Abstract: Existing wearable device-based approaches to capture a_tremor signal have accuracy limitations due to usage of accelerometer sensor with inherent noisy nature. The method and system disclosed herein taps characteristics of the PPG sensor of being sensitive to the motion artifact, as an advantage, to capture tremor_signals present in the PPG sensor. The method disclosed herein describes an approach to extract tremor_signals of interest from the PPG signal by performing a Singular Spectrum Analysis (SSA) followed by spectrum density estimation. The SSA comprises performing embedding on the acquired PPG signal, performing Principal Component Analysis (PCA) on the embedded signal and reconstructing the rest tremor signal from the significant principal components identified post the PCA. Further, the spectrum density estimation detects a dominant frequency present in the principal components, which is the dominant frequency associated with the rest tremor.

Abstract: This disclosure relates generally to patient invariant model for freezing of gait detection based on empirical wavelet decomposition. The method receives a motion data from an accelerometer sensor coupled to an ankle of a subject. The motion data is further processed to denoise a plurality of data windows using a peak detection technique to classify into a real motion data window or a noisy data window. Further, a plurality of denoised data windows are generated by processing spectrums associated with each real motion data window and a plurality of empirical modes using an empirical wavelet decomposition technique (EWT). Then, a resultant acceleration is computed, and a plurality of features are extracted from the denoised data window which enables detection of freezing of gait based on a pretrained classifier model into a (i) a positive class, or (ii) a negative class.

Abstract: While performing heart rate estimation of a user, if the user is in motion, a signal is measured and is likely to have noise data, which in turn affects accuracy of estimated heart rate value. Method and system for heart rate estimation when the user is in motion is disclosed. The system estimates value of a noise signal present in a measured PPG signal by performing a Principal Component Analysis (PCA) of an accelerometer signal collected along with the PPG signal. The system further estimates value of a true cardiac signal for a time window, based on value of the true cardiac signal in a pre-defined number of previous time windows. The system then estimates frequency spectrum of a clean PPG signal based on the estimated noise signal and the true cardiac signal. The system further performs heart rate estimation based on the clean PPG signal.

Abstract: Non-communicable diseases (NCDs) are the pandemics of modern era and are generating huge impact in the modern society. Conventional methods are inaccurate due to a challenge in handling data from heterogenous sensors. The present disclosure is capable of tracking fitness parameters of a user even with heterogenous sensors. Initially, the system receives a raw data from a plurality of heterogenous sensors associated with the user. The raw data is further transformed into a metadata format associated with the corresponding sensor. The transformed data is temporally aligned based on a time based slotting. An algorithm pipeline corresponding to a disorder to be analyzed is selected from a Directed Acyclic Graph (DAG) based on a sensor metadata and a plurality of algorithm metadata corresponding to a plurality of algorithms stored in an algorithm database and an algorithm pipeline. The corresponding disorder is analyzed using the algorithm pipeline.

Abstract: Existing wearable device-based approaches to capture tremor signal have accuracy limitations due to usage of accelerometer sensor with inherent noisy nature. The method and system disclosed herein taps characteristics of the PPG sensor of being sensitive to the motion artefact, as an advantage, to capture tremor signal present in the PPG sensor. The method disclosed herein describes an approach to extract tremor signal of interest from the PPG signal by performing a Singular Spectrum Analysis (SSA) followed by spectrum density estimation. The SSA comprises performing embedding on the acquired PPG signal, performing Principal Component Analysis (PCA) on the embedded signal and reconstructing the rest tremor signal from the significant principal components identified post the PCA. Further, the spectrum density estimation detects a dominant frequency present in the principal components, which is the dominant frequency associated with the rest tremor.

Abstract: Techniques for heart rate estimation are disclosed. In an embodiment, synchronized photoplethysmograph (PPG) and 3-axis acceleration signals are received. Further, the PPG and acceleration signals are partitioned into windows. Furthermore, it is determined whether motion is present in a window of the acceleration signal. Moreover, Fourier transform is performed on the signals to obtain power spectra of the signals in the window when there is motion. Also, it is determined whether a peak of the acceleration signal is present in a range around first highest PPG peak. Further, it is determined whether the peak of the acceleration signal affects heart rate of the user when the peak of the acceleration signal is in the range around the highest PPG peak. The heart rate of the user in the window is then estimated using second highest PPG peak when the peak of the acceleration signal affects heart rate of the user.

