A CITIZEN OF EU

The invention relates to a lightweight personal handheld home monitoring and managing device, which includes a Sound Sensor network/array of Sound Sensor networks combined with an Artificial Neural Network (ANN) and a built-in system and methods, making this device an intelligent and portable apparatus to address specific health issues. The combined apparatus is used for imaging, guidance, diagnosis, control, and management purposes. This present version of the apparatus will address pulmonary disorders, diseases, or similar ailments.

The Mother Device is an innovative personal health management tool. Leveraging a network of nano or microsensors, it analyzes the composition of your exhaled breath to provide insights into your overall health and lung condition. Data collected is converted from analog to digital form using Fast Fourier Transformation (FFT) algorithms and then processed through advanced software solutions. High-precision measurements are ensured through Capacitive Micromachined Ultrasonic Transducers (cMUT) technology. For user-friendly data interpretation, it employs sophisticated artificial neural network (ANN) algorithms. The device offers a variety of secure data storage options, integrates with other devices or health systems via Bluetooth™ and USB modules, and is designed to be highly portable with an intuitive user interface. The Mother Device is more than a breath analyzer; it’s a comprehensive, portable health management system.

Welcome to a new era of health monitoring. We are proud to present our state-of-the-art “Mother Device,” a milestone in personal health management. Engineered using cutting-edge technology, the Mother Device brings professional-grade healthcare monitoring right into your hands. Let’s delve deeper into this groundbreaking innovation.

The core functionality of our Mother Device lies in its ability to analyze the composition of exhaled air. Utilizing a network of nano or microsensors, it detects and quantifies various biomarkers present in your breath. These biomarkers serve as indicators of your overall health and specifically the condition of your lungs. This makes the device highly beneficial for individuals living with chronic lung diseases, such as asthma or COPD, by facilitating effective at-home monitoring of their lung function.

However, monitoring is only a part of what the Mother Device does. It employs an advanced software solution that uses Fast Fourier Transformation (FFT) algorithms to convert the collected analog data into digital form. This digitized data undergoes further processing, enabling the device to decipher and interpret the intricate chemistry of your breath.

The precision of the Mother Device is noteworthy. It implements an ultrasonic principle, known as Capacitive Micromachined Ultrasonic Transducers (cMUT), for measurements. This method delivers an unparalleled level of precision in analyzing the composition of your breath.

But the true potential of the Mother Device lies in its ability to interpret the collected data. It is equipped with a high-performance microprocessor, such as Intel StrongARM SA-1100 or Intel XScale PXA255, which not only stores and processes the data but also applies sophisticated artificial neural network (ANN) algorithms to it. These algorithms facilitate the interpretation of the data, providing you with easily comprehensible results.

The Mother Device is designed with data security and integrity as a high priority. It incorporates various memory modules for data storage, such as Intel 28F128J3, Intel 28F128K3 for the RAM, Atmel 28LV010 for the ROM, and compact flash cards or disc drives for mass storage. This broad spectrum of memory options ensures secure and accessible storage of your health data.

Furthermore, the Mother Device is built to be versatile. It includes integrated Bluetooth™ and USB modules, enabling it to connect and transfer data seamlessly to other devices or healthcare systems. This feature empowers healthcare providers to monitor your health remotely, leading to prompt intervention when needed.

The Mother Device is equipped with a battery power supply, ensuring it is a genuinely portable solution. It also features an intuitive user interface, including a monitor and keypad, designed for effortless interaction.

In summary, the Mother Device is more than just a breath analyzer. It is a comprehensive health management system that fits in the palm of your hand. It empowers you to take control of your health and lead a healthier life.

The ear device, a compact version of the mother device, connects wirelessly or through a wired connection to data processing machines. It retains all the capabilities of the mother device and can even be waterproof in certain models. The accompanying sound-sensor-network device (SSN device) monitors snoring patterns and alerts the user to change sleeping positions. Over time, the device recognizes snore-frequency-components and accurately identifies sleep disorders. The SSN device can also be used for language training by uploading audio files.

The ear device captures lung sounds, improves signal quality, and reduces background noise for enhanced measurement accuracy. It analyzes unique respiratory sounds for diagnosing asthmatic and allergic illnesses, facilitating monitoring of overall lung health. It transmits various types of information and can send audio warnings for preventive actions.

