Description:The present invention relates to the field of artificial intelligence and mental health informatics. More specifically, it pertains to a deep learning-based mental health monitoring system utilizing Long Short-Term Memory (LSTM) neural networks to predict and evaluate the mental well-being of IT employees. The invention further explores the critical impact of data quality metrics and advanced feature engineering techniques on the accuracy, sensitivity, and reliability of mental health prediction models. This innovation addresses the growing need for real-time, data-driven, and personalized mental health assessment tools in high-performance workplace environments.
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 issues among employees in the Information Technology (IT) sector are on the rise due to increasing workloads, tight deadlines, high-performance expectations, and prolonged exposure to digital environments. These stressors contribute to mental fatigue, anxiety, depression, and burnout, which in turn impact productivity, employee retention, and overall workplace morale. Traditional approaches to monitoring mental health, such as periodic surveys or manual psychological assessments, lack the granularity, timeliness, and adaptability required for real-time mental health management.

Recent advancements in artificial intelligence and wearable sensor technology have enabled the continuous tracking of physiological and behavioral data such as heart rate variability, sleep cycles, typing patterns, and screen time. However, merely collecting such data is insufficient without a robust computational framework to derive meaningful mental health indicators from them. Therefore, it is crucial to use predictive modeling techniques that can interpret longitudinal data and identify subtle patterns related to psychological well-being.

Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), are well-suited for time-series data analysis and have demonstrated promise in applications such as sentiment analysis, human activity recognition, and health forecasting. Yet, the effective implementation of LSTM models in mental health monitoring systems requires not only adequate model design but also a deep understanding of the underlying data quality and the relevance of input features. Poor data quality and suboptimal feature selection can significantly degrade model performance and reduce trust in automated predictions.

Data quality in the context of mental health monitoring encompasses factors such as data completeness, noise levels, continuity, temporal resolution, and labeling accuracy. These factors must be systematically assessed and addressed during preprocessing to ensure that only reliable data is used to train the model. Simultaneously, feature engineering—the process of extracting, transforming, and selecting relevant variables—must account for both domain knowledge and statistical importance to optimize model input.

While some systems have employed machine learning for stress detection, they often overlook the dynamic nature of psychological states and fail to personalize predictions based on individual baseline behaviors. Furthermore, these systems rarely consider the compounded effect of substandard data quality and irrelevant features on deep learning performance. As a result, their outputs may lack the precision and sensitivity required for early mental health intervention.

There remains a gap in the development of a comprehensive, AI-driven mental health monitoring system that integrates continuous data collection, automated quality evaluation, intelligent feature extraction, and high-performance modeling. This invention addresses that gap by providing an LSTM-powered platform that dynamically adapts to individual users, optimizes model performance through data-quality-aware pipelines, and offers actionable insights for enhancing employee well-being.

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 develop an intelligent mental health monitoring system specifically designed for IT employees, utilizing LSTM neural networks to analyze time-series data from multiple behavioral and physiological sources.

Another objective of the invention is to explore and quantify the impact of data quality on predictive model performance, ensuring that the mental health insights generated are both accurate and reliable. By incorporating real-time data quality evaluation mechanisms, the system can filter out noisy, incomplete, or inconsistent data.

A further objective is to implement an advanced feature engineering pipeline that extracts, transforms, and selects features relevant to psychological states such as stress, burnout, anxiety, and fatigue. The invention aims to leverage both domain-specific and statistically significant features to improve model interpretability and responsiveness.

The invention also seeks to personalize mental health assessments by calibrating model parameters to individual user baselines and behavioral norms. This personalization ensures higher sensitivity to deviations that may indicate emerging mental health concerns, thereby allowing for timely interventions.

Another important objective is to develop a feedback mechanism where user input or passive behavior data can be continuously used to fine-tune the model. This iterative learning loop enhances the system’s adaptability and long-term relevance in dynamic workplace settings.

The invention aims to facilitate privacy-preserving deployment options such as edge computing or federated learning, where sensitive employee data remains localized and secure, while still contributing to model optimization across users.

Finally, the invention seeks to deliver an interactive and intuitive dashboard that provides employees, HR personnel, and occupational therapists with insights, trend visualizations, and alert mechanisms to support preventive mental health care in high-pressure IT environments.

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.

The present invention discloses a robust LSTM-powered mental health monitoring system for IT employees that incorporates real-time data acquisition, automated data quality assessment, advanced feature engineering, and deep learning-based predictive modeling. By leveraging a combination of wearable sensor data, behavioral metrics, and self-reported information, the system generates personalized mental health insights, focusing on stress levels, burnout risks, and psychological well-being indicators.

The system dynamically evaluates the quality of incoming data and selects the most relevant engineered features to train and update the LSTM model. This approach significantly enhances the model’s performance and generalization capabilities. Moreover, the invention provides secure, privacy-compliant deployments and real-time dashboards to assist in early detection, mental health trend tracking, and timely intervention for employees in fast-paced technology-driven workplaces.

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 mental health monitoring of 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 discloses a deep learning-powered system that uses Long Short-Term Memory (LSTM) neural networks to monitor and predict the mental health states of IT employees by analyzing time-series behavioral and physiological data. The system architecture comprises four key modules: (1) Data Acquisition and Preprocessing, (2) Data Quality Assessment, (3) Feature Engineering, and (4) LSTM-Based Mental Health Prediction. The interaction among these components enables a continuous, adaptive, and personalized approach to mental health surveillance.

The Data Acquisition and Preprocessing module interfaces with multiple data sources, including wearable sensors (heart rate, sleep cycles, skin temperature), system activity monitors (keyboard/mouse usage, screen time, idle duration), and self-reported inputs (mood ratings, stress surveys). Raw data collected from these sources is normalized, time-aligned, and stored in a secure time-series database. Noise reduction algorithms, missing value imputation, and anonymization techniques are applied during preprocessing to ensure data integrity and privacy compliance.

