Abstract: The disclosure herein generally relates to the field of determination of cardiopulmonary signals for multi-persons, and, more particularly, to determination of cardiopulmonary signals for multi-persons using in-body signals obtained by ultra-wide band (UWB) radar. The disclosed method determines of cardiopulmonary signals for multi-persons using in-body signals, wherein a UWB radar signals/waves reflected from inside a human body is utilized for efficient determination of cardiopulmonary signals. The disclosed method and system utilize the UWB radar signals to identify a number of persons along with several details about the persons that include a girth of the each identified person and the orientation of the identified person towards the one or more UWB radar. Further a chest wall distance, a breathing rate, a heart wall distance and a heart rate are determined for all the identified persons based on the identified girth and the identified orientation along with the UWB radar signals.

Abstract: Direct usage of endosomatic EDA has multiple challenges for practical cognitive load assessment. Embodiments of the method and system disclosed provide a solution to the technical challenges in the art by directly using the bio-potential signals to implement endosomatic approach for assessment of cognitive load. The method utilizes a multichannel wearable endosomatic device capable of acquiring and combining multiple bio-potentials, which are biomarkers of cognitive load experienced by a subject performing a cognitive task. Further, extracts information for classification of the cognitive load, from the acquired bio-signals using a set of statistical and a set of spectral features. Furthermore, utilizes a feature selection approach to identify a set of optimum features to train a Machine Learning (ML) based task classifier to classify the cognitive load experienced by a subject into high load task and low load task.

Abstract: A system is provided for automated monitoring the health of a patient. The system is unifying the approach of multi-sensing, robotic platform and cloud computing to monitor the health of the patient with zero or very minimal human intervention. The plurality of physiological parameters and the pathological values is sensed using the plurality of physiological sensors and the plurality of pathological sensors or using a smart phone of the patient. The body of the patient is scanned using a robotic arm. The data sensed by the sensor is then identifies a set of anomalies and send the set of anomalies to cloud server. A cognitive engine present on the cloud server is then diagnoses a disease using cloud computing and send the report to caregiver and doctor. According to another embodiment, a method is also provided for automated monitoring the health of the person using the above mentioned system.

Abstract: This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNN model having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix.

Abstract: An electrically small antenna operable in both single-ended and differential antenna systems, and corresponding circuitry configurations, is provided. The antenna may be arranged on or in a wearable audio device, such as an earbud. The antenna may include a first curved arm electrically coupled to a first port. The antenna may include a second curved arm of equal size and equal shape as the first curved arm and electrically coupled to a second port. The second curved arm may be rotationally positioned 180 degrees, relative to the first curved arm, about an imaginary axis perpendicular to a surface of the wearable audio device. The single-ended antenna system may include a radio frequency integrated circuit (“RFIC”), fixed matching network, a tuneable capacitor, and a switching circuit. The differential antenna system may include an RFIC, fixed matching network, a tuneable capacitor, and a balun.

Abstract: This disclosure relates generally to method and system for an adaptive filter based learning model for time series sensor signal classification on edge devices. The adaptive filter based learning model for time series sensor signal classification enables automated-computationally lightweight learning (significant reduction in computational resources) and inferring/classification in real-time or near-real-time on CPU/memory/battery life constrained edge devices. The disclosed techniques for time series sensor signal classification on edge devices characterizes the intrinsic signal processing properties of the input time series sensor signals using linear adaptive filtering and derivative spectrum to efficiently construct the adaptive filter based learning model based on standard classification algorithms for time series sensor signal classification.

Abstract: Conventional hierarchical time-series clustering is highly time consuming process as time-series are characteristically lengthy. Moreover, finding right similarity measure providing best possible hierarchical cluster is critical to derive accurate inferences from the hierarchical clusters. Method and system for Auto Encoded Compact Sequences (AECS) based hierarchical time-series clustering that enables compact latent representation of time-series using an undercomplete multilayered Seq2Seq LSTM auto encoder followed by generating of HCs using multiple similarity measures is disclosed. Further, provided is a mechanism to select the best HC among the multiple HCs on-the-fly, based on an internal clustering performance measure of Modified Hubert statistic ?. Thus, the method provides time efficient and low computational cost approach for hierarchical clustering for both on univariate and multivariate time-series.

Abstract: In the field of Internet of Things understanding need of applications and translating them to network parameters and protocol parameters is a major challenge. This disclosure addresses problem of enabling network services by cognitive sense-analyze-decide-respond framework. A processor implemented method is provided for enabling network aware applications and applications aware networks by a sense analyze decide respond (SADR) framework.

Abstract: Systems and methods for generating control system solutions for robotics environments is provided. The traditional systems and methods provide robotics solutions but specialized to only a particular robotic application, domain, and selected structure.

Abstract: Embodiments herein provide a method and system for continuously validating a user during an established authenticated session using Photoplethysmogram (PPG) and accelerometer data. State of the art approaches are mostly based on feature extraction and ML modelling for PPG based continuous session validation, while a template based approach in the art follows a complicated approach. The method disclosed herein utilizes less computation intensive template based approach to continuously validate the user across the session. The method comprises preprocessing a PPG data or PPG signal acquired from a wearable device worn by the user to identify segments of negligible motion. A first segment, after authentication using conventional authentication mechanism, serves as the initial reference. The chosen segments are then tested one by one with respect to the reference. If the templates in a segment match those of the reference, it is updated as the new reference, else a re-authentication is triggered.

Abstract: Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.