The device understands spoken commands and can be attached to various body parts for detailed measurements. It detects lung sounds, measures heartbeats, and listens to heart rhythms, providing critical health data. Additionally, it can function as a hearing aid and be implanted into the user’s body.

The ear device is a compact version of the mother device, designed to connect wirelessly or through a wired connection to data processing machines like computers, smartphones, or the mother device. Regardless of the processing machine it’s paired with, the ear device executes identical functions by loading its unique program. It automatically transmits and transfers data to the paired data processing machine and can be recharged using a charging socket or station.

Given the similar internal components shared by the ear device and the mother device, the ear device retains all capabilities of the mother device, making certain models even waterproof up to specific depths. Accompanying the ear device is a variant, the sound- sensor-network device (SSN device), which can be calibrated to monitor the user’s snoring patterns during sleep. If the user needs to change sleeping positions to minimize snoring, a sound alert can be triggered by either the ear device or the mother device. This is also done by the standard ear device. Over time, the device recognizes the user’s snore-frequency- components, which are mapped into a pattern-recognition-behavior-unit, enhancing the device’s ability to detect variations in snoring patterns, thereby accurately identifying sleep disorders like sleep apnea.

The SSN device can be employed for language training by uploading audio files such as MP3s, allowing users to play specialized data files, like language recordings, for practice during travel, work, sleep, or recreational activities.

The ear device can also capture lung sounds, amplify, digitize, and wirelessly or via a wired connection transmit them to the mother device. These sounds further aid in environmental compensation, improving signal quality and reducing background noise for enhanced measurement accuracy, especially in noisy conditions. This is particularly beneficial for clinical use where high accuracy is paramount.

The ear device’s sound analysis relies on the concept that unique respiratory sounds made during breathing can be utilized for diagnosing asthmatic and allergic illnesses. The ear device, composed of various elements including speakers and microphones, is designed to capture respiratory sounds such as snoring, cackling, and wheezing for analysis. This facilitates monitoring of overall lung health.

The ear device is also capable of transmitting various types of information, including music, due to the mother device’s extensive memory capacity and continuous communication with the ear device. Preinstalled or preloaded software can perform multiple functions over specific time periods (e.g., three weeks, six months, or a year), such as specifically keeping tracking of the user’s PEF (Peak Expiratory Flow) blow. If an asthmatic attack is imminent or if the user stops breathing during sleep, the ear device can send an audio warning, helping the user take preventive actions such as medication.

With its built-in microphones, the device can also understand spoken commands, allowing users to communicate without removing it from their ears.
In addition, the ear device can be attached to various body parts for more detailed measurements, as designated by a doctor or the device manual. These could include the heart, wrist, lungs, stomach, neck, throat, or other body areas. Equipped to capture both vibrations and sound signals, the ear device is capable of detecting lung sounds, measuring heartbeats, and listening to heart rhythms, thereby providing critical data for health analysis.

Finally, the ear device can function as a hearing aid thanks to its advanced technology. By adjusting the settings of the ear devices, an “impaired hearing” mode can be activated to capture external sounds and reproduce them at an appropriate volume for the user to hear. Lastly the device can also be implanted into the users body.

Artificial Neural Network (ANN) plays a crucial role in software development by analyzing physiological data and recognizing user behavior patterns. With the ability to learn and train itself, ANN enhances accuracy in diagnosing medical conditions, optimizing measurements, and calibrating devices. User-specific parameters are stored and utilized in the ANN, constantly enriching data through user-device interaction. The device can estimate conditions like COPD/asthma and help manage medication. Collected data is accessible for review and analysis. The setup process allows customization of optimization and training, enabling the exclusion or inclusion of specific parameters. The ultimate aim is to achieve precise user diagnosis and measurement by attaining high precision and accuracy with the assistance of ANN.

Artificial Neural Network is a self-correlating system that analyzes the collected physiological data originating from the behavior pattern of a specific user.

The Artifical Neural Network abbreviated “ANN” or “NN” refers to the intelligence algorithm or unit consist of a software algorithm, implemented hardware unit or a combination of both them and the like and it use to be trained, learned predict, recognize pattern or the like. In terms of “recognize pattern” it is addressing the algorithms ability to learn and train itself to better be able to recognize a specific medical condition, medical circumstance, diseases, medical tread, a healthy human or sportsman parameters, general human behaviour and/or medical parameter to optimize and achieve more accuracy results, diagnosis, measurements and/or calibration or auto calibration of the Sound Sensor network.

