Multi-sensor-based data collection device and optimization method

Abstract

The present invention relates to an optimization system for data collection of a bio-signal collection module, comprising: a sleep mode switching unit that detects a use state of a transmission device and switches the transmission device to a sleep mode if the transmission device is not in use; a data acquisition unit that collects data through the bio-signal collection module if the transmission device is in the sleep mode; a wake mode switching unit that activates only a CPU of the transmission device to switch to a wake mode if it is a wake period or data transmission is required; and a data processing unit that transmits the data collected in the sleep mode to a reception device through wireless communication if the transmission device is in the wake mode.

Description

Multi-sensor based data acquisition device and optimization method

[0001] The present invention was carried out under the support of the Ministry of Science and ICT under project unique number 1711198799 and project number 00265393, wherein the research management agency for the said project is the National Research Foundation of Korea, the research project name is “Brain Science Leading Convergence Technology Development Project”, the research project name is “Development and Verification of Deep Learning-based Cerebrovascular Disease in Slilico Model Using Multimodal Database”, the lead institution is Samsung Seoul Hospital, and the research period is July 1, 2023 – December 31, 2025.

[0002] The present invention relates to a data collection device and an optimization method, and more particularly to a data collection device and an optimization method for efficiently collecting long-term data based on multiple sensors.

[0003]

[0004] In modern society, the use of wearable devices is rapidly increasing, and devices such as smartwatches, in particular, are being widely used for healthcare and personalized health monitoring. These devices provide users with precise data through various sensors that measure heart rate, blood flow, body movement, and environmental information. For example, photoplethysmography (PPG) sensors and IMU sensors can collect physiological and environmental data from users and transmit it to external devices such as smartphones. Through this, users can receive real-time health management and feedback, and these devices are being effectively utilized in fields such as healthcare and fitness.

[0005] However, wearable devices have limited battery capacity, so efficient power management is essential for long-term use. To this end, the device is designed to switch to sleep mode when not in use to minimize battery consumption. In sleep mode, the operation of the CPU and other key hardware is restricted, and various power-saving techniques are applied to minimize energy consumption.

[0006] Nevertheless, battery drain remains a problem in the process where wearable devices continuously collect data in the background and transmit it to external devices. In particular, the process of periodically collecting sensor data and transmitting it via Bluetooth requires continuous CPU operation, which can shorten battery life. This issue acts as a factor that hinders user convenience and the practicality of the device.

[0007] Conventional power saving mode switching functions alone are insufficient to adequately address the battery drain caused by continuous data collection and transmission. In existing technology, the CPU had to operate continuously to collect and transmit sensor data even when the device was in standby mode, which had limitations in reducing energy consumption. Furthermore, the method of activating the entire CPU during data processing and transmission resulted in unnecessary energy consumption, thereby hindering battery efficiency.

[0008] Accordingly, the present invention proposes a method that enables accurate data collection and stable transmission in the background while effectively reducing battery consumption by applying a complex power-saving algorithm that minimizes battery consumption by switching the device to sleep mode when the user is not using the wearable device, and temporarily activates only specific functions of the CPU to perform necessary processing during data collection and transmission, and then switches it back to sleep mode. This algorithm is expected to maximize energy efficiency by selectively activating only specific functions instead of activating the entire CPU, and to extend battery life by optimizing sensor data processing and Bluetooth transmission processes.

[0009]

[0010] Accordingly, the present invention aims to provide a multi-sensor-based data collection device and an optimization method that enables the collection of user life log data for an extended period by more efficiently managing the battery usage of the device.

[0011]

[0012] To achieve the above objective, the present invention comprises, in an optimization system for data collection of a biosignal collection module, a standby mode switching unit that detects the usage status of a transmitting device and switches the transmitting device to a sleep mode when not in use; a data acquisition unit that collects data through a biosignal collection module when the transmitting device is in a sleep mode; an active mode switching unit that switches to a wake mode by activating only the CPU of the transmitting device when an active cycle occurs or data transmission is required; and a data processing unit that transmits data collected in the sleep mode to the receiving device via wireless communication when the transmitting device is in a wake mode.

[0013] Preferably, the biosignal collection module may include a first collection module that collects at least one of acceleration data, angular velocity data, and geomagnetic field data, and a second collection module that collects at least one of green PPG data, red PPG data, and infrared PPG data.