Abstract: While performing heart rate estimation of a user, if the user is in motion, measured signal is prone to have noise data, which in turn affects accuracy of estimated heart rate value. Disclosed herein is a method and system for heart rate estimation when the user is in motion. The system estimates value of a noise signal present in a measured PPG signal by performing a Principle Component Analysis (PCA) of an accelerometer signal collected along with the PPG signal. The system further estimates value of a true cardiac signal for a time window, based on value of the true cardiac signal in a pre-defined number of previous time windows. The system then estimates frequency spectrum of a clean PPG signal based on the estimated noise signal and the true cardiac signal. The system further performs heart rate estimation based on the clean PPG signal.

Abstract: This disclosure relates to method for estimating heart rate associated with subject in presence of plurality of motion artifacts. The method includes receiving, a photoplethysmography signal and an acceleration signal associated with the subject; learning, by principal component analysis, a projection matrix by projecting input signal into n-dimensional subspaces to obtain a plurality of principal components; selecting, at least one principal component by (a) matching a dominant frequency of the principal components obtained from the PPG signal and a dominant frequency of the principal components obtained from the accelerometer signal, by applying a Fourier transform for a spectrum estimation; and (b) computing, at least one of (i) percentage of energy contributed by the principal component of the PPG signal, (ii) percentage of energy contributed by the principal component of the accelerometer signal; and estimating, the heart rate of the subject based on the at least one selected principal component.

Abstract: Systems and methods for adaptive sensors calibration are provided. The traditional systems and methods provide for heart rate monitoring using PPG methods but do not focus on adaptive calibration with real-time feedback and dynamic signal quality validation. Embodiments of the present disclosure provide for obtaining an initial value pertaining to a set of calibration parameters of a first sensor and performing a comparison with a pre-defined threshold; incrementing value of the set of calibration parameters by a pre-defined value and performing a comparison; obtaining from the sensor configuration matrix, an initial value pertaining to a set of calibration parameters of a second sensor and performing a comparison; and repeating the steps performed above if the comparisons performed does not match pre-defined quality threshold and finally updating sensor configuration matrix whenever pre-defined quality threshold is matched.

Abstract: The heart rate monitoring systems currently available are prone to mistaking heart rate variation (caused due to the user involving in the physical activity) as an abnormal variation, even though such variations are ‘normal’, and may even trigger a false alarm. Even the monitoring systems which consider mobility states of the user so as to filter out such variations caused due to user activities may fail to consider change in mobility states at the time of measuring the heart rate data, which may have direct impact on the heart rate variations. A system and a method for heart rate estimation are provided wherein data pertaining to transition between different mobility states is considered by the system to filter out variations due to the transition between mobility states of the user. Different types of such transitions are identified, and appropriate methods are executed to filter the estimated heart rate data.

Abstract: Sensor data fusing systems and methods are provided. The fusing system reads and parses a floor plan to obtain a location of a user, identifies a grid in the floor plan using the location, determines a distance between the user and beacons placed at every corner of identified grid, and further trilaterating the location using beacon identifiers. The system further assigns a weight to the trilaterated location based on the distance between the user and the beacons in the grid to obtain a first set of weights, and computes one or more weights using number of particles generated with respect to an inertial measurement obtained from an inertial sensor to obtain a second set of weights. The fusing system further fuses the first set of weights and the second set of weights to obtain a first and a second co-ordinate that indicates specific position of the user in the location.

Abstract: Techniques for heart rate estimation are disclosed. In an embodiment, synchronized photoplethysmograph (PPG) and 3-axis acceleration signals are received. Further, the PPG and acceleration signals are partitioned into windows. Furthermore, it is determined whether motion is present in a window of the acceleration signal. Moreover, Fourier transform is performed on the signals to obtain power spectra of the signals in the window when there is motion. Also, it is determined whether a peak of the acceleration signal is present in a range around first highest PPG peak. Further, it is determined whether the peak of the acceleration signal affects heart rate of the user when the peak of the acceleration signal is in the range around the highest PPG peak. The heart rate of the user in the window is then estimated using second highest PPG peak when the peak of the acceleration signal affects heart rate of the user.

