Description:FIELD OF THE INVENTION:
The present invention relates to the field of mental health diagnosis and, more particularly, to the use of wearable smart gadgets for the detection and diagnosis of depressive illnesses. The invention leverages sensor-based data collection, machine learning algorithms, and IoT-based systems to provide an objective and data-driven method for identifying symptoms of depression.
BACKGROUND OF THE INVENTION:
Depression is one of the most prevalent mental health disorders globally, affecting millions of individuals. Traditional methods for diagnosing depression rely heavily on self-reporting and clinical evaluations, which may not always provide accurate or timely results. With the growing adoption of wearable technology and advancements in machine learning, there has been an increasing interest in developing automated systems for diagnosing mental health disorders, including depression.
Existing research has demonstrated that certain physiological signals—such as heart rate, respiration rate, skin temperature, and oxygen saturation—can be correlated with depressive symptoms. However, much of the prior work has focused on behavioral analysis, subjective self-reporting, and statistical correlation. There is a need for a more integrated, multimodal approach that combines physiological and behavioral data with advanced machine learning techniques for more accurate and real-time diagnosis of depression.
A new approach to the diagnosis and detection of depressive illnesses is the use of wearable smart gadgets for depression detection. Prior work in this area has detected and diagnosed depression using behavioral approaches, machine learning, and correlation analysis. Data gathering procedures, mechanisms for self-reporting, decision-making tools, and parameter association with depressive tendencies are all reviewed in this article. Through the identification of needs and problems, this study seeks to provide a path for future research in this topic. We have discovered that there has to be more focus on mood diversity among individuals, multimodal methods, and overall solutions. There has been talk of developing an Internet of Things (IOT) based system that uses sensors and wearables to identify cases of depression. One possible component of the system is an Arduino microcontroller linked to a wearable device. Using an SVM classifier, the system can identify depressive symptoms based on the user's heart rate, respiration rate, skin temperature, and oxygen saturation levels. After receiving signal data from the wearable, the HC-05 Bluetooth module transmits this information to the smartphone. The user may access the collected data via the system and, if necessary, consult a specialist. Sixty men who were at risk of depression due to unemployment were randomly assigned to one of three groups: (1) an intervention program (22 participants), (2) an intervention program plus sensors (19 participants), or a control group (19). There were observable stress measurements, anxiety, sadness, and good and negative impacts assessed in the subjects. People between the ages of 18 and 65 who were willing to participate in the research who were unemployed and experiencing stress as a result of financial limitations and other obligations. Another crucial need for participation in this research was access to computers and the internet. In order to assess their physiological state and cognitive capacity, researchers used electrocardiogram (ECG) and electroencephalogram (EEG) sensors; to record any kind of movement, they administered an ACT exam. There were three groups of participants: one with sensor access (IP+S), one without (IP), and a control group that completed pre- and post-treatment surveys at the conclusion of the evaluation period. Members of each of these categories were asked for their opinions. In this approach, we put a computerized cognitive behavioral therapy (CCBT) system through its paces as a means of preventing and treating depression. It is useful for gaining knowledge about various methods for dealing with common issues and symptoms. Using electrocardiogram (ECG), activity-based cardiac troponin (ACT) and electroencephalogram (EEG) sensors to help in the former and activity-, emotion-, and mood-related questions to aid in the latter. Thanks to new approaches, more sophisticated algorithms, and the advent of consumer level devices giving greater features and accuracy, research on diagnosing depression using smart wearables is a developing area. With all the recent progress in the sector, it's probable that research and diagnosis will benefit from less human effort required to gather data. In most cases, the results in the clinic have matched the theory. However, this is just applicable to that one study. Commensuration, which sets the standards for comparing outcomes, is necessary. As things stand, machine learning algorithms are king when it comes to regression and classification.
RELATED APPLICATIONS & PATENTS
1. Machine learning techniques to detect potential signs of depression in users based on their network behaviour and tweets, and classifiers were trained and tested using features extracted from users' activities on social media networks and their tweets.
2. CNN model was proposed for analyzing human emotions, which can help us understand a person's emotions better by analyzing their facial expressions and through text messages of depressed individuals.
A stress prediction method using machine learning to detect the development of stress or anxiety problems at an early stage, which can prevent serious consequences as sometimes patients suffering from mental illness are not aware of the severity of their condition or do not keep up with counseling for longer period of time.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention discloses a novel IoT-based wearable system designed to detect and diagnose depression through the continuous monitoring of physiological and behavioral parameters. The system utilizes wearable sensors to collect data on heart rate, respiration rate, skin temperature, and oxygen saturation levels, which are processed using a support vector machine (SVM) classifier to identify potential symptoms of depression.
