Mental Health Monitoring

Identify individuals who may require immediate assistance, such as those displaying signs of extreme distress. Also used for early identification and monitoring of mental health conditions, such as depression and anxiety, through emotional cues.

Use Case
Sentiment Analysis

In the dynamic field of healthcare, a critical yet often unmet need is the early and accurate identification of individuals displaying signs of mental distress or those at the onset of mental health conditions like depression and anxiety. Traditional methodologies predominantly rely on subjective assessments and patient self-reporting, which can be unreliable and late in capturing the full spectrum of mental health symptoms. This challenge is further compounded by the subtlety of emotional cues and the variability in how mental health symptoms manifest, making it difficult for healthcare professionals to promptly identify and intervene in cases of mental distress.


To address these challenges, we have partnered with a healthcare provider to introduce a groundbreaking solution that harnesses the power of computer vision technology. Our system employs state-of-the-art computer vision algorithms to analyze a range of visual cues, such as micro-expressions, eye movements, and even minute changes in posture that often go unnoticed. These algorithms are trained on diverse datasets to ensure sensitivity and accuracy across different populations. In addition to real-time analysis, our system uses historical data to track changes over time, enabling proactive management of mental health conditions. The interface of our solution is designed with the dual focus of simplicity for end users and comprehensive functionality for healthcare professionals, ensuring ease of access to critical data while maintaining rigorous data privacy and security standards.

  • Accurate Early Detection: Our solution significantly improves the early detection of mental health issues by identifying subtle visual cues, thereby enabling timely intervention.
  • Tailored Patient Monitoring: Continuous and precise monitoring allows for the development of personalized care plans, enhancing the effectiveness of treatment.
  • Reduced Response Time: The immediate identification of distress signals enables quicker response, crucial in emergency situations.
  • Data-Driven Insights: Accumulated data analytics provide healthcare professionals with deeper insights into patient mental health trends, supporting more informed decision-making.
  • Enhanced Patient Engagement: The intuitive design of the patient interface promotes active participation in mental health management, fostering a sense of control and involvement in the healing process.

Techstacks Used

Technologies and Tools
Machine Learning (ML): TensorFlow, PyTorch, scikit-learn Natural Language Processing (NLP): NLTK (Natural Language Toolkit), SpaCy Deep Learning: TensorFlow, PyTorch, Keras Infrastructure: AWS Cloud Services, Azure Security Tools, Google Cloud Platform, NVIDIA GPUs, Kubernetes, Docker Data Storage and Processing: Hadoop, Apache Spark, Apache Kafka User Interface Development: React, Angular Security and Monitoring: Splunk, Palo Alto Networks Biometric and Behavioral Analysis Technology: Wearable Sensors, Custom AI Algorithms

Get Custom Solution, Estimates  &
Recommendations with Confidentiality!

Let’s spark the Idea

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.