Tata Consultancy Services Limited

Oishee Mazumder

Aniruddha Sinha

Debatri Chatterjee

Kingshuk Chakravarty

Dibyendu Roy

Sangheeta Roy

Arpan Pal

Avik Ghose

Sakyajit Bhattacharya

Rahul Dasharath Gavas

Sundeep Khandelwal

Abhijit Das

Srinivasa Chakravarthy Vaddadi

Dipayan Biswas

Shivam Gupta

Ayush Rai

Debatri Chetterjee

The present invention relates to a method and system for determining progression of atrial fibrillation (AF) based on hemodynamic metrics. In conventional CFD models, effect of the AF on a cardiovascular system is not modeled and evaluation of associated hemodynamic metrics and its effect on a Left Atrium (LA) dynamics is not considered. The method and system for determining progression of the AF based on the hemodynamic metrics, analyzes the effect of the AF on cardiovascular parameters of the LA and a left Ventricle (LV), for AF variations. A 3D-CFD model is modelled from a plurality of scan images of a heart of a subject and the AF variations are incorporated in a zero-dimensional (OD) lumped cardiovascular hemodynamic model along with a novel rhythm generator that are used for extracting a plurality of LA hemodynamic metrics of wall shear stress (WSS) that are possible indicators for progression of the AF.

Method and system for determining progression of atrial fibrillation based on hemodynamic metrics

This disclosure relates generally to a method and system that provides personalized neuro motor rehabilitation therapy using a musculoskeletal arm model. The arm model is personalized using anthropometric measures and further adapted to operate using an optimized set of muscle actuators considering associated redundancy. The method generates trajectories associated with reach motion profiles for each motion task utilizing joint kinematics and inverse dynamics by integrating forward dynamics and muscle synergy concepts to select the optimized set of muscle actuators. The generated trajectories are further ranked based on muscle synergy, minimum energy consumption and optimized trajectory to select rehabilitation therapy best suited for effective recovery. Conventional methods that work with neural dynamics in deriving muscle synergy are dependent on single tasks, leaving synergy variation with task variability unexplored. The present disclosure provides understanding of work space, task variability and synergy paradigm to derive conclusive control actions for aiding rehabilitation effectively.

Personalized neuromotor rehabilitation therapy for upper limb using a neuromusculoskeletal arm model

This disclosure relates generally to method and system for determining myocardial ischemia severity based on hemodynamic parameters estimation. Many patients suffer from myocardial ischemia due to narrowing of coronary artery resulting poor oxygen supply in cardiac muscles. The method includes receiving Electrophysiology (EP) signal from a simulated heart surface model to generate a single lead ECG template. The method further estimates hemodynamic parameters using a hemodynamic module based on the single lead ECG template and then estimates cardiac pressure-volume loop variables. The myocardial ischemia severity of the heart surface model is determined which includes one of moderate ischemia, severe ischemia and silent ischemia. Here, the cardiac source module is coupled with the hemodynamic module to determine cardiac transmembrane potential (TMP) of the heart surface model through contractility function. This method serves as a guidance platform for patient care such as synthetic data generation for disease classification pertaining to coronary artery.

Method and system for determining myocardial ischemia severity based on hemodynamic parameters estimation

This disclosure provides a simulation platform to study and perform predictive analysis on valvular heart disease, Mitral stenosis (MS) and provides a control approach to correct hemodynamic imbalances during MS conditions. Conventional approaches of valve repair or replacement are often associated with risk of thromboembolism, need for anticoagulation, prosthetic endocarditis, and impaired left ventricle function. The cardiovascular hemodynamics model of the present disclosure helps to create 'what if' conditions to study variations in different hemodynamic parameters like blood flow, aortic and ventricular pressure, etc. during normal and pathological conditions. An adaptive control system in conjunction with the hemodynamic cardiovascular system (CVS) is provided to handle hemodynamic disbalance during moderate to severe MS conditions. The adaptive controller is hypothesized in line with the neuromodulation approach and modulates left ventricular contractility and vagal tone to counter the symptoms associated with MS.

