WO2023007372A1

WO2023007372A1 - Data storage from sensor array

Info

Publication number: WO2023007372A1
Application number: PCT/IB2022/056893
Authority: WO
Inventors: Siddhartha Panda; Biswanath Panda; Shivam TRIVEDI; Rohit Bhargava; Karun MALHOTRA; Thachat Ragash
Original assignee: Indian Institute Of Technology Kanpur; Murata Manufacturing Co., Ltd.
Priority date: 2021-07-28
Filing date: 2022-07-26
Publication date: 2023-02-02

Abstract

Examples of a wearable electronic system are described. The system comprises a sensor array and a data acquisition (DAQ) system comprising a processing module. The processing module receives a user identifier associated with the user, and sensor data from the sensor array. The processing module analyses the sensor data to determine a set of transient state sensor data from sensor data. The sensor data may be analysed based on the user identifier and a state of the sensor array. The processing module filters out the set of transient state sensor data from the plurality of sensor data to obtain steady state sensor data and causes to store the steady state sensor data.

Description

DATA STORAGE FROM SENSOR ARRAY EARLIEST PRIORITY DATE:

This Application claims priority from a Provisional patent application filed in India having Patent Application No. 202111034021, filed on July 28, 2021, and titled “DATA STORAGE FROM SENSOR ARRAY”

TECHNICAL FIELD

The present subject matter relates, in general, to data acquisition.More specifically, the present subject matter relates to approaches for storing data acquired from a sensor array.

BACKGROUND

With advancement of healthcare technologies, increasingly sophisticated medical devices are utilized for detection and treatment of diseases. Early detection of symptoms associated with a disease has proven to be an efficient process in curing the disease. Therefore, it is crucial to regularly monitor physiological parameters of a patient to detect symptoms associated withthe disease. In this regard, sensor devices may be utilized to monitor physiological parameters of a patient. A sensor device may capture physiologicaldata, such as biophysical signals, from the patient. Further, the physiological data may be processed and analysed to identify an anomaly and predict an occurrenceof a disease.

BRIEF DESCRIPTION OF DRAWINGS

The features, aspects, and advantages of the present subject matterwill be better understood with regards to the following description and accompanying figures. The use of the same reference number in different figuresindicate similar or identical features and components.

FIGS. 1-3 illustrate a block diagram of a data storage system, as pervarious examples;

FIG. 4 illustrates a perspective view of a wearable electronic system comprising a data acquisition module, as per an example; and

FIG. 5 illustrates a flow diagram depicting a method for storing dataacquired from a sensor array, as per an example.

Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements. The figures are not necessarily to scale, and the size of some parts may be exaggerated to more clearly illustrate the example shown. Moreover, the drawings provide examples and/or implementations consistent with the description; however, the description is notlimited to the examples and/or implementations provided in the drawings.

DETAILED DESCRIPTION

Regular monitoring of physiological parameters is crucial for timely diagnostic of health ailments and subsequent treatment. To this end, sensor devices may be used for acquiring physiological data from a patient. Such physiological data may then be interpreted and analysed for identifying any healthailment. Examples of physiological parameters include, but are not limited to, body temperature, heart rate, blood pressure, serum levels, and immunological functions.

Typically, specialized sensor equipment may be utilized for acquiring relevant physiological data from a patient. Such sensor equipment may have complex architecture and may require involvement of a skilled person for its operation. Therefore, the cost and inconvenience associated with the use of such sensor equipment inhibits its use for regular monitoring of the physiological parameters.

In certain cases, wearable sensor devices may be utilized for regular and personalized monitoring of physiological parameters of patients. For example, a wearable sensor device may include a plurality of sensors and an electronic data acquisition device. The plurality of sensors may be integrated directly on the patient’s body as a patch or integrated into a garment to be worn by the patient. When activated, the plurality of sensors may read one or more physiological parameter of the patient. Further, the data acquisition device may acquire readings, such as biophysical signals, from the plurality of sensors and interpret physiological data for the patient.

When in operation, plurality of sensors of a wearable sensor device may take multiple readings corresponding to a patient undergoing a monitoring session. Further, the patient may have to monitor their physiological parametersregularly. In this regard, the wearable sensor device may be used for multiple monitoring sessions, wherein a plurality of readings may be collected during eachof the multiple monitoring sessions. In addition, the wearable monitoring device may be used by a number of patients, for example, within a family, or at a test centre. To such an end, the data acquisition device may receive data corresponding to each of the multiple monitoring sessions performed by the sensor array.

Such data obtained from the sensor array may be large and require substantially large storage space. However, increasing storage capacity of the data acquisition device may hamper compactness and size of the wearable electronic device. This may make the wearable electronic device, specifically, theDAQ device, bulky and uncomfortable, thereby affecting usability thereof. In an example, the data obtained from the sensor array may be stored remotely, for example, on cloud storage or a computing system, to reduce a storage capacity of the DAQ device.

In an example, the data may be transmitted from the DAQ device via existing connectivity technologies such as intermittent wired approach or wirelessemail. However, such transmission of data requires Internet connectivity. Therefore, transmission of data from the wearable electronic device or DAQ device over Internet may fail in areas with substandard or inefficient network infrastructure, such as rural areas. In another example, the data may be live streamed from the DAQ device via Bluetooth. However, live streaming of such large amount of data may be a battery exhausting activity. Due to this, active hours of the wearable electronic device may decrease, thereby decreasing a totalnumber of monitoring sessions that can be performed using the wearable electronic device. Live streaming of data may also fail in cases where multiple DAQ devices may stream data to a same central computing device. In addition, data may be lost if Internet or Bluetooth connectivity shuts down during the transmission.

In certain cases, the wearable electronic device may be used in rural areas with limited or erratic power supply. In such cases, decrease in active hoursof the wearable electronic device may affect operation of the wearable electronicdevice adversely, thereby affecting diagnosis or monitoring of health ailments reliably. Moreover, increasing power capacity of the wearable electronic device may affect size and usability of the wearable electronic device. In addition, increasing the power capacity or the storage capacity may have added costs, thereby making the wearable electronic device less cost effective.

Approaches for acquiring and storing sensor data from a wearable electronic system, are described. The wearable electronic system includes a sensor array and a data acquisition (DAQ) system. In an example, the sensor array may include a plurality of resistive sensors (referred to as sensors, hereinafter). The sensors may be operable to sense or detect a change in physical condition associated with the sensors. Examples of a resistive sensor may include, but is not limited to, thermocouple, thermistor, resistance temperature detector (RTD), light-dependent resistor (LDR), and thermometer. Inone example, the sensors within the sensor array may be connected to each other in parallel configuration.