[0014] Preferably, the transmitting device can perform data preprocessing and data compression operations, which are operations for transmitting collected data, in an active mode.

[0015] Preferably, the data processing unit may cause the standby mode switching unit to switch the transmitting device to standby mode after data transmission, or release the standby mode when a termination condition is satisfied.

[0016] Preferably, the data processing unit may be deemed to have satisfied the termination condition when it corresponds to at least one of the termination of the application, a transition to a charging state, and a low battery state.

[0017] In addition, the present invention is further characterized by comprising: a step of detecting the usage status of a transmitting device and switching the transmitting device to sleep mode when not in use; a step of collecting data through a biosignal collection module when the transmitting device is in sleep mode; a step of switching to wake mode by activating only the CPU of the transmitting device when an active cycle occurs or data transmission is required; and a step of transmitting the data collected in sleep mode to the receiving device via wireless communication when the transmitting device is in wake mode.

[0018] Preferably, the step of switching to the standby mode and the step of transmitting to the transmitting device may be repeated until the transmission from the transmitting device to the receiving device is repeated a certain number of times.

[0019]

[0020] According to the present invention, there is an advantage in that battery usage can be optimized by efficiently managing standby mode and active mode.

[0021] In addition, the present invention has the advantage of maximizing battery usage time, thereby enabling the analysis of more data with the same battery capacity.

[0022]

[0023] Figure 1 shows a configuration diagram of an artificial intelligence-based data compression and restoration system according to an embodiment of the present invention.

[0024] Figure 2 shows a configuration diagram of a biosignal collection module according to an embodiment of the present invention.

[0025] FIG. 3 shows a flowchart of a second collection module according to an embodiment of the present invention.

[0026] Figure 4 shows a configuration diagram of a multi-sensor-based data collection device and optimization system according to an embodiment of the present invention.

[0027] FIG. 5 shows a detailed flowchart of a multi-sensor-based data collection device and optimization method according to an embodiment of the present invention.

[0028] FIG. 6 shows a flowchart of data collection between a smartwatch and a smartphone in a multi-sensor-based data collection device and optimization method according to an embodiment of the present invention.

[0029] FIG. 7 shows a flowchart of an encoder through an AI-based first compression module and a decoder through a first restoration module in an AI-based data compression and restoration system according to an embodiment of the present invention.

[0030] FIG. 8 shows a flowchart of data transmission between a smart watch and a smartphone in an artificial intelligence-based data compression and restoration system according to an embodiment of the present invention.

[0031] FIG. 9 shows a specific flowchart of data transmission between a smartwatch and a smartphone in an artificial intelligence-based data compression and restoration system according to an embodiment of the present invention.

[0032]

[0033] In an optimization system for data acquisition of a biosignal acquisition module,

[0034] A sleep mode switching unit that detects the usage status of a transmitting device and switches the transmitting device to sleep mode when not in use;

[0035] A data acquisition unit that collects data through a biosignal collection module when the above-mentioned transmitting device is in standby mode;

[0036] An active mode switching unit that switches to active mode (wake mode) by activating only the CPU of the transmitting device when an active cycle occurs or data transmission is required; and

[0037] A data processing unit that transmits data collected in standby mode to the receiving device via wireless communication when the transmitting device is in active mode;

[0038] A data collection optimization system characterized by including

[0039]

[0040] The present invention will be described in detail below with reference to the contents described in the attached drawings. However, the present invention is not limited or restricted by exemplary embodiments. Identical reference numerals in each drawing indicate components that perform substantially the same function.

[0041] The purpose and effects of the present invention may be naturally understood or become clearer through the following description, and the purpose and effects of the present invention are not limited solely to the description below. Furthermore, in describing the present invention, if it is determined that a detailed description of known technology related to the present invention may unnecessarily obscure the essence of the present invention, such detailed description will be omitted.

[0042] The terms used in this invention are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the description of the invention, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0043] Terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0044] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this invention.

[0045] In interpreting the components, they are interpreted to include a margin of error even without a separate explicit indication. In the case of descriptions regarding temporal relationships, for example, where the temporal sequence is described using 'after,' 'following,' 'next,' 'before,' etc., cases that are not continuous are included unless 'immediately' or 'directly' is used.

[0046] Hereinafter, the technical configuration of the present invention will be described in detail with reference to the attached drawings.