Neuromodulation based adaptive controller for mitral stenosis

This disclosure relates generally to health monitoring and assessment systems, and more particularly to perform postural stability assessment of a user and quantify the assessed postural stability. In an embodiment, the system, by monitoring specific actions (which are part of certain tests done for the postural stability assessment) being performed by a user, collects inputs which are then processed to determine SLS duration, the body joint vibration, and the body sway area of the user, while performing the tests. By processing the SLS duration, the body joint vibration, and the body sway area together, a postural stability index score for the user is determined, and based on this score, postural stability assessment for the user is performed.

Method and system for postural stability assessment

Conventionally, a neuronal controller located inside the central nervous system governing the maintenance of the upright posture of the human body is designed from a control system perspective using proportional-integral-derivative (PID) controllers, wherein human postural sway is modeled either along a sagittal plan or along a frontal plane separately resulting in limited insights on intricacies of a governing neuronal controller. Also, existing neuronal controllers using a reinforcement learning (RL) paradigm are based on complex actor-critic on-policy algorithms. Analyzing human postural sway is critical to detect markers for progression of balance impairments. The present disclosure facilitates modelling the neuronal controller using a simplified RL algorithm, capable of producing postural sway characteristics in both sagittal and frontal plane together. The Q-learning technique of the RL paradigm is employed for learning an optimal state-action value (Q-value) function for a tuneable Markov Decision Process (MDP) model.

Modeling a neuronal controller exhibiting human postural sway

This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.

Method and system for detecting Parkinson's disease progression

Collision avoidance and postural stability adjustment may provide an effective dual task paradigm to interpret the effect of proprioceptive adaptation on balance control. However, conventionally tasks are physical tasks performed under supervision in specific set up environments. Implementations of the present disclosure provide methods and systems for interpreting neural interplay involving proprioceptive adaptation in a lower limb during a dual task paradigm. The disclosed method provides a better interpreting of the neuronal mechanisms underlying adaptation and learning of skilled motor movement and to determine the relationship of lower limb proprioceptive sense and postural stability by simulating integration of a Single Limb Stance (SLS) functionality test for postural stability and a single limb collision avoidance task, in an adaptive Virtual Reality (VR) environment provided to a subject.

Method and system for interpreting neural interplay involving proprioceptive adaptation during a dual task paradigm

A system and method for risk prediction and corrective action for a contact type activity is provided. The method includes generating a personalized full body musculoskeletal model to depict the knee and ankle joint behavior of a subject during the contact type activity. Various contact type activities are simulated using the personalized full body musculoskeletal model. Injury biomarkers and their parameters based on the contact type activities are identified, parameters are indicative of risk of injury to participating muscle groups said activity. Based on the injury biomarkers, optimal muscle co-activation parameters are analyzed by a neuro-muscular controller, to adapt the participating muscle groups for providing the correction action against the predicted risk of injury. Said optimal muscle co-activation parameters are indicative of muscle synergy during the contact type activity.

Method and system for injury risk prediction and corrective action for high contact type activity

The disclosure relates to digital twin of cardiovascular system called as cardiovascular model to generate synthetic Photoplethysmogram (PPG) signal pertaining to disease conditions. The conventional methods are stochastic model capable of generating statistically equivalent PPG signals by utilizing shape parameterization and a nonstationary model of PPG signal time evolution. But these technique generates only patient specific PPG signatures and do not correlate with pathophysiological changes. Further, these techniques like most synthetic data generation techniques lack interpretability. The cardiovascular model of the present disclosure is configured to generate the plurality of synthetic PPG signals corresponding to the plurality of disease conditions. The plurality of synthetic PPG signals can be used to tune Machine Learning algorithms. Further, the plurality of synthetic PPG signals can be utilized to understand, analyze and classify cardiovascular disease progression.

Method and system for pressure autoregulation based synthesizing of photoplethysmogram signal

Systems and methods for quantification of postural balance of users in an augmented reality (AR) environment. Traditional systems and methods provide for quantifying the postural balance using the AR environment but none of them quantify or restrict the functional tasks performed by the users to a predefined level. Embodiments of the present disclosure provide for the quantification of the postural balance with a variable step height in the AR environment by acquiring first set of information comprising of data on skeletal joints, filtering the first set of information for obtaining a filtered set of data, computing the set of postural data based upon the filtered set of data and quantifying the postural balance based upon the set of postural data by computing threshold values for obtaining postural stability index scores and determining based upon the postural stability index scores, the postural balance of the users in the AR environment.

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Kolkata, India

Oishee Mazumder