The sensor array may be coupled to the data acquisition (DAQ) system. The DAQ system may acquire biophysical signals from the sensors. For example, the DAQ system may interpret sensor data from the electric signals. Inparticular, the DAQ system may sample the acquired biophysical signals that correspond to changes in physical conditions of the sensors. Further, the DAQ system may convert the acquired signals into sensor data. It may be noted that the DAQ system may be independent of a type of signal acquired from the sensor array. Continuing further, the DAQ system includes a processing module. The processing module may be a hardware device, a software program, a firmware, or a combination thereof. The processing module may be coupled to the sensor array. In one example, each of the sensors of the sensor array may be connected to the processing module. To this end, the processing module maymanage or direct flow of data, current, or voltage to and from the sensors of the sensor array.

In operation, the processing module receives a user identifier associated with the user. By providing the user identifier, the user may associate a persistent identifier, i.e., the user identifier, for the user with user’s data from one or more sessions initiated on the wearable electronic system. For example, the user identifier may be a unique identifier associated with the user.

On initialization, the sensors of the sensor array acquire a plurality of sensor data associated with the user. In particular, the sensors may generate biophysical signals corresponding to physiological parameters of the user. For example, the sensors may determine a change in physical condition, for example, temperature, pressure, sound, motion, light, etc. The sensors may covert such change into electrical biophysical signals.

The processing module of the DAQ system may receive the plurality of sensor data from the sensor array. In an example, the processing module mayreceive the biophysical signals from the sensors and process it to interpret the plurality of sensor data. Pursuant to present subject matter, each of the plurality of sensor data (referred to as sensor data, hereinafter) is associated with a sensorfrom the sensors of the sensor array. The sensor data may be generated by eachof the sensors of the sensor array, wherein the sensors of the sensor array may be located at different locations and sensor data from a sensor may be indicative of change in physiological parameters at a location corresponding to the sensor.

Based on the user identifier and a state of the sensor array, the processing module analyses the plurality of sensor data to determine a set of transient state sensor data from the plurality of sensor data. Herein, the state of the sensor array may be one of transient state and steady state. In this regard, the processing module may determine state of each of the sensors of the sensorarray when the sensors acquire corresponding sensor data. For example, the plurality of sensor data may include multiple readings from each of the sensors of the sensor array for an ongoing monitoring session of the user. To such an end, some of the plurality of sensor data may be acquired during the transient state of the sensors while remaining of the plurality of sensor data may be acquired after the sensors achieve the steady state. For example, the sensors may reach steady state when each of the sensors receive constant amount of electrical signal, such as current or voltage. To such an end, the processing module may determine the transient state sensor data from the plurality of sensor data that corresponds to the transient state of the sensor array.

Further, the processing module filters out the set of transient state sensor data from the plurality of sensor data to obtain steady state sensor data. It may be noted that the steady state sensor data corresponds to the steady stateof the sensor array. In an example, the sensors may achieve the steady state after a short time after the initialization of the wearable electronic system. In this manner, the processing module generates the steady state sensor data that is free of noise and reliable. Thereafter, the processing module causes to store thesteady state sensor data.

As would be understood, the various examples of the present subject matter provide a variety of technical advantages. The DAQ system described in the present subject matter may acquire data from the sensors of the sensor array in a robust manner in contrast to conventional large and bulky data acquisition devices. The robust hardware of the DAQ system and intelligent firmware ensures that the wearable electronic system achieves steady-state saturation in a short time interval. Further, the processing module filters out the set of transientstate sensor data from the plurality of sensor data. In this manner, only the steady state sensor data corresponding to a monitoring session of the user may be stored within a storage module. This allows the DAQ system to use the storage module optimally, thereby being able to store data corresponding to multiple monitoring sessions associated with same or different users. In addition, storing steady state sensor data ensures that optimized data is stored and made available for further processing and analytics. This enhances reliability of reportsgenerated for the user and ensures that an ailment is accurately diagnosed.

In addition, steady state sensor data corresponding to multiplemonitoring sessions is stored within the storage module of the DAQ system. Storing data corresponding multiple user monitoring sessions is achieved without increasing storage capacity thus, retaining compactness and form factor of the wearable electronic system. Moreover, as the data is not transmitted to a remotestorage location continuously, power consumption of the DAQ system is optimized. As a result, operating hours or active hours of the wearable electronicsystem is optimized and the wearable electronic system is capable of performing greater number of monitoring sessions. Further, the data may be accessed by the user or paramedic staff for report generation or analytics without any dependence on the external connectivity feature, such as Bluetooth, Wi fi, or Internet.

Due to the compact size of the DAQ system, portability as well as wearability of the DAQ system may be enhanced. The DAQ system operates efficiently in low power mode. Owing to less cost, compact body and ease of self-operation, the DAQ system may be used for regular personalized medical diagnosis as well as public diagnosis at community test centres in a comfortableway .

The above examples are further described in conjunction with appended figures FIGS. 1-5. It should be noted that the description and figures merely illustrate the principles of the present subject matter. It will thus be appreciated that various arrangements that embody the principles of the presentsubject matter, although not explicitly described or shown herein, may be devisedfrom the description and are included within its scope. Moreover, all statements herein reciting principles, aspects, and examples of the present subject matter, as well as specific examples thereof, are intended to encompass equivalents thereof. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are usedthroughout the figures to reference like features and components. FIG. 1 illustrates a block diagram of a wearable electronic system 100, as per an example. The wearable electronic system 100 comprises a sensor array 102 and a data acquisition (DAQ) systeml04 coupled to the sensor array 102. The sensor array 102 includes a plurality of resistive sensors (not shown inFIG. 1). As would be understood, a resistive sensor may convert a mechanical change into change in resistance across the resistive sensor. For example, the mechanical change may be caused due to a change in temperature, light, humidity, pressure, displacement, and the like. To this end, an electrical signal generated by the resistive sensor may correspond to such change in physical conditions associated with the resistive sensor. Moreover, such electrical signals may be a voltage signal, or a current signal. In one example, the sensor array 102 may include 32 resistive sensors arranged in parallel to each other.

The electrical signals generated by the plurality of sensors of the sensor array 102 are read by the DAQ systeml04. In one example, the sensor array 102 may be a medical device adhering to body and requiring highly sensitive data acquisition. Subsequently, the DAQ system 104 may be used for medical applications that enables precise data acquisition from the sensor array 102, while maintaining low power consumption, compactness, and portability of the DAQ system 104. The DAQ systeml04 is electrically coupled with the sensorarray 102, either directly or through other connecting means. Example of the connecting means include, but are not limited to, data transmission cables, power transmission cables, and electrical wire.

The DAQ system 104 includes a processing modulel06. For example, the processing modulel06 may control operation of the DAQ systeml04 for acquiring sensor data from the sensorarray 102. The processing modulel06 maybe implemented as either software installed within the DAQ system 104, or as hardware in the form of electronic circuitry integrated within the circuitry of the DAQ system 104.

