Method and system for digital biomarkers platform

    公开(公告)号:US12237086B2

    公开(公告)日:2025-02-25

    申请号:US17653248

    申请日:2022-03-02

    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.

    Systems and methods for fusing inertial and bluetooth low energy sensor data for localization

    公开(公告)号:US10397753B2

    公开(公告)日:2019-08-27

    申请号:US15336191

    申请日:2016-10-27

    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.

    SYSTEMS AND METHODS FOR ESTIMATING ERRORS IN GYROSCOPE SENSORS

    公开(公告)号:US20190056426A1

    公开(公告)日:2019-02-21

    申请号:US15935731

    申请日:2018-03-26

    CPC classification number: G01P21/00 G01C19/00 G01C19/5776 G01C25/005 G01P3/44

    Abstract: Embodiments of the present disclosure provide systems and methods that establish non-linear components of gyroscope errors which have not been studied or explored earlier. These errors include but are not limited to non-linear dynamic error which is a function of the angular velocity itself. Bias instability has been observed within the same environment of temperature and atmospheric pressure. In other words, the embodiments of the present disclosure analyse and models static bias errors and dynamic non-linear errors in the gyroscope sensor which may be used to model and correct errors accordingly In subsequent measurements. The system provide solution(s) when there is no way of directly estimating temperature for bias correction of gyroscope, by estimating a temperature change by considering indirect measurements from other sensors present onboard the device.

    Real-time monitoring of proximity between a plurality of computing devices

    公开(公告)号:US11558710B2

    公开(公告)日:2023-01-17

    申请号:US17178081

    申请日:2021-02-17

    Abstract: Conventionally, Received Signal Strength Indicator (RSSI)-based solutions have been extensively devised in the domains of indoor localization and context-aware applications. These solutions are primarily based on a path-loss attenuation model, with customizations on RSSI processing and are usually regression-based. Further, existing solutions for distance and proximity estimation incorporate data features derived only from the RSSI values themselves with additional features like frequency of occurrence of certain RSSI values thus are less accurate. Present disclosure provides systems and methods that implement a classification model that uses RSSI as well as temporal features derived from the received data packets. The model uses data from multiple devices in different environments for training and can execute proximity decisions on the device itself. The method of the present disclosure monitoring proximity between a plurality of devices implements/uses an effective protocol for decision aggregation to suppress false positive proximity events generated and further stabilizes device's response.

    Systems and methods for real-time tracking of trajectories using motion sensors

    公开(公告)号:US12111982B2

    公开(公告)日:2024-10-08

    申请号:US18465409

    申请日:2023-09-12

    CPC classification number: G06F3/0346 G06F1/163 G06F3/014

    Abstract: Tracking motion using inertial sensors embedded in commercial grade wearables like smartwatches has proved to be a challenging task, especially if real-time tracking is a requirement. Present disclosure provides system and method wherein data from sensors are obtained and scaled. Further, Euler Rodrigues Matrix (ERM) is generated based delta value obtained using sensor data. The scaled sensor data and ERM are used for generating feature vectors. Windowing technique is applied for subsets of feature vectors to obtain label for each window and machine learning model is trained with the label and window. Further, during real-time, sensor data is obtained, and steps of ERM, feature vectors generation, and application of windowing technique are repeated, and coordinates are estimated for each window in real-time based on which trajectories are tracked in real-time for each window.

    Wearable apparatus and a method for calculating drift-free plantar pressure parameters for gait monitoring

    公开(公告)号:US11350851B2

    公开(公告)日:2022-06-07

    申请号:US16827756

    申请日:2020-03-24

    Abstract: The present disclosure provides wearable apparatus and method for calculating drift-free plantar pressure parameters for gait monitoring of an individual. Most conventional techniques use different kind of sensors placed in in-sole based wearable apparatus but are costly and not effective in calculating accurate plantar pressure parameters. The disclosed wearable apparatus uses off-the shelf piezoelectric sensors that are widely available in market with less cost. The drift-free plantar pressure parameters are calculated using drift-free static pressure data obtained by numerically integrating acquired dynamic sensor data from the piezoelectric sensors, using a LiTCEM correction mechanism. A 6-DOF Inertial Measurement Unit (IMU sensor) helps in isolating zero-pressure duration indicating when a foot of the individual is in air during a stride, while obtaining the drift-free static pressure data. The disclosed wearable apparatus calculate the drift-free plantar pressure parameters for long duration and facilitates monitoring walking patterns of the individual.

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