The system comprises an Arduino microcontroller connected to various sensors embedded in a wearable device, such as a wristband or chest strap. The HC-05 Bluetooth module transmits the collected data to a connected smartphone, where the information is processed and analyzed. The user can access the data through a dedicated mobile application and consult a specialist if necessary.
The invention also includes a study involving unemployed men between the ages of 18 and 65 who were at risk of depression due to financial stress. The study group was divided into three cohorts: an intervention program with sensors, an intervention program without sensors, and a control group. The physiological and cognitive states of the participants were measured using electrocardiogram (ECG), electroencephalogram (EEG), and activity-based cardiac troponin (ACT) sensors. Pre- and post-treatment assessments were conducted to evaluate the system's effectiveness.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
A new method for detecting depression using wearable smart gadgets and machine learning techniques. It highlights the use of behavioral approaches, machine learning, and correlation analysis in diagnosing depressive illnesses.
The present invention provides a wearable smart gadget system for the detection and diagnosis of depression, comprising:
• A set of physiological sensors embedded in a wearable device to monitor heart rate, respiration rate, skin temperature, and oxygen saturation levels.
• An Arduino microcontroller for processing sensor data and performing data transmission via Bluetooth.
• A machine learning algorithm, specifically an SVM classifier, for analyzing physiological data and identifying symptoms of depression.
• A mobile application for real-time data visualization, notifications, and user interaction with healthcare professionals.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
A new method for detecting depression using wearable smart gadgets and machine learning techniques. It highlights the use of behavioral approaches, machine learning, and correlation analysis in diagnosing depressive illnesses.
The present invention relates to the field of mental health diagnosis and, more particularly, to the use of wearable smart gadgets for the detection and diagnosis of depressive illnesses. The invention leverages sensor-based data collection, machine learning algorithms, and IoT-based systems to provide an objective and data-driven method for identifying symptoms of depression.
1. System Components:
o Wearable Device: The wearable gadget includes sensors that monitor various physiological parameters: heart rate, respiration rate, skin temperature, and oxygen saturation. These sensors are embedded in a wristband, chest strap, or similar wearable form factor.
o Arduino Microcontroller: The core processing unit of the system, responsible for receiving and processing sensor data. The Arduino Uno microcontroller interfaces with the sensors and the HC-05 Bluetooth module for data transmission.
o HC-05 Bluetooth Module: This module enables wireless communication between the Arduino and a smartphone or other compatible device. Data is transmitted in real-time to a mobile application.
o Mobile Application: The app acts as a user interface for viewing the collected data, receiving notifications, and consulting a mental health specialist. It also runs the SVM classifier algorithm to identify depressive symptoms based on the physiological data received.
2. Physiological Data Collection and Processing:
o The system uses wearable sensors to continuously gather physiological data. These include:
? Heart Rate: Monitored via a photoplethysmogram (PPG) sensor.
? Respiration Rate: Measured through a respiration sensor or by analyzing heart rate variability.
? Skin Temperature: Captured via an embedded temperature sensor to track changes in body temperature.
? Oxygen Saturation: Monitored using a pulse oximeter.
o The analog data from these sensors is sent to the Arduino Uno, where it is amplified, digitized, and pre-processed. The data is then transmitted via Bluetooth to the connected smartphone for further analysis.
3. Machine Learning-Based Analysis:
o A Support Vector Machine (SVM) classifier is used to analyze the sensor data and identify depressive symptoms. The system compares real-time physiological signals with pre-existing models of depressive tendencies.
o The system can also detect behavioral trends, such as low activity levels or irregular sleep patterns, which may be indicative of depressive episodes.
4. User Interaction:
o The mobile application provides users with real-time feedback on their physiological state. The system can send notifications or alerts if depressive symptoms are detected, prompting users to seek professional advice.
o Data can be logged over time for long-term monitoring, and users can share this data with healthcare professionals for more informed clinical evaluations.
5. Study and Testing:
o A clinical study involving 60 men at risk of depression was conducted to validate the system's effectiveness. Participants were randomly assigned to one of three groups: an intervention program with sensors (IP+S), an intervention program without sensors (IP), and a control group.
o Participants’ stress, anxiety, sadness, and physiological data were tracked over the course of the study using ECG, EEG, and ACT sensors. Pre- and post-treatment assessments demonstrated a correlation between the system's measurements and depressive symptoms, proving the system's diagnostic capabilities.