In operation, the processing module 106 may receive a user identifier associated with a user. For example, the user may wear the wearable electronic system 100, such that the sensor array 102 of the wearable electronic system 100 is in contact with the user. After powering the wearable electronic system lOOto an ON’ state, the user may provide the user identifier to the DAQ system 104. Once activated, the plurality of sensors of the sensor array 102 may acquire a plurality of sensor data associated with the user. The plurality of sensor data mayinclude multiple readings from each of the sensors. Further, the DAQ system 104 receives the plurality of sensor data from the sensor array 102. Each of the plurality of sensor data is associated with a corresponding sensor that sensed thesensor data. The plurality of sensor data may be indicative of physical conditionsat different locations associated with the corresponding sensor on the user’s body.

Thereafter, the processing module 106 analyses the plurality of sensordata, based on the user identifier and a state of the sensor array. The state of thesensor array is one of transient state and steady state. For example, based on the user identifier, the processing modulel06 may identify characteristics of the user. Examples of such characteristics include, but are not limited to, gender, age, weight, height, co-morbidities, and the like. Subsequently, the processing module 106 may determine ideal or reference readings for the user.

Further, the processing module 106 determines the state of a sensor when recording or sensing a corresponding sensor data. The processing module 106 determines a set of transient state sensor data from the plurality of sensor data. As may be understood, the transient state of the sensor array 102 or a sensor of the sensor array 102, may be an intermediary state before achieving the steady state. In an example, high current or voltage may flow through the sensors during the transient state. The transient state sensor data corresponds to sensor data recorded during the transient state of the sensor array.

Continuing further, the processing module 106 may filter out the set oftransient state sensor data from the plurality of sensor data to obtain steady state sensor data. The steady state sensor data corresponds to the steady state of thesensor array. For example, constant current and/or voltage may flow through thesensors of the sensor array 104 during the steady state. Moreover, the sensors may achieve the steady state after a period of time, from the activation or initialization of the wearable electronic system 100. To such an end, the steady state may be stable, therefore, the steady state sensor data may be accurate sensor data.

The processing modulel06may then cause to store the steady state sensor data. For example, the processing module 106 may store the steady statesensor data within a storage module (not shown in FIG. 1) that is local to the DAQ system 104. Such storage module may be accessed without any external communication means, such as Bluetooth, or Internet. In a similar manner, sensor data for other monitoring sessions for same or different user of the wearable electronic system 100 may be stored within the storage module. Theseand other examples for acquiring data from the sensor array 102 are further described in conjunction with FIG. 2.

FIG. 2 provides a block diagram of a data acquisition (DAQ) system 104, as per an example. The DAQ system 104 includes processor(s) 202, memory(s) 204 and interface(s) 206. The processor(s) 202 may be a single processing unit or may include a number of units, all of which could include multiple computing units. The processor(s) 202 may be implemented as one or more microprocessor, microcomputers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. The processor(s) 202 maybe adapted to fetch and execute processor-readable instructions stored in the memory(s) 204 to implement one or more functionalities. The processors(s) 202may be operable to extract, receive, process, share and store information based on instructions that drive the DAQ system 104.

The memory(s) 204 may be coupled to the processor(s) 202. The memory(s) 204 may include any computer-readable medium known in the art including, for example, volatile memory, such as Static Random-Access Memory(SRAM) and Dynamic Random-Access Memory (DRAM), and/or non-volatile memory, such as Read Only Memory (ROM), Erasable Programmable ROMs(EPROMs), flash memories, hard disks, optical disks, and magnetic tapes. [0037] The interface(s) 206 may include a variety of software and hardware enabled interfaces. The interface(s) 206 may facilitate multiple communicationswithin a wide variety of protocols and may also enable communication with one or more computer enabled terminals or similar network components.

The DAQ systeml04 may further include other component(s) 208. Theother component(s) 208 may include a variety of other electrical components that enable functionalities of reading, acquiring or obtaining sensor data from sensorarray (such as the sensor array 102). Example of such other component(s) 208 include, but is not limited to, switch(es), housing, power source(s), socket(s), port(s), voltage regulator(s), alarm, indicator(s), and controller(s).

The DAQ system 104 further includes one or more module(s) 210. Themodule(s) 210 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement a variety of functionalities of the module(s) 210. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the module(s) 210 may be executable instructions. Such instructions in turn may be stored on a non- transitory machine- readable storage medium which may be coupled either directly with the DAQ system 104 or indirectly (for example, through networked means). In an example, the module(s) 210 may include a processing resource (for example, either a single processor or a combination of multiple processors), to execute such instructions. In the present examples, the processor-readable storage medium may store instructions that, when executed by the processing resource, implement module(s) 210. In other examples, module(s) 210 may be implemented as electronic circuitry. The module(s) 210 include a processing module 106, an analog front-end module 212, a storage module 214, a communication module 216, a fault detection module 218, and other module(s) 220. The other module(s) 220 may further implement functionalities that supplement applications or functions performed by the DAQ system 104 or any of the module(s) 210.

The data 222 includes data that is either stored or generated as a result of functionalities implemented by any of the module(s) 210. It may be further noted that information stored and available in the data 222 may be utilizedfor storing sensor data from the sensor array 102 or may correspond to output acquired from the sensor array 102. The data 222 may include plurality of sensor data 224, user identifier 226, indicative features 228, and other data 230. The plurality of sensor data 224 may include multiple readings acquired from each ofthe sensors of the sensor array 102; and the user identifier 226 may include a unique identifier of a user of the DAQ system 104. The indicative features 228 may include indicators for fault detection corresponding to each of the sensors the sensor array 102. The other data 230 may include information, for example, associated with the operation of the DAQ system 104.

A user of the DAQ system 104 may activate the wearable electronic system 100. The user may wear the sensor array 102 such that the plurality of sensors of the sensor array 102 are in contact with the user’s body. Once worn, the user may actuate a power button of the DAQ system 104 to activate the wearable electronic system 100. Subsequently, the sensor array 102 and the DAQ system 104 may be activated.

In operation, the processing module 106 receives a user identifier associated with the user. In one example, the processing module 106 may createa user profile for the user, based on the user identifier. The user identifier may uniquely identify the user. For example, the user profile may indicate identification information, medical history, medication history, family medical history, treatmenthistory, and medical directives. Moreover, the user identifier may connect the user profile, hence the user, with user devices, monitoring sessions, and engagement data. In an example, the user identifier may be a series of alphabets, numbers, symbols, special characters, or a combination thereof. In another example, the user identifier may be a biometric input, such as fingerprint. The user identifier of the user may be stored as user identifier 226. Thereafter, the sensor array 102 acquires a plurality of sensor data associated with the user, wherein the plurality of sensor data may be stored as the plurality of sensor data 224. In particular, the processing module 106 may trigger the sensor array 102. To such an end, the processing module 106 may control flow of electrical signals from the DAQ system 104 to the sensor array 102to obtain the plurality of sensor data 224.