The intelligent part of the device is based on the Artificial Neural Network (ANN). The ANN takes as input the measured data and is trained to recognize the specific user’s behavior, a given pattern as mentioned below, which makes the device highly personalized.

User’s unique and personal parameters will be learned and stored through a learning process by the device’s Artificial Neural Network module. The algorithm is designed to remember specific patterns to determine the user’s condition, whether it is relatively healthy (“normal” condition) or relatively sick (“abnormal” condition). In the FM mode, the device, with the help of the ANN, should estimate the user’s condition, and after completing the measurement mode, it should compare the guessed data with the practically measured data. The aim is for the comparison to yield an error percentage of less than 2%, enabling the device to predict the user’s condition, such as COPD/asthma behavior, each time it enters the FM mode. This will assist the user in preparing for peaks and quiet periods of illness, as well as in managing their medication. Another purpose is to learn and recognize the user’s data patterns to improve measurement accuracy. While existing devices are typically 90% accurate, the device with the assistance of the ANN should achieve around 98% accuracy. This will contribute to a more precise user diagnosis and higher precision in administering medication doses.

After the end of the learning period, the device should be able to know more about the specific user’s pattern behavior, more accurate data, etc., as mentioned before. Thus, the device should be capable of estimating, calculating, recognizing, calibrating, and correlating exactly the value of different measurable parameters for the user’s condition. It should also be able to correlate the measurement values through the analysis of lung sound and a few other parameters, as well as calculate and estimate other desirable measurement values.

A mass storage unit inside the device will store the learned information thereafter to treat it with the ANN. The Artificial Neural Network (ANN) resources comprise the information stored in the device, which is constantly enriched with more accurate data about the user through measurement results and communication between the user and the device. As a result, the device no longer relies on reference or standard values, although these values remain in its storage unit. The user always has access to this information, but they are prohibited from modifying, deleting, or overwriting the data, as they form an integral part of the ANN resources.

Although ANN has an adaptive character, the data collected will be saved in the device and could be accessed later in order to review the user’s condition. After a given period of time, when the device will learn more about the user, and a set of the user’s outgoing data will be collected, compiled, and stored as input data for the ANN part, each of this input data will get a set of date and time stamps.

The utilization of Artificial Neural Network (ANN) in Software Development is employed when powering on the Device, as it automatically initiates the self-test mode while performing essential processes such as sensor network testing, calibration, and data verification.

The device setup process allows doctors/specialists to customize the optimization and training of the Artificial Neural Network (ANN). After the initial setup or a master reset activation, specific input data/parameters can be chosen to be excluded from the optimization process, based on factors such as local regulations. Conversely, physicians or specialists have the option to select specific parameters to be included in the optimization process for a defined period, such as 14 days or a month, to enhance accuracy or calibration of the device. At the conclusion of this period, the device’s optimization and training progress will be determined based on these choices and configurations.

The Sound Sensor Network (SSN) is a key feature of our main health monitoring device, providing robust collection, communication, and analysis of respiratory sounds. Its primary role includes capturing lung sounds, alerting users during measurement procedures, and utilizing AI-driven analysis to manage a variety of health conditions. With the SSN, we offer a comprehensive solution to advanced health monitoring.

The Sound Sensor Network (SSN) is an innovative component of our main health monitoring device. Primarily, the SSN is responsible for capturing respiratory sounds, which are then converted into valuable health data.

The SSN is located within the ear device, where it collects lung sounds and other respiratory noises. This is an essential function as it improves the Signal to Noise Ratio in measurements, providing precision especially crucial in clinical environments.

The SSN also communicates with the user during the measurement process. The integrated Sound Reproducer within the SSN serves to inform the user in case of an error during the measurement procedure through an embedded miniature microphone.

Further, the SSN is a vital part of the data analysis process. It works to store and analyze the collected respiratory sound information using advanced Artificial Intelligence mechanisms, allowing for efficient management of various health problems or diseases.

To summarize, the SSN, with its capacity to collect, communicate, and analyze health data, is a comprehensive solution designed for advanced health monitoring.