6. Applications and Advantages:
o The system offers a practical and efficient method for real-time depression detection.
o The integration of IoT-based technologies allows for seamless data collection and analysis, reducing the reliance on subjective self-reporting.
o The wearable design ensures that the system is non-invasive, easy to use, and accessible for long-term monitoring.
o The system's data logging and analysis features provide valuable insights for both users and healthcare professionals, enabling personalized treatment plans and early intervention.
ADVANTAGES OF THE INVENTION
The suggested approach has the advantage of alerting them via alarm and audio messages, which were not available in prior methods, which used just the blink of light with different colors in smart watches.
, C , Claims:1. A wearable system for detecting and diagnosing depression, comprising:
o A set of physiological sensors configured to monitor heart rate, respiration rate, skin temperature, and oxygen saturation levels;
o An Arduino microcontroller interfaced with said sensors to receive, process, and transmit sensor data;
o A Bluetooth communication module configured to wirelessly transmit sensor data from the Arduino microcontroller to a connected smartphone;
o A mobile application configured to receive and process said sensor data and identify depressive symptoms using a machine learning algorithm;
wherein the physiological sensors comprise: a photoplethysmogram (PPG) sensor for heart rate monitoring; a respiration sensor for measuring respiration rate or heart rate variability; a temperature sensor for measuring skin temperature; and a pulse oximeter for measuring oxygen saturation levels;
wherein the machine learning algorithm is a Support Vector Machine (SVM) classifier used to analyze physiological sensor data and identify depressive symptoms based on pre-existing models of depressive tendencies; and
wherein system further comprising a mobile application that provides real-time feedback to the user on their physiological state and sends notifications if depressive symptoms are detected;
wherein the mobile application is configured to: Log sensor data over time for long-term monitoring; Allow the user to share data with healthcare professionals for further evaluation.
2. The system as claimed in claim 1, wherein the system can detect behavioral trends, such as low physical activity or irregular sleep patterns, and correlate them with potential depressive episodes.
3. The system as claimed in claim 1, further comprising a data logging feature to store and analyze sensor data over time for tracking the user’s physiological and behavioral changes related to depressive symptoms; wherein the mobile application is configured to allow the user to consult with a mental health specialist based on the detected symptoms and analyzed data; and wherein the system is implemented in a wearable form factor, including but not limited to a wristband or chest strap, enabling continuous monitoring of the user’s physiological parameters.
4. The system as claimed in claim 1, wherein the sensors, Arduino microcontroller, and Bluetooth communication module are housed in a wearable, compact, and non-invasive device for long-term use by the user.
5. A method for detecting and diagnosing depression using a wearable system, comprising the steps of:
Continuously monitoring physiological parameters including heart rate, respiration rate, skin temperature, and oxygen saturation using wearable sensors;
Transmitting the collected physiological data to an Arduino microcontroller for preliminary processing and amplification;
Wirelessly transmitting the processed sensor data to a connected smartphone via a Bluetooth communication module;
Analyzing the physiological data using a machine learning algorithm, specifically a Support Vector Machine (SVM) classifier, to identify depressive symptoms;
Providing real-time feedback and notifications to the user through a mobile application if depressive symptoms are detected;
wherein the collected physiological data is compared with pre-existing models of depressive tendencies stored in the mobile application to identify patterns indicative of depression.
Wherein system further comprising the step of logging physiological data over time for tracking long-term behavioral and physiological changes associated with depressive symptoms.
6. The method of claim 5, further comprising the step of sending alerts to the user to consult with a mental health professional if the detected depressive symptoms surpass a predetermined threshold.
7. The method of claim 5, wherein the mobile application allows users to share their physiological data with healthcare providers to assist in clinical evaluations and the development of personalized treatment plans.
8. The method of claim 5, further comprising the step of detecting low physical activity or irregular sleep patterns and correlating these behaviors with physiological data to identify potential depressive episodes; and wherein the wearable system is compact and designed for continuous use, allowing for non-invasive monitoring and data collection over extended periods of time.
9. The method of claim 5, further comprising the step of using a Bluetooth-enabled wearable device for transmitting data to the smartphone, ensuring real-time data analysis and user feedback; and wherein the wearable device is capable of transmitting physiological data using IoT-based communication protocols, including but not limited to Wi-Fi, Bluetooth, or Zigbee, for seamless integration with external systems and data management platforms.
10. The system of claim 1, wherein the mobile application allows the user to reprogram or customize system settings, including changing data analysis parameters or updating machine learning models for improved accuracy in diagnosing depression.