In one example, the processing module 106 may cause to trigger a first sensor from the plurality of sensors of the sensor array during a first time period. In this regard, the processing module 106 may determine a constant current value for the sensor array 102 based on rating of the plurality of sensors within the sensor array 102. Based on the constant current value, the processingmodule 106 may cause a power source within the DAQ system 104 to generate a constant current for the sensor array 102.

Continuing further with the present example, the processing module 106 may select the first sensor from the plurality of sensors of the sensor array 102. In an example, the processing module 106 may employ the analog front-endmodule 212 to select the first sensor from the plurality of sensors. For example, the analog front-end module 212 may include one or more multiplexers, wherein a number of multiplexers may be dependent on a number of sensors in the sensorarray 102. Moreover, the analog front-end module 212 may be electricallycoupled to the power source that generates the constant current.

For example, the analog front-end module 212 may select a first circuitassociated with the first sensor and supply the constant current to the first sensorduring the first time period. Further, the analog front-end module 212 may receivean output signal acquired by the first sensor, wherein the signal is associated with the user. The output signal may indicate a change in physiological parameters ofthe first sensor, during the first time period, at a first location on user’s body. Thereafter, the analog front-end module 212 may provide the output signal received from the first sensor to the processing module 106.

As the constant current is supplied only to the selected first circuit associated with the first sensor, the sensors of the sensor array 102 other than the first sensor may be non-operational during the first time period. This eliminates any interference due to flow of current in the other sensors in the outputsignal read for the first sensor. In a similar manner, output signal may be obtainedfrom each of the plurality of sensors of the sensor array 102. In this manner, the analog front-end module 212 enables data acquisition from the plurality ofsensors of sensor array.

The processing module 106 may receive the output signal from the analog front-end module 212. For example, the output signal acquired by the firstsensor is a bio-physical signal associated with the user. In an example, the DAQ system 104 may include an analog-to digital converter, wherein the processing module 106 may translate the analog output signal generated by the first sensorinto digital output signal using the analog-to digital converter. Examples of the analog-to digital converter (ADC) may include, but is not limited to, flash ADC, slope integration ADC, and successive approximation ADC. To such an end, the processing module 106 may process or sample the digital output signal to obtain sensor data corresponding to the first sensor. In this manner, each of the pluralityof sensors of the sensor array 102 may be read sequentially during a corresponding time period. Subsequently, the processing module 106 may obtaina plurality of sensor data corresponding to the plurality of sensors, wherein more than one reading (or sensor data) may be obtained from each of the sensors.

The processing module 106 may store the sensor data as plurality ofsensor data 224. In an example, the processing module 106 may associate time- stamp with each of the plurality of sensor data 224, wherein the plurality of sensordata 224 may be stored with corresponding time-stamps. As may be understood, each of the plurality of sensor data 224 is associated with a corresponding sensorfrom the plurality of sensors of the sensor array 102.

Although the present example describes that the processing module 106 obtains the plurality of sensor data 224 at a beginning of the operation. However, such implementation of the processing module 106 should not be construed as a limitation. In other implementations of the present subject matter, receiving of the plurality of sensor data 224may be a continuous process. In this regard, at least one sensor data may be received at the beginning of theprocessing or analysis, while other sensor data from the plurality of sensor data 224 may be received in due course of time. As a result, the plurality of sensor data 224 may be analysed as they are obtained.

Returning to the present example, the processing module 106 analyses the plurality of sensor data 224, based on the user identifier 226 and a state of the sensor array 102. The state of the sensor array 102 is one of transientstate and steady state. As may be understood, a sensor may exist in at least twostates, namely, transient state and steady state. It may be noted that the steady state of a sensor is a condition of equilibrium in a network or circuit that may be achieved after the effects of the transients are no longer viable in the circuit. In particular, the steady state is achieved after the initial oscillations or turbulence dissipates. Moreover, when the sensor achieves the steady state, the sensor is considered to be stable. On the other hand, the transient state is the state of thesensor after the activation or supply of power, and before it achieves the steady state. The sensor may be in transient state during a time between the activation of the sensor and the steady state. Therefore, during the transient state, a process variable or variable may change in the circuit resulting in asynchronous interfacing between the DAQ system 104 and the sensor. For example, the process variable may be current, and voltage.

The processing module 106 may obtain user profile data, based on theuser identifier 226. As mentioned previously, the user identifier 226may be associated with the user profile, wherein the user profile comprises user history data. The user profile data may include, for example, identification information, medical history, medication history, family medical history, treatment history, andmedical directives of the user. In an example, based on the user history data anduser identification, the processing module 106 may predict desired output interval for the sensors. In an example, on determining that the user is undergoing chemotherapy, based on medical history and medication history, the processingmodule 106 may determine that body temperature of the user may be higher dueto lower white blood cell (WBC) count. In such a case, the desired output intervalfor the sensors may be higher than 99°C, wherein the sensors measure body temperature of the user.

The processing module 106 analyses the plurality of sensor data 224todetermine a set of transient state sensor data from the plurality of sensor data 224. The set of transient state sensor data corresponds to the transient state of the sensor array 102. In an example, the processing module 106 may determine desired output intervals for the sensors of the sensor array 102. In this regard, the processing module 106 determines an amount of time taken by the sensors of the sensor array 102 to output sensor data that is within the corresponding desired output intervals. In addition, the processing module 106 may determine time taken by the sensor array 102 to achieve an equilibrium state such that it receives constant power supply, i.e., steady state. Based on the time taken by the sensors to output sensor data within desired output intervals and to achieve the steady state, the processing module 102 may determine a time period corresponding to the transient state. Subsequently, the processing module 106 may determine the set of transient state sensor data acquired from the sensor array 102 during the transient state of the sensors. In an example, the sensorarray 102 may achieve the steady state after five minutes from activation. In sucha case, the set of transient state sensor data may correspond to sensor dataacquired during the first five minutes from operation of the sensor array 102.

Once determined, the processing module 106 may filter out the set of transient state sensor data from the plurality of sensor data 224to obtain steady state sensor data. In an example, the processing module 106 may identify the setof transient state sensor data from the plurality of sensor data 224 based on time-stamp associated with each of the plurality of sensor data 224. Further, the processing module 106 may delete or remove the set of transient state sensor data from the plurality of sensor data 224. In this manner, the processing module 106 may obtain steady state sensor data for the monitoring session that the user is undergoing.

After obtaining the steady state sensor data, the processing module 106 may cause to store the steady state sensor data. In an example, the processing module 106 may associate the steady state sensor data with the useridentifier and the user profile. In this regard, the steady state sensor data may be stamped with the user identifier to ensure privacy of the data. Further, the processing module 106 may cause to store the steady state sensor data within the storage module 214 for further processing and analytics.