Abstract: Systems and methods for adaptive sensors calibration are provided. The traditional systems and methods provide for heart rate monitoring using PPG methods but do not focus on adaptive calibration with real-time feedback and dynamic signal quality validation. Embodiments of the present disclosure provide for obtaining an initial value pertaining to a set of calibration parameters of a first sensor and performing a comparison with a pre-defined threshold; incrementing value of the set of calibration parameters by a pre-defined value and performing a comparison; obtaining from the sensor configuration matrix, an initial value pertaining to a set of calibration parameters of a second sensor and performing a comparison; and repeating the steps performed above if the comparisons performed does not match pre-defined quality threshold and finally updating sensor configuration matrix whenever pre-defined quality threshold is matched.

Abstract: Method(s) and System(s) for determining location of a user device within a premise are described. The method includes identifying multiple zones with physical boundaries within the premise based on parameters associated with geometry of the premise. The premise includes multiple access points distributed across the multiple zones. Thereafter, the method includes collecting a first set of Received Signal Strength Indicator (RSSI) Data that is representative of strength of signals received from each accessible access point, at different locations within the premise. After collecting the first set, the method includes computing a Variable Path Loss Exponent (VPLE) within each zone for each accessible access point for determining location of the user device based on at least one of the first set of RSSI data, a line of sight condition, a non-line of sight condition and distance between each accessible access point from each location.

Abstract: The present disclosure envisages a computer implemented system and method for Wi-Fi based indoor localization. The system includes a repository for storing attributes of the floor plan of an indoor area with respect to the zones on the floor plan. A communicating module receives a threshold number of data points from user devices located in the area. These data points include a plurality of Received Signal Strength Indicators (RSSI) captured from the access points positioned in the area. A k-means clustering is then performed on the data points for grouping the data points into ‘k’ number of clusters and a decision tree is built by following a condition based approach. Distance values are then calculated pertaining to the RSSIs stored at the decision tree, and zone circles are plotted. Zone of user presence is then determined by correlating the plotted zone circles upon the floor plan using maximum overlap property.

Abstract: Sensor data fusing systems and methods are provided. The fusing system reads and parses a floor plan to obtain a location of a user, identifies a grid in the floor plan using the location, determines a distance between the user and beacons placed at every corner of identified grid, and further trilaterating the location using beacon identifiers. The system further assigns a weight to the trilaterated location based on the distance between the user and the beacons in the grid to obtain a first set of weights, and computes one or more weights using number of particles generated with respect to an inertial measurement obtained from an inertial sensor to obtain a second set of weights. The fusing system further fuses the first set of weights and the second set of weights to obtain a first and a second co-ordinate that indicates specific position of the user in the location.

Abstract: Method(s) and System(s) for determining location of a user device within a premise are described. The method includes identifying multiple zones with physical boundaries within the premise based on parameters associated with geometry of the premise. The premise includes multiple access points distributed across the multiple zones. Thereafter, the method includes collecting a first set of Received Signal Strength Indicator (RSSI) Data that is representative of strength of signals received from each accessible access point, at different locations within the premise. After collecting the first set, the method includes computing a Variable Path Loss Exponent (VPLE) within each zone for each accessible access point for determining location of the user device based on at least one of the first set of RSSI data, a line of sight condition, a non-line of sight condition and distance between each accessible access point from each location.

Abstract: The present disclosure envisages a computer implemented system and method for Wi-Fi based indoor localization. The system includes a repository for storing attributes of the floor plan of an indoor area with respect to the zones on the floor plan. A communicating module receives a threshold number of data points from user devices located in the area. These data points include a plurality of Received Signal Strength Indicators (RSSI) captured from the access points positioned in the area. A k-means clustering is then performed on the data points for grouping the data points into ‘k’ number of clusters and a decision tree is built by following a condition based approach. Distance values are then calculated pertaining to the RSSIs stored at the decision tree, and zone circles are plotted. Zone of user presence is then determined by correlating the plotted zone circles upon the floor plan using maximum overlap property.