Description:The embodiments of the present invention generally relate to the field of mental health monitoring and machine learning. Specifically, it pertains to a system and method for optimizing mental health assessments for IT employees using Long Short-Term Memory (LSTM) networks. The invention focuses on improving the accuracy and effectiveness of mental health predictions by addressing challenges in data quality and feature engineering. This invention aims to provide a personalized, real-time mental health monitoring solution for IT professionals, which can detect stress, burnout, and other mental health issues, thus enabling early interventions.

BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Mental health problems, particularly stress, anxiety, and burnout, are common among IT professionals due to the high-pressure environment, long working hours, and cognitive demands. These issues are often exacerbated by the nature of the work, which can lead to mental fatigue and a lack of work-life balance. However, monitoring and managing mental health in such high-performance environments remains a significant challenge.

Current methods for mental health monitoring rely primarily on self-reports, surveys, or periodic assessments, which can be subjective and lack real-time insights. These traditional approaches may fail to detect early signs of mental distress, especially in environments where employees are reluctant to openly discuss their mental health due to stigma or work culture.

Recent advances in wearable technology and data analytics have led to the development of more objective methods of monitoring health, including physiological data such as heart rate, sleep patterns, and physical activity levels. While these methods have shown promise, they are often not well integrated with advanced machine learning models that can analyze the data in real-time and provide actionable insights.

Machine learning models, particularly Long Short-Term Memory (LSTM) networks, have demonstrated effectiveness in processing sequential data, making them well-suited for time-series analysis of mental health indicators. LSTM models are capable of identifying complex patterns and trends over time, which is critical in understanding mental health dynamics. However, the success of such models is highly dependent on the quality of the data and the features used in the training process.

One of the main challenges in applying LSTM models to mental health monitoring is ensuring the quality of the input data. Data from wearable devices or digital platforms can often be noisy, incomplete, or inconsistent. Proper data preprocessing, including anomaly detection, imputation, and noise reduction, is crucial to ensure the reliability of the analysis.

Another key challenge is feature engineering, which involves selecting the most relevant variables from raw data to improve model performance. In mental health monitoring, feature selection can significantly impact the accuracy of predictions. Identifying which data points (e.g., physiological signals, behavioral patterns, or environmental factors) are most indicative of mental distress is essential for enhancing the model’s ability to detect early signs of stress or burnout.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

The primary objective of the present invention is to provide an optimized system for mental health monitoring in IT employees, which uses Long Short-Term Memory (LSTM) networks to predict and assess mental health conditions, such as stress and burnout, based on physiological, behavioral, and environmental data.

Another objective of the invention is to improve the quality of input data used in the LSTM model by applying advanced data preprocessing techniques to handle issues such as missing data, noise, and inconsistencies. This ensures that the system operates with high accuracy and reliability.

A further objective is to enhance the feature engineering process by selecting the most relevant features for mental health monitoring. By focusing on employee-specific data and temporal patterns, the system aims to optimize feature extraction, thereby improving model performance and prediction accuracy.

The invention also aims to develop a personalized mental health assessment system for IT employees. By tailoring the model to individual employee profiles, the system ensures that predictions are customized, leading to more accurate mental health assessments and interventions.

The system seeks to provide real-time monitoring of employees’ mental health, offering continuous feedback on indicators such as stress levels, fatigue, and burnout. This real-time monitoring enables early detection of mental health issues, allowing for timely interventions that can prevent more serious conditions.

An additional objective is to integrate this system into existing workplace environments seamlessly. By leveraging wearable devices and software applications, the invention aims to provide unobtrusive monitoring that does not interfere with the work of employees while still delivering valuable insights.

The invention aims to facilitate proactive mental health management in IT environments by generating alerts and notifications when abnormal patterns indicative of mental distress are detected. These alerts empower managers and employees to take immediate action to address potential issues before they escalate.

SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

In an aspect, the present invention provides an optimized system and method for mental health monitoring in IT employees using Long Short-Term Memory (LSTM) neural networks. The system collects real-time physiological, behavioral, and environmental data from wearable devices and software platforms, which are processed through advanced data preprocessing techniques to ensure high data quality. The invention incorporates personalized feature engineering, focusing on employee-specific patterns and temporal factors, to improve model accuracy.
By employing LSTM-based models, the system analyzes the processed data to predict mental health conditions such as stress, burnout, and fatigue. The system provides continuous, real-time mental health assessments, offering personalized feedback and alerts when abnormal mental health patterns are detected. This enables IT employees and managers to take proactive steps to manage mental health and improve overall well-being in the workplace.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary system for optimizing mental health monitoring in IT employees, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, 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. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

The present invention provides a comprehensive solution for optimizing mental health monitoring of IT employees using Long Short-Term Memory (LSTM)-based machine learning models. The system is designed to collect, process, and analyze data from various sources, including wearable devices, software applications, and environmental sensors, to continuously monitor mental health indicators. The system utilizes multiple stages of data preprocessing, feature engineering, and real-time analytics to provide accurate predictions of mental health issues such as stress, burnout, and fatigue.

The data collection module gathers real-time physiological data (such as heart rate, skin temperature, and sleep patterns) through wearable devices that are worn by employees during their workday. Additionally, the system tracks behavioral data, such as typing speed, screen time, and activity levels, which are indicators of cognitive load and work-related stress. Environmental factors, such as room temperature, noise levels, and lighting conditions, are also collected to provide a comprehensive view of the employee's work environment. All this data is continuously fed into the system for real-time processing.