[0047] FIG. 1 shows a configuration diagram of an artificial intelligence-based data compression and restoration system (10) according to an embodiment of the present invention. Referring to FIG. 1, the artificial intelligence-based data compression and restoration system (10) can perform compression and restoration processes for data transmission between a transmitting device (100) and a receiving device (300). The transmitting device (100) may include a data collection unit (110), a preprocessing unit (130), a compression unit (150), and a transmission unit (170). The receiving device (300) may include a receiving unit (310), a restoration unit (330), and a storage unit (350).

[0048] In one embodiment, an artificial intelligence-based data compression and restoration system (10) may include a data collection unit (110), a compression unit (150), a transmission unit (170), and a restoration unit (330).

[0049] Specifically, the artificial intelligence-based data compression and restoration system (10) can perform data preprocessing through the preprocessing unit (130) at the transmitting device (100) and transmit the compressed data in the form of a packet to the receiving unit (310) of the receiving device (300). At the receiving device (300), the packet can be restored by the restoration unit (330). The restored data can be stored in the storage unit (350) of the receiving device (300).

[0050] In the artificial intelligence-based data compression and restoration system (10), the transmitting device (100) may be a wearable device, for example, a smartwatch.

[0051] The data collection unit (110) can collect life log data through a biosignal collection module (190) embedded in the transmitting device (100). The biosignal collection module (190) can be implemented as a software development kit (SDK) for collecting biosignals, and the software development kit may be a set of libraries and tools that support a developer in interacting with the sensor hardware of the device and collecting data.

[0052] The biosignal collection module (190) can collect biosignal data in real time, and the collected biosignal data can be obtained through a built-in 9-axis IMU (Inertial Measurement Unit) sensor and an LED sensor. In addition, life log data may be data collected through the biosignal collection module (190).

[0053] FIG. 2 shows a configuration diagram of a biosignal collection module (190) according to an embodiment of the present invention. The biosignal collection module (190) may include a first collection module (191) and a second collection module (193).

[0054] The first collection module (191) and the second collection module (193) may each utilize the Sensor Manager Software Development Kit (SSD), a general sensing module, and the Privileged Health Software Development Kit (Privileged Health SDK), a health-related module, as examples. The software development kits mentioned herein are examples for illustrative purposes only and do not limit the scope of the invention to specific tools.

[0055] The first collection module (191) can collect at least one of acceleration data, angular velocity data, and geomagnetic data. The accelerometer can measure data up to 8g, the gyroscope can measure data up to 2000° / s, and the magnetometer can measure data up to 2000μT. The data collected from these sensors can provide at least one of the user's movement, rotation, and direction.

[0056] The second collection module (193) can collect at least one of green PPG data, red PPG data, and infrared PPG data. It can measure data within the range of the maximum light signal that the photodetector of the PPG (photoplethysmography) sensor can measure. The data collected from these sensors detects changes in blood volume, thereby allowing the extraction of biosignals such as heart rate and oxygen saturation.

[0057] FIG. 3 shows a flowchart of a second collection module (193) according to an embodiment of the present invention. The second collection module (193) can operate in a batch mode that periodically collects and transmits data, and in an on-demand mode that transmits measured data in real time for real-time data processing.

[0058] In one embodiment, the Privilege Health software development kit can collect green PPG data with a sampling rate of 25 Hz. The Privilege Health software development kit can collect red PPG data and infrared PPG data with a sampling rate of 100 Hz. The green PPG data operates in batch mode, and the red PPG data and infrared PPG data operate in on-demand mode, so that both functions can be activated to collect data.

[0059] In one embodiment, the first collection module (191) sensor manager software development kit can collect IMU data with a sampling rate of 100 Hz. The second collection module (193) privileged health software development kit can collect IMU data with a sampling rate of 25 Hz, and in this case, the sensor manager software development kit can be used to collect IMU data.

[0060] The collected data may include collected life log data. Since the scale of the collected data varies by sensor, the preprocessing unit (130) may be used to normalize the data to the same scale. The preprocessing unit (130) may perform Min-Max normalization to configure the data to be suitable for model input. The aforementioned Min-Max normalization is an example to aid understanding and does not limit the scope of the present invention to a specific algorithm. The normalized data may include data from each sensor of the same scale, and may include acceleration, angular velocity, geomagnetic field, green PPG, red PPG, and infrared PPG data of the same scale.