The Data Quality Assessment module evaluates multiple quality dimensions including completeness, continuity, frequency, and temporal consistency. Each data stream is scored in real-time, and low-quality segments are either corrected or excluded. The system uses threshold-based heuristics and dynamic filters to detect anomalies, outliers, and data gaps. Quality indicators are appended as metadata to help inform the training and inference stages of the predictive model.

The Feature Engineering module transforms raw signals into meaningful indicators of mental health. It includes both statistical features (mean, variance, entropy, skewness) and domain-specific metrics (circadian rhythm disruption, heart rate variability indices, digital fatigue scores). Dimensionality reduction techniques such as Principal Component Analysis (PCA) and feature selection algorithms like mutual information gain and recursive feature elimination (RFE) are applied to optimize the input vector for the LSTM model. The selected features are sequenced based on time intervals to maintain temporal dependencies.

The LSTM-Based Mental Health Prediction module uses stacked LSTM layers followed by dense neural layers to model complex, long-term temporal dependencies within the engineered features. The model outputs probabilistic scores related to mental states such as “normal,” “mild stress,” “high stress,” and “burnout risk.” These outputs are mapped to actionable recommendations and visualized through a real-time dashboard accessible to users and authorized health advisors. Periodic model retraining using online learning or federated learning is supported for continuous improvement.

The entire system operates on a secure cloud or edge platform with modular APIs for data ingestion, preprocessing, quality scoring, and inference. The architecture is scalable, allowing organizations to monitor thousands of employees concurrently while preserving personalized assessments. Privacy is enforced through anonymization, encryption, and role-based access control, ensuring compliance with data protection standards like GDPR and HIPAA.

In one embodiment, the invention is deployed using a combination of wearable devices and desktop monitoring software installed on employee workstations. Wearable sensors continuously collect data such as heart rate, body temperature, and sleep quality, while the software tracks typing speed, application usage, and screen exposure. The two data streams are fused in real-time using a temporal aggregator, which ensures proper alignment of behavioral and physiological metrics.

The system computes behavioral fatigue indices (e.g., frequent switching between applications, reduced keystroke velocity) and physiological stress markers (e.g., elevated heart rate variability). These indicators are fed into the LSTM model after undergoing quality assessment and feature optimization. Based on the model’s predictions, the system generates daily mental health reports for individual users and trend summaries for HR personnel. Recommendations may include wellness breaks, ergonomic suggestions, or confidential counseling options.

In another embodiment, the invention operates as a decentralized real-time burnout risk alert system that leverages federated learning. Instead of transferring raw user data to a central server, the system trains a local LSTM model on each employee’s device using only local data. Model weights are periodically aggregated at the central server using a federated averaging protocol.

This embodiment enhances privacy while still allowing the global model to learn across multiple users. The local models are particularly effective in capturing unique stress patterns, which are personalized for each employee. When the LSTM detects a high probability of burnout risk over a three-day period, the system issues personalized, non-intrusive alerts and resources to mitigate risk. These alerts can also be shared with designated occupational health experts if the employee opts in for external support.

In a third embodiment, the invention is optimized for employees working in rotational or night shifts, who typically exhibit irregular sleep-wake cycles and behavioral patterns. The system includes a time-zone-sensitive feature engineering pipeline that contextualizes work hours, rest periods, and engagement levels based on shift timings. Instead of fixed-hour thresholds, the system dynamically adjusts circadian indicators and fatigue scores to reflect personalized baselines.

LSTM models in this embodiment are trained using stratified sequences grouped by shift type (morning, evening, night). The inclusion of time-sensitive features—such as “time since last rest” or “circadian misalignment score”—significantly improves prediction accuracy for this user group. The dashboard provides managers with anonymized group-level insights, helping them restructure schedules and workloads to minimize psychological strain across global teams.

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 mental health monitoring of IT employees comprising:
o a data acquisition module configured to collect multimodal behavioral and physiological data;
o a data quality assessment module configured to evaluate the completeness, consistency, and accuracy of said collected data;
o a feature engineering module configured to extract and transform features relevant to mental health indicators;
o a deep learning model comprising at least one Long Short-Term Memory (LSTM) layer trained to detect patterns in the processed features; and
o a performance evaluation module configured to assess the impact of data quality and feature engineering on model performance.

2. The system of claim 1, wherein the feature engineering module further comprises a temporal pattern encoder and a normalization unit tailored to individual employee baselines.

3. A method for real-time mental health prediction of IT employees, the method comprising:
o collecting behavioral and physiological data from at least one employee;
o evaluating data quality based on predefined metrics;
o selecting and engineering relevant features from the collected data;
o inputting said features into an LSTM-based model;
o generating a mental health score or stress prediction output;
o providing real-time feedback via a user interface.

4. The method of claim 3, wherein the data is collected from wearable sensors, productivity software, and self-reported inputs.
5. The system of claim 1, wherein the model performance is measured using one or more of the following metrics: root mean squared error, prediction confidence, or change sensitivity to mental health patterns.
6. The system of claim 1, wherein the feature engineering module uses principal component analysis (PCA) or autoencoders for dimensionality reduction.
7. The system of claim 1, wherein the system continuously adapts the feature set and re-trains the LSTM model using reinforcement learning based on incoming data.
8. The system of claim 1, wherein the prediction outputs are securely shared using federated learning protocols to preserve data privacy.
9. The method of claim 3, wherein alerts are triggered when mental health scores cross a predefined threshold, prompting preventive actions.
10. The system of claim 1, further comprising a feedback loop that integrates employee-reported experiences to validate and adjust model outputs.