Once the data is collected, it passes through a data preprocessing module. This module ensures that the data is cleaned, normalized, and transformed, addressing common issues such as missing data, noisy signals, and outliers. Advanced techniques such as anomaly detection, imputation, and filtering are used to ensure the data’s quality and reliability. The preprocessed data is then prepared for the feature engineering stage, where the system extracts the most relevant features for mental health monitoring. Temporal features, such as changes in activity or sleep patterns over time, are given special emphasis, as they provide more accurate insights into long-term mental health trends. Additionally, employee-specific characteristics are considered, such as their baseline physiological and behavioral patterns, which are used to optimize predictions for individual employees.

The LSTM-based model is the core of the system, processing the temporal data and identifying complex patterns that indicate mental health issues. LSTM models are particularly well-suited for this task because they excel at handling time-series data, such as those generated by wearable devices and behavioral sensors. The LSTM model undergoes rigorous training using the preprocessed data and engineered features. Hyperparameter optimization techniques, such as learning rate adjustments, gradient clipping, and dropout, are used to fine-tune the model for optimal performance. Once trained, the model can make real-time predictions about an employee's mental health status based on incoming data, providing personalized mental health scores.

The system then generates mental health assessments for employees, presenting the results in an easy-to-understand format. These assessments may include stress levels, risk of burnout, and other psychological indicators, with real-time feedback provided to both employees and managers. The system is designed to trigger alerts when abnormal mental health patterns are detected, helping employees and managers take proactive steps to address potential issues. Alerts may include recommendations for rest breaks, lifestyle adjustments, or direct intervention from mental health professionals. This proactive approach helps maintain employee well-being and reduces the risk of mental health crises.

In first embodiment, the system integrates with wearable devices that employees use to track physiological and behavioral data. The wearable devices monitor vital signs such as heart rate variability, skin temperature, and sleep patterns. The wearable is paired with an employee's personal smartphone or computer, transmitting real-time data to the monitoring system. The data is processed through the preprocessing module, where missing or inconsistent values are handled, and any anomalies are flagged for review.

The LSTM model is trained using a large dataset of historical employee data, allowing it to learn patterns specific to each individual. When the system detects any deviation from a typical pattern, such as increased heart rate or irregular sleep cycles, it triggers an alert to the employee or their manager. This system is ideal for environments where employees are often working remotely or in flexible workspaces, as the monitoring is continuous and unobtrusive. The personalized feedback and alerts are sent through a mobile application or web portal, allowing employees to track their mental health in real-time.

In second embodiment, the system integrates with a software application used by employees throughout their workday. The application monitors work-related activities, such as typing speed, screen time, and activity level, and collects environmental data from sensors embedded in the office. For instance, the system can collect data on room temperature, humidity, and lighting levels. The combination of environmental and behavioral data allows the system to assess how the workplace conditions may contribute to employee mental health issues, such as stress or fatigue.

The data is fed into the preprocessing module, where it is normalized and cleaned. Feature engineering focuses on extracting patterns related to work habits, such as extended periods of inactivity, high workload intensity, or repetitive tasks. The LSTM-based model processes the temporal sequences of data, enabling the system to predict when an employee may be at risk of burnout. If the system identifies any concerning patterns, it can trigger personalized suggestions, such as taking a break, adjusting work tasks, or using mindfulness exercises to reduce stress.

Third embodiment combines data from both wearable devices and office environment sensors to provide a comprehensive mental health monitoring solution. Employees wear wearable devices that track physiological signals, while environmental data is collected from sensors installed throughout the workplace. The hybrid system combines these two sources of data, allowing the LSTM model to perform a more accurate assessment of an employee's mental health. For example, if the system detects that an employee is experiencing high stress due to a combination of poor sleep patterns and an overcrowded work environment, it can recommend personalized interventions such as adjusting the work environment or recommending stress-reduction techniques.

The system also incorporates machine learning feedback loops, where the model continuously learns from new data, improving its predictions over time. The real-time analysis provides continuous mental health tracking, which is invaluable for managers looking to create a healthier work environment and provide personalized support to their teams. Alerts and reports can be generated regularly, providing insights into potential issues before they escalate into burnout or other serious mental health conditions. This hybrid approach combines the strengths of wearable and environmental data to create a holistic mental health monitoring system that adapts to the specific needs of individual employees.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.
, Claims:1. A system for optimizing mental health monitoring in IT employees, comprising:
a data collection unit for gathering real-time physiological, behavioral, and environmental data from IT employees;
a data preprocessing module configured to clean, normalize, and transform the collected data, addressing issues such as missing data, inconsistencies, and noise;
a feature engineering module that extracts relevant features from the preprocessed data, including temporal and employee-specific features;
a Long Short-Term Memory (LSTM) neural network trained on the preprocessed data and engineered features to predict mental health indicators, such as stress, anxiety, and burnout;
a real-time monitoring unit that provides personalized mental health assessments and alerts when abnormal mental health patterns are detected.

2. The system of claim 1, wherein the data collection unit further includes wearable devices configured to monitor heart rate, sleep patterns, physical activity, and screen time.

3. The system of claim 1, wherein the data preprocessing module includes an anomaly detection component that identifies and handles outliers and inconsistent data points in the collected data.

4. The system of claim 1, wherein the feature engineering module applies employee-specific customization by selecting features based on individual behavioral and physiological patterns.

5. The system of claim 1, wherein the LSTM neural network utilizes hyperparameter tuning techniques, including learning rate adjustments, gradient clipping, and dropout, to optimize model performance.