Abstract: Existing systems for behavioural tracking and identification have the disadvantage that they do not analyse data in behavioural aspects. As a result, they lack ability to pre-empt scenarios involving actions that adversely affect user health. The disclosure herein generally relates to behavior prediction, and, more particularly, to a method and system for identifying unhealthy behavior trigger and providing nudges. The system generates a casual inference model, which is a reverse causality model facilitating mapping of context with one or more behaviour of the user. The system further collects and processes real-time data using the casual inference model, to perform behavioral analysis of the user.

Abstract: This disclosure relates generally to a unified platform for domain adaptable human behaviour inference. The platform provides a unified, low level inference and high level inference of domain adaptable human behaviour inference. The low level inferences include cross-sectional analysis techniques to infer location, activity, physiology. Further the high inference that provide useful and actionable for longitudinal tracking, prediction and anomaly detection is performed based on several longitudinal analysis techniques that include welch analysis, cross-spectrum analysis, Feature of interest (FOI) identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with low level inference, human meta-data and application domain knowledge.

Abstract: This disclosure relates generally to real time analysis of range of motion (ROM), wherein ROM is a measurement of movement around a specific joint or body part. The existing techniques for ROM fail for measurements made in certain planes and are not very effective for ROM measurements for extremely slow and very fast movements. The disclosed provides a real time analysis of ROM based on computation of range of motion (ROM) of a joint and a set of ROM parameters using a gyroscope. The gyroscope collects data from a subject at pre-defined neutral position of the subject as well as a pre-defined rotation movement of a joint of the subject. The received data is corrected for bias and processed at real time to analyze the ROM by computing range of motion (ROM) of a joint and a set of ROM parameters.

Abstract: This disclosure relates generally to breathing analysis of a subject. Breathing analysis on a regular basis allows early detection for the onset of diseases, thus saving resources and cost in treatments. The existing state of art techniques require specialized devices to collect-infer the breathing and are mostly limited to analyzing breathing rate. The disclosure enables breathing analysis using a personal digital assistant (PDA). The breathing analysis includes (a) estimating exhale period-inhale period, (b) estimating the breathing rate and (c) determining the type of breathing. A PDA such as a smartphone is used receive accelerometer data from a subject. The received data is pre-processed in several steps including estimating a plurality of parameters, identifying a plurality of breathing cycles. The breathing cycles of the subject are further analyzed at real time based on the plurality of parameters to provide the breathing analysis of a subject.

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.

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: This disclosure relates generally to classification of cardiopulmonary fatigue. The method and system provides a longitudinal monitoring platform to classify cardiopulmonary fatigue of a subject using a wearable device worn by the subject. The activities of the subject is continuous monitored by plurality of sensors embedded in a wearable device. The received sensor signals are processed in multiple stages to classify cardiopulmonary fatigue as healthy or unhealthy based on respiratory, heart rate and recovery duration parameters extracted from the received sensor data. Further using the classified cardiopulmonary fatigue level, the C2P also performs longitudinal analysis to detect potential cardiopulmonary disorders.

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.

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.

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.

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.

Abstract: This disclosure relates generally to a unified platform for domain adaptable human behaviour inference. The platform provides a unified, low level inference and high level inference of domain adaptable human behaviour inference. The low level inferences include cross-sectional analysis techniques to infer location, activity, physiology. Further the high inference that provide useful and actionable for longitudinal tracking, prediction and anomaly detection is performed based on several longitudinal analysis techniques that include welch analysis, cross-spectrum analysis, Feature of interest (FOI) identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with low level inference, human meta-data and application domain knowledge.

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: This disclosure relates generally to classification of cardiopulmonary fatigue. The method and system provides a longitudinal monitoring platform to classify cardiopulmonary fatigue of a subject using a wearable device worn by the subject. The activities of the subject is continuous monitored by plurality of sensors embedded in a wearable device. The received sensor signals are processed in multiple stages to classify cardiopulmonary fatigue as healthy or unhealthy based on respiratory, heart rate and recovery duration parameters extracted from the received sensor data. Further using the classified cardiopulmonary fatigue level, the C2P also performs longitudinal analysis to detect potential cardiopulmonary disorders.

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.

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: A detection system for detecting the presence and direction of a subject moving through a doorway is disclosed. The system comprises magnets disposed on the doorway, a communication device, typically a smartphone and a server. The magnets are positioned on the doorway to create a magnetic field across the doorway. When the subject having the smartphone on the body passes through the doorway, a magnetometer in the smartphone senses perturbations caused by the magnetic field and generates signals corresponding to the sensed perturbations and a processor of the smartphone processes the signals to detect the presence and direction of the subject through the doorway. The data generated by the processor and a unique identification associated with the smartphone is transmitted to the remotely located server.

Abstract: An indoor positioning system and method of localizing a person/object in an indoor environment by identifying the orientation and direction of a person/object to provide a true location of the person/object without navigation errors. The system comprises magnets disposed on a doorway to create a unique magnetic field; a wireless communication unit comprising a magnetometer sensor to sense perturbations in each of the unique magnetic fields in the event that the person/object with the wireless communication unit passes through the doorway, and generate corresponding signals; a processor receiving the signals and extracting data from the same; and a backend server wirelessly communicating with the wireless communication unit, the backend server processing the data sample received from the wireless communication unit to identify the opening and the wireless communication unit to localize the person/object.

Abstract: A detection system for detecting the presence and direction of a subject moving through a doorway is disclosed. The system comprises magnets disposed on the doorway, a communication device, typically a smartphone and a server. The magnets are positioned on the doorway to create a magnetic field across the doorway. When the subject having the smartphone on the body passes through the doorway, a magnetometer in the smartphone senses perturbations caused by the magnetic field and generates signals corresponding to the sensed perturbations and a processor of the smartphone processes the signals to detect the presence and direction of the subject through the doorway. The data generated by the processor and a unique identification associated with the smartphone is transmitted to the remotely located server.

Abstract: An indoor positioning system and method of localizing a person/object in an indoor environment by identifying the orientation and direction of a person/object to provide a true location of the person/object without navigation errors. The system comprises magnets disposed on a doorway to create a unique magnetic field; a wireless communication unit comprising a magnetometer sensor to sense perturbations in each of the unique magnetic fields in the event that the person/object with the wireless communication unit passes through the doorway, and generate corresponding signals; a processor receiving the signals and extracting data from the same; and a backend server wirelessly communicating with the wireless communication unit, the backend server processing the data sample received from the wireless communication unit to identify the opening and the wireless communication unit to localize the person/object.