To such an end, the steady state sensor data is stored in a user- readable manner, such that the steady state sensor data is in association with the user profile or the user identifier. It may be noted that once the steady state sensor data is stored within the storage module 214, the plurality of sensor data 224 associated with the user may be deleted. In this manner, no unnecessary data is stored within the storage module 214 thereby optimizing its use. Examplesof the storage module 214 may include, but is not limited to, non-volatile memory, such as Read Only Memory (ROM), Erasable Programmable ROMs (EPROMs), flash memories, hard disks, optical disks, and magnetic tapes.

The steady state sensor data is stored locally within the storage module 214, in user-readable format. This enables a medical staff, such as a paramedic, a nurse, or a doctor, to access the steady state sensor data instantly without any communication means. In an example, the storage module 214 maybe implemented as a flash memory. In such a case, a flash memory card may bewithdrawn from a flash memory slot of the DAQ system 104 to instantly access the steady state sensor data. As the steady state sensor data is free of noise, thediagnosis based on the steady state sensor data is accurate.

In certain cases, the processing module 106 may cause to transmit thesteady state sensor data to a central system, using the communication module 216. The communication module 216 may be electrically connected to the processing module 106 and the storage module 214. The communication module216 may establish a data communication interface between the DAQ system lOOand a central system. As would be understood, such data communication interface may be wired or wireless, and direct or via other communication means. The communication module 216 may enable the DAQ system 104 to communicate over existing wired or wireless networking technologies, for example, cellular network, Ethernet, LTE, Internet, Bluetooth, and Wi-Fi. Further, the central system may be a server, a computing system associated with the userof the wearable electronic system 100, or a user device.

Further, the fault detection module 218 may implement a fault detection mechanism for the wearable electronic system 100. In one example, the fault detection module 218 may detect a hardware error within the DAQ system 104 and/or the sensor array 102, such as loose connections, faulty connections, faulty sensor, or sensor misplacement. In an example, the fault detection module 218 may be implemented as a software program within the DAQ system 104 or the processing module 106. In another example, the fault detection module 218 may be implemented as a hardware circuitry within the DAQ system 104. In operation, the fault detection module 218 may analyse the plurality of sensor data 224, based on the user identifier and the state of the sensor array 102. For example, the fault detection module 218 may detect the hardware errorbased on high impedance state within the DAQ system 104 or the sensor array 102, an out of range output signal from a selected sensor of the sensor array 102, or an out of range sensor data. Based on the analysis, the fault detection module 218 may determine if working condition of any of the plurality of sensors of the sensor array 102 is incorrect. The fault detection module 218 may identify an outof range output signal or sensor data, based on the user history data and the sensor array 102. In an example, the fault detection module 218 may detect occurrence of a fault, i.e., incorrect working condition of a sensor when output sensor data from the sensor is out of range while the sensor array 102 is in steadystate and user history does not indicate any previous health ailments.

On determining incorrect working condition of the sensor from the plurality of sensors, the fault detection module 218 may determine an indicative feature associated with the sensor. The indicative feature may be executable ona plurality of indicators. In an example, the plurality of indicators may be implemented as Light Emitting Diodes (LEDs), wherein at least three LEDs, suchas RGB (red- blue- green) LEDs, may be provided within the DAQ system 104. Although in present example, the plurality of indicators are implemented as LEDs, however, such example should not be construed as a limitation. In other examples of the present subject matter, the DAQ system 104 may include other type of indicators to indication of fault visually, in audio, or a combination thereof.

On detection of the hardware error, the fault detection module 218 may notify the user of the wearable electronic system 100 using the plurality of indicators. In this regard, the fault detection module 218 may access the indicative features 228 to identify the indicative feature corresponding to the faulty sensor. In an example, the indicative features 228 may include a unique colour combination for each sensor of the sensor array 102. For example, to indicate thefailure of ‘A’ sensor from the sensor array 102, a unique colour combination of the LEDs corresponding to the ‘A’ sensor is identified. Further, the fault detection module 218 may cause the plurality of indicators to execute the indicative feature to indicate incorrect working condition of the sensor. In this regard, the unique colour combination corresponding to the ‘x’ sensor may light up on the LEDs to indicate fault in the ‘A’ sensor of the sensor array.

This enhances reliability of the wearable electronic system 100 and the DAQ system 104. Moreover, such LED indicators are low cost and easy to operate, thereby preventing any substantial increase in cost of the DAQ system 104. As the LED indicators are used, faulty sensor of the sensor array 102 may be identified without using any power intensive technology, such as display device. Therefore, fault indication is provided without extra power consumption overhead or complex firmware deployment. The fault indication using the LED indicators would be easy to debug or identify the non-working or faulty sensors from a large number of sensors, i.e., the plurality of sensors of sensor array 102.

The DAQ system 104 enables portable data carrier that facilitates dataacquisition and storage from the sensor array 102. The DAQ system 104 is equipped with a digital interface for smooth acquisition and sending of bio- physical signals to a central system. Further, the DAQ system 104 may be adaptable to connect with any type of analog front-end module 212, wherein the analog front-end module enables data acquisition from any type of sensors.

FIG. 3 illustrates a perspective view of a wearable electronic system300 including a sensor array 302 and a DAQ system 304, as per an example.The sensor array 302 may include a plurality of resistive sensors (depicted as a first resistive sensor 306A and a second resistive sensor 306B). In one example, the wearable electronic system 300 may include more than 30 sensors. The firstresistive sensor 306A and second resistive sensor 306B (collectively referred to as sensors 306) may be thermal sensors, wherein the thermal sensors may acquire skin surface temperature of a user. In one example, the sensors 306 may sense body temperature for multiple points on breast of the user. In such a casethe wearable electronic system 300 may be capable of detecting breast cancer disease in a cost-effective and comfortable way.

As illustrated in FIG. 3, the sensor array 302 may be electrically coupled to the DAQ system 304. In one example, the sensor array 302 may be connected to the DAQ system 304 via a plug in receptacle connector (not shownin FIG. 4). The plug-in receptacle connector may include a first portion and a second portion complimentary to the first portion. For example, the first portion ofthe plug-in receptacle connector may extend from the sensor array 302 while thesecond portion of the plug-in receptacle connector may extend from the DAQ system 304. To this end, the first portion of the plug-in receptacle connector maybe attached to a first end of a first cable wherein a second end of the first cable may be attached to the sensor array 302. Similarly, the second portion of the plug-in receptacle connector may be attached to a first end of a second cable wherein a second end of the second cable may be attached to the DAQ system 304. The plug-in receptacle connector may be, for example, a mechanicalconnector, a magnetic male female pogo-pin connector, or a circular lemoconnector.