Abstract: Wellness estimation allows tracking of various health parameters and estimating health condition(s) of the user being monitored. Photophlethysmogram (PPG) based health monitoring systems exist. This disclosure relates generally to PPG based wellness monitoring, and more specifically to a pulse harmonics based wellness estimation. The system collects PPG signals from a user being monitored, as input. The system calculates 12 pulse harmonics from a fundamental frequency of the PPG signal. From the pulse harmonics, further a plurality of key features are extracted, and in turn a wellness metric and a wellness index are calculated, which represents health of the user.

Abstract: This disclosure relates to attention shifting of a robot in a group conversation with two or more attendees, wherein at least one of them is a speaker. State of the art has dealt with several aspects of Human-Robot Interaction (HRI) including responding to a source of sound at a time, addressing a fixed viewing area or determining who is the speaker based on eye gaze direction. However, attention shifting to make the conversation human-like is a challenge. The present disclosure uses audio-visual perception for speaker localization. Only qualified direction of arrivals (DOAs) are used for the audio perception. Further the audio perception is complimented by visual perception employing real time face detection and lip movement detection. Use of HRI rules, clustering of the DOAs, dynamic adjustment of rotation of the robot and a dynamically updated knowledge repository enriches the robot with intelligence to shift attention with minimum human intervention.

Abstract: This disclosure relates to monitoring of machines having one or more vibrating objects. Conventional systems that address the technical problem of detecting frequency of vibrating objects are expensive, require manual intervention, sometimes depend on prior knowledge of location of faults or involve high convergence time. Systems and methods of the present disclosure provide a cost-effective and fully automated solution that employ multiple strobe sources along with a low cost camera. Besides being cost-effective and automated, the solution also reduces convergence time significantly. Employing multiple strobe sources enables generating multiple strobing frequencies in a single iteration. The strobing frequency is a configured to be a multiple of a camera frame rate selected from a set of camera frame rates having mutually prime elements to ensure faster convergence compared to the art.

Abstract: System and method for aiding communication for subjects suffering from paralysis of muscles controlled by peripheral nervous system are disclosed. A method for aiding communication for said subjects includes capturing, from a plurality of sensors, sensor data generated based on an interaction of a subject with an interactive UI. The plurality of sensors includes one or more body-parts movement tracking sensors for tracking motion of body-parts of the subject and one or more physiological signal sensors for monitoring physiological signals generated from the subject during the interaction. A plurality of model parameters indicative of characteristics of the subject related to the interaction are determined based on the sensor data. The navigation at the interactive UI is controlled on the plurality of model parameters.

Abstract: The disclosure generally relates to methods and systems for maintaining quality of experience (QoE) in real-time live video streaming. Conventional techniques in achieving the QoE employ either additional bandwidth of the network or compromise on a latency of the network. The present disclosure provides a rendering algorithm to compensate for lost entropy without attempting to regain exact lost packet(s) information. The rendering algorithm enhance the QoE by regaining structural continuity of the lost packet(s) information despite variations in the end-to-end transmission channel. A segmentation algorithm is employed to segment each frame into one or more packets, so that each packet should only have integer number of MCU blocks. The rendering algorithm to identify the lost packets and to compensate the lost information. The present disclosure provides a loss resilient solution to maintain the QoE without trading off the bandwidth efficiency and the network latency.

Abstract: A method and system for detection of coronary artery disease (CAD) in a person using a fusion approach has been described. The invention the detection of CAD in the person by capturing of a plurality of physiological signals such as phonocardiogram (PCG), photoplethysmograph (PPG), ECG, galvanic skin response (GSR) etc. from the person. A plurality of features are extracted from the physiological signals. The person is then classified as CAD or normal using the each of the features independently. The classification is done based on supervised machine learning technique. The output of the classification is then fused and used for the detection of the CAD in the person using a predefined criteria.

Abstract: Any sensing system is faced with triangle of dilemma between accuracy, latency and energy. High energy and high latency sensing systems are often very accurate but less useful. Embodiments herein provide a method and system for edge based sensor controlling in the IoT network for event monitoring. The system disclosed herein applies a hierarchical sensor selection process and adaptively chooses sensors among multiple sensors deployed in the IoT network. Further, on-the-fly changes operation modes of the sensors to automatically produce the best possible inference from the selected sensor data, in time, power and latency at the edge. Further, sensors of the system include a waveform and diversity control mechanism that enables controlling of an excitation signal of the sensor.

Abstract: This disclosure relates generally to a method and system for multi-hop path selection for mobile robots based on cloud platform providing an optimal path for end-to-end communication in the multi-hop network. Multi-hop path selection for mobile robots, conventionally performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. Further, computation at mobile robot end is not an energy efficient solution. The disclosed cloud system communicates the optimal path to the source mobile robot to reach the destination mobile robot through the plurality of Access Points (APs). Multi-hop path selection for mobile robots, currently performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. The optimal path computed at cloud system increases the life-time of robotics network there by increasing the efficiency.

Abstract: This disclosure relates generally to the use of distributed system for computation, and more particularly, relates to a method and system for optimizing computation and communication resource while preserving security in the distributed device for computation. In one embodiment, a system and method of utilizing plurality of constrained edge devices for distributed computation is disclosed. The system enables integration of the edge devices like residential gateways and smart phone into a grid of distributed computation. The edged devices with constrained bandwidth, energy, computation capabilities and combination thereof are optimized dynamically based on condition of communication network. The system further enables scheduling and segregation of data, to be analyzed, between the edge devices. The system may further be configured to preserve privacy associated with the data while sharing the data between the plurality of devices during computation.