[0061] In another embodiment, the data collection unit (110) may be a data collection optimization system (110) for collecting data of the biosignal collection module (190).

[0062] FIG. 4 shows a configuration diagram of a multi-sensor-based data collection device and optimization system (110) according to an embodiment of the present invention.

[0063] Referring to FIG. 4, a data optimization system (110) for data collection of a biosignal collection module may include a standby mode switching unit (111), a data acquisition unit (112), an active mode switching unit (113), and a data processing unit (114).

[0064] The standby mode switching unit (111) detects the usage status of the transmitting device (100) and can switch the transmitting device (100) to a standby mode (sleep mode) when not in use. A standby mode is a low-power state entered when the transmitting device (100) is not used by a user, and may refer to a state suitable for long-term data collection tasks by maintaining essential functions such as biosignal collection while minimizing battery consumption through screen deactivation, process limiting, hardware operation optimization, etc.

[0065] In one embodiment, the standby mode switching unit (111) can switch the transmitting device (100) to standby mode by detecting the state using the Android Power Manager (PowerManager API).

[0066] The data acquisition unit (112) can collect various life log data through a multimodal sensor when the transmitting device (100) is in standby mode. The multimodal sensor may include a PPG sensor and can obtain information on at least one green, red, and infrared LED. The multimodal sensor may include an IMU sensor and can collect data on at least one acceleration, angular velocity, and geomagnetic sensor.

[0067] In another embodiment, the multimodal sensor may collect data through a biosignal collection module (190), and may include life log data obtainable from a first collection module (191) and a second collection module (193), but is not limited thereto.

[0068] The active mode switching unit (113) can switch a device in which only the CPU of the transmitting device (100) is activated to active mode (wake mode) when an active cycle occurs or data transmission is required. An active cycle refers to a time interval during which the device switches from standby mode to a state in which only the CPU required for data processing is activated to collect and process sensor data. Through the active cycle, the sensor can switch to active mode. Active mode may include a process of processing user life log data and then returning to standby mode.

[0069] The data processing unit (114) can transmit data collected in standby mode to the receiving device (300) via wireless communication when the transmitting device (100) is in active mode.

[0070] The data processing unit (114) may include a preprocessing unit (130), a compression unit (150), and a transmission unit (170). After data transmission, the data processing unit (114) may cause the standby mode switching unit (111) to switch the transmitting device (100) to standby mode, or release the standby mode when a termination condition is met. In one embodiment, the data processing unit (114) may use an Android Power Manager to activate only the CPU of the transmitting device (100) to perform data preprocessing, data compression, and data transmission.

[0071] FIG. 5 shows a detailed flowchart of a multi-sensor-based data collection device and optimization method according to an embodiment of the present invention.

[0072] Referring to FIG. 5, the multi-sensor-based data collection optimization algorithm has a termination condition in which the algorithm terminates when the application is closed, charged, or the battery is low, and one hour can be set as one cycle. During one cycle, the algorithm may be switched from standby mode to active mode three times at 20-minute intervals, and a total of three data collections may be performed per cycle. It is not limited to this, and the time of one cycle can be arbitrarily specified, and the number of times and the time of switching to active mode within the cycle can also be freely set.

[0073] FIG. 6 shows a flowchart of data collection between a smart watch and a smartphone in a multi-sensor-based data collection device and optimization system (110) according to an embodiment of the present invention.

[0074] Referring to FIG. 6, first, the multi-sensor-based data collection device and optimization system (110) can detect the usage status of the smartwatch and switch it to standby mode. Subsequently, the multi-sensor-based data collection device and optimization system (110) can collect life log data from the smartwatch switched to standby mode. Next, the multi-sensor-based data collection device and optimization system (110) can transmit data by activating only the CPU required for data processing according to the activation cycle. The transmitted data can be checked through an application on a smartphone, which is a receiving device (300).

[0075] FIG. 7 shows a flowchart of an encoder through an AI-based first compression module and a decoder through a second restoration module in an AI-based data compression and restoration system (10) according to an embodiment of the present invention.

[0076] The compression unit (150) may include a first compression module that inputs collected life log data into an encoding model to perform first compression, and a second compression module that converts the first compressed life log data into packets to perform second compression.