The first portion of the plug-in receptacle connector may receive the second portion of the plug in receptacle connector to enable coupling between the sensor array 302 and the DAQ system 304. Moreover, the plug-in receptacleconnector may provide electrical as well as mechanical coupling between the sensor array 302 and the DAQ system 304. In certain cases, two or more plug-inreceptacle connectors may be used for coupling the sensor array 302 and the DAQ system 304. Accordingly, two or more cables may extend from each of thesensor array 302 and the DAQ system 304. In one example, the cables may be Flexible Printed Cables (FPC) and the plug-in receptacle connector may be an FPC connector or Flat Flex connector (FFC). In addition, wired asynchronous interface between the sensor array 302 and the DAQ system 304 using the cables ensures that there are no timing constraints or pairing requirements with the DAQsystem 304. This allows the DAQ system 304 to acquire sensor data from the sensor array 302 at any time.

As mentioned previously, the DAQ system 304 may include a processing module (such as the processing module 106) electrically connected to the sensor array 302, a power source, an analog front-end module (such as the analog front-end module 212), a storage module (such as the storage module214), a communication module (such as the communication module 216), and a fault detection module (such as the fault detection module 218). In an example, the analog front- end module 212 is further electrically connected to the sensor array 302 and the power source 302. Further, the DAQ system 304 may includea power button 308to activate the wearable electronic system 300. The user mayactuate the power button308 to activate the sensor array 302 and the DAQ system 304.

In an example, the processing module 106 of the DAQ system 304 is implemented using an integrated radio frequency (RF) controller that has two separate controller stacks. In particular, a first controller of the RF controller stackperforms the operations associated with data acquisition from the sensor array 302, data storage within the storage module 214, communication using thecommunication module 216, and fault detection using the fault detection module 218. On the other hand, a second controller of the RF controller stack supports over the air firmware updates. This allows the firmware of the DAQ system 304 to be updated wirelessly anywhere, without a need to transport the system 300 to development centre.

In operation, the processing module 106 receives a user identifier (such as the user identifier 226) associated with the user of the system 300. Thereafter, the processing module 106 triggers the sensors 306 of the sensor array 302 to acquire sensor data. In an example, the processing module 106 mayacquire sensor data from the first sensor 306-A during a first time period. Thereafter, the processing module 106 may acquire sensor data from the second sensor 306-B during a second time period. In this manner, the processing modulel06 may acquire sensor data from each of the sensors 306 in a sequential manner. In an example, the processing module 106 may perform multiple iterations of data acquisition from each of the sensors 306 of the sensor array 302. The processing module 106 may acquire the plurality of sensor data (such as the plurality of sensor data 224) from the sensor array, wherein the plurality ofsensor data 224 may include multiple readings or sensor data from each of the sensors 306.

Continuing further, based on the user identifier 226 and a state of the sensor array302, the processing module 106 analyses the plurality of sensor data 224. The processing module 106 determines the state of the sensor array 302 asone of a transient state and a steady state. For example, based on the user identifier 226, the processing module 106 may determine user history data. In anexample, the processing module 106 may retrieve the user history data from thestorage module 214 of the DAQ system 304. In another example, the processing module 106 may communicate with a central system, such as a central server toacquire the user history data. In such a case, the processing module 106 maycommunicate with the central system using the communication module 216. Based on the user history data and the state of the sensor array 302, the processing module 106 determines a time period of transient state of the sensor array 302. In an example, the processing module 106 may determine theset of transient state sensor data from the plurality of sensor data 224, based on timestamp associated with each of the plurality of sensor data 224. Thereafter, the processing module 106 may filter out the set of transient state sensor data from the plurality of sensor data 224 to obtain steady state sensor data.

In an example, on completion of a first iteration of data acquisition fromthe sensor array 302, the processing module 106 may initiate a second iteration of data acquisition from the sensor array 302. Such iterative data acquisition maybe performed in continuous manner until a specified number of iterations is reached, or the DAQ system 304 is powered OFF. In such a case, sensor data acquired for first three iterations of data acquisition from the sensor array 302 may correspond to transient state. In such a case, the sensor data corresponding to the first three iteration may be deleted to obtain the steady state sensor data.

Thereafter, the processing module 106 may cause to store the steadystate sensor data within the storage module 214. In an example, the processingmodule 106 may associate the user identifier 226 of the user with the steady statesensor data prior to storing within the storage module 214. In an example, the processing module 106 may store the steady state sensor data as a log file withinthe storage module 214. Typically, the DAQ system 304 may operate in ‘ Offline ’mode i.e., when the DAQ system 304 is not connected to any external wired or wireless communication network. Moreover, the processing module 106 may operate to associate the steady state sensor data with the user identifier 226 in ‘ Offline ’ mode. As the user’s data is stamped with the user identifier and storageand power of the DAQ system 304 is optimized, multiple users may use the system 300in the ‘ Offline ’ mode while ensuring privacy of user data.

In certain cases, the fault detection module 218 may analyse the plurality of sensor data 224 to determine if working condition of any of the plurality of sensors 306 of the sensor array 302 is incorrect. On determining incorrect working condition of a sensor, such as the first sensor 306- A, the fault detection module 218 may determine an indicative feature associated with the first sensor306-A. The indicative feature may be a unique colour code executable on the plurality of indicators (depicted as indicators 310-A, 310-B, 310-C). In an example, the indicators 310- A, 310-B, 310-C may be RGB LEDs. To this end, thefault detection module 218 may cause the indicators 310-A, 310-B, 310-Cto execute the indicative feature to indicate incorrect working condition of the first sensor 306-A.

The processing module 106 may further cause to transmit the steady state sensor data, stamped with the user identifier 226, to the central system. In this regard, the processing module 106 may transmit the steady state sensor datausing the communication module 216, in a wired or wireless manner. Due to thestamping, steady state sensor data or user data for different users may be transmitted while ensuring user-data identification and security.

The DAQ system 304 may have a split architecture. In particular, one or more storage modules, such as the storage modules 214 may be stacked in the DAQ system 304 as add-on accessory. Further, such stack of the storage modules may connect to the processing module 106 for acquisition and storage of bio-physical signals from the sensors 306.

In an example, the central system may generate a user report, based on the user identifier, and the steady state sensor data. In this regard, the centralsystem may process the user history data and the steady state sensor data corresponding to the monitoring session undergone by the user. Subsequently, the central system may generate the user report that indicates a health conditionof the user, determined by examining the user history data, the steady state data, and ideal references . The user report may contain information about the wearable electronic system 300, diseases monitored by the wearable electronic system 300, the ideal references, the physiological parameters monitored by the wearable electronic system 300, and the steady state sensor data. This information may help the user to identify any anomaly in order to consult a doctor. Further, a medical staff may use the user report for interpretation, diagnosis, andtreatment.

