackers in practical scenarios, viz., i) need to perform multiple calibrations and ii) presence of inherent noise in the recorded data. To address these issues, we have proposed a protocol for one-time calibration against the "regular" or the "multiple" calibration phases. It is seen that though it is always desirable to perform multiple calibration, the one-time calibration also produces comparable results and might be better for individuals who are not able to perform multiple calibrations. In that case, "One-time calibration" can also be done by a participant and the calibration results are used for the rest of the participants, provided the chin rest and the eye tracker positions are unaltered. The second major issue is the presence of the inherent noise in the raw gaze data, leading to systematic and variable errors. We have proposed a signal processing chain to remove these two types of errors. Two different psychological stimuli-based tasks, namely, recall-recognition test and number gazing task are used as a case study for the same. It is seen that the proposed approach gives satisfactory results even with one-time calibration. The study is also extended to test the effect of long duration task on the performance of the proposed algorithm and the results confirm that the proposed methods work well in such scenarios too.

Affordable sensor based gaze tracking for realistic psychological assessment.

Eye movement analysis finds tremendous usefulness in various medical screening applications and rehabilitation. Infrared sensor based eye trackers are becoming popular but these are expensive and need repeated calibration. Moreover, with multiple calibration also, there persists some noises called, variable and systematic, resulting in inaccurate gaze tracking. This study aims to build an one time calibration module to avoid the overhead of multiple calibration and to design an algorithm to remove both the types of errors effectively. The proposed approach is used for correcting the gaze tracking data for Digit Gazing task and standard recall-recognition test, where an accuracy of 90% and 82% are achieved respectively for detecting the gaze positions against the raw eye gaze data. Results also show that it is possible to perform accurate gaze tracking with one-time calibration method provided the experimental setup is not altered.

Posturography stability score generation for stroke patient using Kinect: Fuzzy based approach.

Aim of this paper is to formulate a posturography stability score for stroke patients using fuzzy logic. Postural instability is one of the prominent symptoms of stroke, dementia, parkinsons disease, myopathy, etc. and is the major precursor of fall. Conventional scoring techniques used to assess postural stability require manual intervention and are dependent on live interaction with physiotherapist. We propose a novel scoring technique to calculate static stability of a person using posturography features acquired by Kinect sensor, which do not require any manual intervention or expert guidance, is cost effective and hence are ideal for tele rehabilitation purpose. Stability analysis is done during Single Limb Stance (SLS) exercise. Kinect sensor is used to calculate three features, naming SLS duration, vibration index, calculated from mean vibration of twenty joints and sway area of Centre of Mass (CoM). Based on the variation of these features, a fuzzy rule base is generated which calculates a static stability score. One way analysis of variance (Anova) between a group of stroke population and healthy individuals under study validates the reliability of the proposed scorer. Generated fuzzy score are comparable with standard stability scorer like Berg Balance scale and fall risk assessment tool like Johns Hopkins scale. Stability score, besides providing an index of overall stability can also be used as a fall predictability index.

Analysis of muscle activation in lower extremity for static balance.

Balance plays an important role for human bipedal locomotion. Degeneration of balance control is prominent in stroke patients, elderly adults and even for majority of obese people. Design of personalized balance training program, in order to strengthen muscles, requires the analysis of muscle activation during an activity. In this paper we have proposed an affordable and portable approach to analyze the relationship between the static balance strategy and activation of various lower extremity muscles. To do that we have considered Microsoft Kinect XBox 360 as a motion sensing device and Wii balance board for measuring external force information. For analyzing the muscle activation pattern related to static balance, participants are asked to do the single limb stance (SLS) exercise on the balance board and in front of the Kinect. Static optimization to minimize the overall muscle activation pattern is carried out using OpenSim, which is an open-source musculoskeletal simulation software. The study is done on ten normal and ten obese people, grouped according to body mass index (BMI). Results suggest that the lower extremity muscles like biceps femoris, psoas major, sartorius, iliacus play the major role for both maintaining the balance using one limb as well as maintaining the flexion of the other limb during SLS. Further investigations reveal that the higher muscle activations of the flexed leg for normal group demonstrate higher strength. Moreover, the lower muscle activation of the standing leg for normal group demonstrate more headroom for the biceps femoris-short-head and psoas major to withstand the load and hence have better static balance control.

Neuromechanical Cost Functionals Governing Motor Control for Early Screening of Motor Disorders.

Developing a quantifier of the neural control of motion is extremely useful in characterizing motor disorders and personalizing the model equations using data. We approach this problem using a top-down optimal control methodology, with an aim that the quantity estimated from the collected data is representative of the underlying neural controller. For this purpose, we assume that during the flexion of an arm, human brain optimizes a functional. A functional is defined as a function of a function that returns a scalar. Generally, in forward problems, this functional is chosen to be a function of metabolic energy spent, jerkiness, variance of motion, etc., integrated throughout the trajectory of motion. Current states (angular configuration and velocity) and torque applied can approximately determine the motion of a joint. Therefore, any internal cost functional optimized by the brain has to have its effect in the control of the torque. In this work, we study the flexion of the arm in normals and patient groups and intend to find the cost functionals governing the motion. To achieve this, we parametrize the cost functional governing the motion into the variables θp and ωp , which are then estimated using marker data obtained from the subjects. These parameters are shown to vary significantly for the normal and patient populations. The θp values were shown to be significantly higher in the case of patients than in the case of normals and ωp values showed an exactly opposite trend. We also studied how these cost functionals govern the applied torques in both subject groups and how is it affected while perturbed with sinusoidal frequencies. A time frequency analysis of the resulting solutions revealed a distinguishing pattern for the normals compared with the patient groups.

Accurate upper body rehabilitation system using kinect.

The growing importance of Kinect as a tool for clinical assessment and rehabilitation is due to its portability, low cost and markerless system for human motion capture. However, the accuracy of Kinect in measuring three-dimensional body joint center locations often fails to meet clinical standards of accuracy when compared to marker-based motion capture systems such as Vicon. The length of the body segment connecting any two joints, measured as the distance between three-dimensional Kinect skeleton joint coordinates, has been observed to vary with time. The orientation of the line connecting adjoining Kinect skeletal coordinates has also been seen to differ from the actual orientation of the physical body segment. Hence we have proposed an optimization method that utilizes Kinect Depth and RGB information to search for the joint center location that satisfies constraints on body segment length and as well as orientation. An experimental study have been carried out on ten healthy participants performing upper body range of motion exercises. The results report 72% reduction in body segment length variance and 2° improvement in Range of Motion (ROM) angle hence enabling to more accurate measurements for upper limb exercises.