[0077] The first compression module can extract key features of collected life log data through an encoding model. The first compression module can compress the extracted key features into a low-dimensional vector.

[0078] The first compression module may use an encoding model with the same structure but different learned weights for each type of collected life log data. According to one embodiment, the encoding model may be an artificial intelligence-based Neural Network (NN) model. The weights of the encoding model may be learned individually according to the characteristics of each sensor, and may be composed of a model having the same structure but different weights, consisting of acceleration, angular velocity, geomagnetic field, green PPG, red PPG, and infrared PPG. Additionally, red PPG and infrared PPG may be collected in on-demand mode and learned as a single weight. The artificial intelligence-based NN model may include, for example, a Recurrent Neural Network (RNN) or a Convolutional Neural Network (CNN) including Long Short-Term Memory (LSTM).

[0079] Referring to FIG. 7, which is an embodiment, the first compression module may be a CNN-based encoder model and can extract features of data by repeatedly performing convolution and pooling operations and compress them into a low-dimensional vector.

[0080] The second compression module can perform secondary compression on life log data compressed into low-dimensional vectors by configuring it into packets through base64 encoding. A packet may consist of a header, data, and parse bits, and in data communication, it refers to a unit of information transmitted in blocks from one device to another.

[0081] The header may include the type and length of the data, sender and receiver information, packet number, etc. Additionally, the receiving end can analyze the header to determine the data processing method, verify the packet order, and facilitate the recovery process.

[0082] The packet contains compressed valid data, and an AI-based NN model includes compressed values ​​of important feature data, and by compressing the data a second time, the amount of data transmitted can be reduced and the transmission time shortened.

[0083] A parity bit is a simple error detection code used to detect errors that may occur during data transmission; it adds bits to ensure that the sum of the transmitted bits is even or odd, allowing the receiving end to verify the integrity of the data.

[0084] The biosignal data collection cycle can be repeated every 1 second. The biosignal cycle may refer to the operation and algorithm from the data collection unit (110) operating in the transmitting device (100) to the preprocessing unit (130) and the compression unit (150).

[0085] The transmission unit (170) can transmit a packet from the transmitting device (100) to the receiving device (300).

[0086] The restoration unit (330) can restore the packet received from the receiving device (300) into life log data by inputting it into a decoding model and going through a plurality of restoration steps. The plurality of restoration steps may include base64 decoding and a decoding model. base64 decoding refers to a first restoration module, and the decoding model may refer to a second restoration module.

[0087] The first restoration module can restore the packet received from the receiving device (300) into a low-dimensional vector of the same form as the input of the second compression module through base64 decoding.

[0088] The second restoration module can restore low-dimensional vectors into life log data through a decoding model. Additionally, the decoding model can form a symmetric structure with the encoding model.

[0089] Referring to FIG. 7, the second restoration module may be a CNN-based decoder model and can compress the features of the data into a low-dimensional vector by repeatedly performing Convolution Transpose (deconvolution) operations. Convolution Transpose can receive the reduced feature map and restore the data to its original size.

[0090] FIG. 8 shows a flowchart of data transmission between a smart watch and a smartphone in an artificial intelligence-based data compression and restoration system (10) according to an embodiment of the present invention.

[0091] Referring to FIG. 8, in the process of compressing and restoring data from a smartwatch and transmitting it to a smartphone, the smartwatch data is collected, undergoes a preprocessing process, compresses the data through an encoder, and transmits it; the compressed data is received by the smartphone, and the compressed data is restored through a decoder on the smartphone, thereby allowing the data to be stored on the smartphone.

[0092] FIG. 9 shows a specific flowchart of data transmission between a smart watch and a smartphone in an artificial intelligence-based data compression and restoration system (10) according to an embodiment of the present invention.

[0093] Referring to FIG. 9, each data can be collected using the Sensor Manager software development kit and the Privilege Health software development kit, which are sensing modules in a wearable device, and each data can be unified through a normalization process to the same scale. The unified data can be compressed through a first compression module consisting of five encoder models for each data, and it can be confirmed that the first compression module compresses the data by 75% compared to the original data. The data that has undergone the first compression is further compressed by 14.9% through a second compression module including base64 encoding, and it can be confirmed that it is compressed by a total of 78.725%. The process from the data collection unit (110) to the compression unit (150) can be repeated every 1 second. The second compressed data is transmitted to a smartphone via Bluetooth communication, and it can be confirmed that about 99.5% of the collected data is obtained from the smartphone through the first and second decomposition modules, which are configured in a symmetrical structure.