Further, the processing module 106 may cause to store the steady state sensor data and the generated user report within the central system. In thisregard, the processing module 106 may cause to stamp the steady state sensor data and the generated user report with the user identifier and store within the central system. For example, user reports corresponding to a plurality of users of different wearable electronic systems, such as the wearable electronic system 300, may be stored at the central system. This may enable the user to access theuser report for analysis from anywhere. Such user report may be available to userin multiple file formats, including .pdf, UniPlot IPZ, and .csv

For example, the processing module 106 may initiate such transmission of the steady state sensor data in Online ’ mode, such as when theDAQ system 304 has substantial power or is being charged. This reduces chances of shutdown of the DAQ system 304 during the transmission, thereby preventing loss of data during transmission. In an example, the processing module 106 may deactivate the communication module 216, thereby switching the DAQ system 304 from the ‘ Online ’ mode to Offline ’ mode, when power or charge of the DAQ system 304 drops below a pre-defined threshold. As mentioned previously, in ‘ Offline ’ mode, the processing module 106 may cause to store the steady state sensor data within the local storage module 214, such as a flash memory. This optimizes power consumption of the DAQ system 304.

During operation, the user may re-actuate the power button 308. In such a case, the processing module 106 may pause receiving the plurality of sensor data 224 from the sensor array302. Moreover, the processing module 106may cause to switch the DAQ system 304 into the ‘ Offline ’ mode, when paused.

In one example, the DAQ system 304 may have a length in a range of 80mm to 150mm. Further, a width of the DAQ system 304 may be in a range of 30mm to 60mm. Additionally, a height of the DAQ system 304 may be in a rangeof 15mm to 35mm. Owing to compact size and robust hardware of the DAQ system 304 and precise data acquisition, the DAQ system 304 may be capable of medical applications. It may be noted that use of the DAQ system 304 for determining body temperature of the user for detection of breast cancer diseaseis only illustrative and should not be construed as limiting in any way. Further, the DAQ system 304 may be used in many applications to acquire bio-physical signals for calculating other physiological parameters, or other parameters.

FIG. 4 illustrates a flow diagram depicting a method 400for acquiring data from a sensor array, as per an example. The sensor array 102 may includea plurality of resistive sensors. The order in which the method 400 is described isnot intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method 400, or an alternative method. Furthermore, method 400 may be implemented by a processing resource through any suitable hardware, non-transitory machine- readable instructions, or combination thereof.

At block 402, a user identifier associated with a user is received. The user identifier 226 may be a unique identifier associated with the user. The user identifier may be associated with a user profile corresponding to the user. The user profile may include user history data. In an example, the user identifier 226may be sequence of characters, numbers, symbols, special characters, or a combination thereof.

At block 404, a plurality of sensor data is received from a sensor array .The plurality of sensor data 224 may include multiple iterations of sensor readingsfrom each of the sensors of the sensor array 102. Subsequently, the plurality of sensor data 224 may include multiple reading from each sensor of the sensor array 102. As may be understood, each of the plurality of sensor data 224 is associated with a corresponding sensor from the plurality of sensors 102. For example, the plurality of sensor data 224 may be time-stamped. Moreover, an identification of a sensor may be associated with sensor data acquired from the sensor.

At block 406, the plurality of sensor data is analysed, based on the user identifier and a state of the sensor array. The plurality of sensor data 224 isanalysed to determine a set of transient state sensor data from the plurality of sensor data 224. The state of the sensor array 102 is one of transient state and steady state. In an example, the sensors of the sensor array 102 may be stable or in equilibrium in the steady state, and the sensors may achieve the steady state after the transient state. For example, the plurality of sensor data 224 may be analysed based on the user history data and the state of the sensor array 102to determine the set of transient state sensor data that corresponds to the transient state of the sensor array 102.

At block 408, the set of transient state sensor data is filtered out from the plurality of sensor data to obtain steady state sensor data. The steady state sensor data corresponds to the steady state of the sensor array. In an example, the set of transient state sensor data is deleted from the plurality of sensor data 224.

At block 410, the steady state sensor data is stored. In an example, the steady state sensor data is stored within a storage module 214. For example, the storage module 214 may be a removable memory device, such as flash memory. Further, the storage module 214 may continuously store the time- stamped steady state sensor data in a prescribed format. In particular, the steadystate sensor data may be stamped with the user identifier. In this manner, the steady state sensor data may be stored with no Internet or communication network while ensuring security and privacy of user data. Subsequently, the useror a medical staff can easily access the steady state sensor data of the user fromthe storage module 214 for quick diagnostics. In an example, the steady state sensor data of the user may be stored as log file within the storage module 214. Due to operation in Offline mode, the DAQ system 104 may operate for longer duration ranging from 6 to 7 hours by using a single battery source.

Further, such log file may be transmitted to a central system, such as a central server, using a communication module 216. The steady state sensor data may then be stored at the central system in association with the user profileand user identifier. Subsequently, the steady state sensor data may be accessed via existing wireless connectivity technologies such as Bluetooth low energy or email. Moreover, a user report may be generated for the user, based on the userprofile and the steady state sensor data. Such user report and the steady state sensor data may be accessed by the user from the central system. To such an end, when several wearable electronic systems, such as the system 100, are in operation at a same time, their sensor data may be transmitted sequentially to the central system. This may avoid a possibility of data loss and collision. In an example, the user may trigger transmission of the log file by pressing a button onthe DAQ system 104.

FIG. 5 illustrates a computing environment 500 implementing a non- transitory computer readable medium for data acquisition and storage of sensor data from a sensor array. In an example, the computing environment 500 includesprocessor(s) 502 communicatively coupled to a non-transitory computer readable medium 504 through a communication link 506. In an example, the processor(s)502 may have one or more processing resources for fetching and executing computer-readable instructions from the non-transitory computer readable medium 504. The processor(s) 502 and the non-transitory computer readable medium 504 may be implemented, for example, in system 104 (as has been described in conjunction with the FIGS. 1 and 2).

The non-transitory computer readable medium 504 may be, for example, an internal memory device or an external memory device. In anexample implementation, the communication link 506 may be a network communication link. The processor(s) 502 and the non-transitory computer readable medium 504 may be communicatively coupled to a sensor array508 (similar to the sensor array 102) over the network.

In an example implementation, the non-transitory computer readable medium 504 includes a set of computer readable instructions 510 which may be accessed by the processor(s) 502 through the communication link 506. Referringto FIG. 5, in an example, the non-transitory computer readable medium 504 includes instructions 510 that cause the processor(s) 502 to receive a user identifier associated with a user. In an example, the user identifier uniquely identifies the user based on a user profile. For example, the user profile may be associated with the user identifier and may include user history data. The instructions 510 may cause the processor(s) 502 to receiving a plurality of sensordata from the sensor array 508. Each of the plurality of sensor data, such as theplurality of sensor data 224, is associated with a corresponding sensor from the plurality of sensors. In an example, a sensor data acquired from a sensor may correspond to a value of resistance within the sensor. The value of the resistancemay then be used for determining, for example, temperature, pressure, force, humidity, and displacement. For example, conversion of the resistance may be performed based on a lookup table, or by solving Steinhart-Hart equations, to obtain the sensor data from the value of resistance.

The non-transitory computer readable medium 504 includes instructions 510 that cause the processor(s) 502 to analyse the plurality of sensordata 224 to determine a set of transient state sensor data from the plurality of sensor data 224. In an example, the plurality of sensor data 224 maybe analysedbased on the user identifier and a state of the sensor array. The state of the sensor array is one of transient state and steady state. For example, based on the user history data and the state, an amount of time of the transient state determined. To such an end, the set of transient state sensor data is determined.

The non-transitory computer readable medium 504 includes instructions 510 that cause the processor(s) 502 to filter out the set of transient state sensor data from the plurality of sensor data 224 to obtain steady state sensor data. The steady state sensor data corresponds to the steady state of the sensor array 508. Further, the non-transitory computer readable medium 504 includes instructions 510 that cause the processor(s) 502 to cause to store the steady state sensor data within a storage module, such as the storage module 214.1n an example, the steady state sensor data is stored as a log file stamped with the user identifier.

Although implementations of present subject matter have been described in language specific to structural features and/or methods, it is to be noted that the present subject matter is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed and explained in the context of a few implementations for the present subject matter.

Claims

T/We Claim:

1 . A wearable electronic system, comprising: a sensor array comprising a plurality of resistive sensors, wherein the sensor array is to acquire a plurality of sensor data associated with a user; a data acquisition (DAQ) system coupled to the sensor array, DAQ system comprising a processing module, wherein the processing module is to: receive a user identifier associated with the user; receive the plurality of sensor data from the sensor array, wherein each of the plurality of sensor data is associated with a corresponding sensor from the plurality of sensors; based on the user identifier and a state of the sensor array, analysethe plurality of sensor data to determine a set of transient state sensor datafrom the plurality of sensor data, wherein the state of the sensor array is one of transient state and steady state, and the set of transient state sensor data corresponds to the transient state of the sensor array; filter out the set of transient state sensor data from the plurality of sensor data to obtain steady state sensor data, wherein the steady state sensor data corresponds to the steady state of the sensor array; and cause to store the steady state sensor data.

2. The wearable electronic system as claimed in claim 1, wherein theprocessing module is to: create a user profile for the user, based on the user identifier; associate the steady state sensor data with the user identifier; and cause to store the steady state sensor data in association with the userprofile.

3. The wearable electronic system as claimed in claim 2, wherein the user identifier is associated with the user profile, the user profile comprising userhistory data, and wherein the processing module is to analyse the plurality of sensor data based onthe user history data.

4. The wearable electronic system as claimed in claim 1, wherein the DAQ system comprises a storage module, and wherein the processing module is tocause to store the steady state sensor data within the storage module.

5. The wearable electronic system as claimed in claim 1, wherein the DAQsystem comprises a communication module, and wherein the processing module is to cause transmission of the steady state sensor data to a central system, using the communication module.

6. The wearable electronic system as claimed in claim 5, wherein the centralsystem is configured to generate a user report, based on the user identifier, and the steady state sensor data.

7. The wearable electronic system as claimed in claim 1, the DAQ system further comprising a fault detection module and a plurality of indicators, wherein the fault detection module is to: analyse the plurality of sensor data, based on the user identifier and the state of the sensor array; based on the analysis, determine if working condition of any of the pluralityof sensors of the sensor array is incorrect; on determining incorrect working condition of a sensor from the plurality of sensors, determine an indicative feature associated with the sensor, the indicative feature being executable on the plurality of indicator modules; and cause the plurality of indicator modules to execute the indicative feature toindicate incorrect working condition of the sensor.

8. The wearable electronic system as claimed in claim 1, wherein the DAQ system further comprises an analog front-end module, wherein the analog front-end module is configured to select a first sensor from the plurality of resistive sensors of the sensor array; receive an output signal acquired by the first sensor, wherein the signal is associated with the user; and provide the output signal to the processing module.

9. The wearable electronic system as claimed in claim 8, wherein the signalacquired by the first sensor is a bio-physical signal associated with the user.

10. The wearable electronic system as claimed in claim 1, wherein the DAQsystem further comprises a power button, wherein the power button, when actuated, causes the processing module to initiatereceiving the plurality of sensor data from the sensor array; or the power button, when re-actuated, causes the processing module topause receiving the plurality of sensor data from the sensor array.

11. A method comprising: receiving a user identifier associated with a user; receiving a plurality of sensor data from a sensor array, wherein each of the plurality of sensor data is associated with a corresponding sensor from the plurality of sensors; based on the user identifier and a state of the sensor array, analysing theplurality of sensor data to determine a set of transient state sensor data from theplurality of sensor data, wherein the state of the sensor array is one of transient state and steady state, and the set of transient state sensor data corresponds to the transient state of the sensor array; filtering out the set of transient state sensor data from the plurality of sensor data to obtain steady state sensor data, wherein the steady state sensordata corresponds to the steady state of the sensor array; and causing to store the steady state sensor data.

12. The method as claimed in claim 11, wherein the user identifier isassociated with a user profile corresponding to the user, the user profile comprising user history data, and the analysing the plurality of sensor data basedon the user identifier and the state of the sensor array, comprises: analysing the plurality of sensor data, based on the user history data andthe state of the sensor array.

13. The method as claimed in claim 12, the method further comprising: associating the steady state sensor data with the user identifier; transmitting of the steady state sensor data to a central system; causing to store the steady state sensor data in association with the userprofile; and generating a user report, based on the user profile and the steady statesensor data.

14. A non-transient computer readable medium containing program instruction for causing a computer to perform a method for storing data obtained from a sensor array, the sensor array comprising a plurality of resistive sensors, the method comprising: receiving a user identifier associated with a user; receiving a plurality of sensor data from a sensor array, wherein each of the plurality of sensor data is associated with a corresponding sensor from the plurality of sensors; based on the user identifier and a state of the sensor array, analysing theplurality of sensor data to determine a set of transient state sensor data from theplurality of sensor data, wherein the state of the sensor array is one of transient state and steady state, and the transient state sensor data corresponds to the transient state of the sensor array; filtering out the set of transient state sensor data from the plurality of sensor data to obtain steady state sensor data, wherein the steady state sensordata corresponds to the steady state of the sensor array; and causing to store the steady state sensor data.

15. The non-transient computer readable medium as claimed in claim 11 , thecomputer readable medium containing program instruction for causing acomputer to perform the method for storing data, the method further comprising:analysing the plurality of sensor data, based on the user identifier and the state of the sensor array; based on the analysis, determining if working condition of any of theplurality of sensors of the sensor array is incorrect; on determining incorrect working condition of a first sensor from theplurality of sensors, determining an indicative feature associated with the first sensor, the indicative feature being executable on a plurality of indicator modules;and causing the plurality of indicator modules to execute the indicative featureto indicate incorrect working condition of the first sensor.