[0094] An optimization method for data collection of a biosignal collection module, which is another embodiment of the present invention, may include the steps of switching a transmitting device (100) to a standby mode, collecting data through a biosignal collection module, switching the transmitting device (100) to an active mode, transmitting to a receiving device, and repeating the steps of switching to a standby mode and transmitting to the transmitting device (100).

[0095] The optimization method for data collection of the biosignal collection module may refer to the data collection unit (110) described above.

[0096] The step of switching the transmitting device (100) to standby mode can detect the usage status of the transmitting device (100) and switch the transmitting device (100) to standby mode when it is not in use. The step of switching the transmitting device (100) to standby mode may refer to the aforementioned standby mode switching unit (111).

[0097] The step of collecting data through the biosignal collection module can collect data through the biosignal collection module when the transmitting device (100) is in standby mode. The step of collecting data through the biosignal collection module may refer to the aforementioned data acquisition unit (112).

[0098] The step of switching the transmitting device (100) to an active mode may be performed by activating only the CPU of the device when an active cycle occurs or data transmission is required. The step of switching the transmitting device (100) to an active mode may refer to the aforementioned active mode switching unit (113).

[0099] The step of transmitting to the receiving device may transmit data collected in standby mode to the receiving device via wireless communication when the transmitting device (100) is in active mode. Additionally, the step of switching to standby mode and the step of transmitting to the transmitting device (100) may be repeated until the transmission from the transmitting device (100) to the receiving device is repeated a certain number of times. The repetition of the step of transmitting to the receiving device and the step of switching to standby mode and the step of transmitting to the transmitting device (100) may refer to the aforementioned data processing unit (114).

[0100] Although the present invention has been described in detail above through representative embodiments, those skilled in the art will understand that various modifications can be made to the above-described embodiments within the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the claims set forth below as well as all modifications or variations derived from the claims and equivalent concepts.

[0101]

[0102] Accordingly, the present invention aims to provide a multi-sensor-based data collection device and an optimization method that enables the collection of user life log data for an extended period by more efficiently managing the battery usage of the device.

Claims

1. In an optimization system for data acquisition of a biosignal acquisition module, A sleep mode switching unit that detects the usage status of a transmitting device and switches the transmitting device to sleep mode when not in use; A data acquisition unit that collects data through a biosignal collection module when the above-mentioned transmitting device is in standby mode; An active mode switching unit that switches to active mode (wake mode) by activating only the CPU of the transmitting device when an active cycle occurs or data transmission is required; and A data processing unit that transmits data collected in standby mode to the receiving device via wireless communication when the transmitting device is in active mode; A data collection optimization system characterized by including 2. In Paragraph 1, The above biosignal collection module is, Data collection optimization characterized by including a first collection module that collects at least one of acceleration data, angular velocity data, and geomagnetic field data, and a second collection module that collects at least one of green PPG data, red PPG data, and infrared PPG data.

3. In Paragraph 1, The above-mentioned transmitting device is, A data collection optimization system characterized by performing data preprocessing and data compression operations, which are operations for transmitting collected data, in active mode.

4. In Paragraph 1, The above data processing unit is, After data transmission, the standby mode switching unit switches the transmitting device to standby mode, or A data collection optimization system characterized by releasing standby mode when termination conditions are met.

5. In Paragraph 4, The above data processing unit is, A data collection optimization system characterized by considering a termination condition to be satisfied when at least one of the termination of an application, transition to a charging state, and a low battery state is met.

6. In an optimization method for data collection of a biosignal acquisition module, A step of detecting the usage status of a transmitting device and switching the transmitting device to sleep mode when not in use; A step of collecting data through a biosignal collection module when the above-mentioned transmitting device is in standby mode; A step of switching to wake mode by activating only the CPU of the transmitting device when an active cycle occurs or data transmission is required; and A step of transmitting data collected in standby mode to the receiving device via wireless communication when the transmitting device is in active mode; A data collection optimization method characterized by including 7. In Paragraph 6, A data collection optimization method characterized by repeating the step of switching to a standby mode and the step of transmitting to the transmitting device until transmission from the transmitting device to the receiving device is repeated a certain number of times.

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