AI network providing safety and security to an elderly person in a park

Peace of Mind: How AI Can Help Monitor Loved Ones' Safety

Lena Voss in Tech & Innovation December 2025 • 4 min read.

"Discover how context-aware AI is revolutionizing care for vulnerable individuals, ensuring timely assistance and promoting independence."

Imagine a world where you could ensure the safety of your loved ones without constant supervision. Whether it's a child on their way home from school, an elderly parent with memory issues, or a family member with a disability, the worry can be overwhelming. Fortunately, advancements in artificial intelligence (AI) are making it possible to provide a safety net that respects independence while offering peace of mind.

Traditional methods of care often involve strict routines and constant monitoring, which can feel restrictive and diminish a person's sense of freedom. But what if technology could step in to bridge the gap? A recent study published in the Indian Journal of Science and Technology explores an AI-driven approach to monitoring care-needing individuals using probabilistic models. This innovative system learns daily patterns and detects anomalies, offering a way to ensure safety without intruding on personal autonomy.

This isn't about replacing human caregivers; it's about enhancing their ability to provide support. By using AI to analyze movement patterns and contextual data, caregivers can receive timely alerts when something seems amiss, allowing them to intervene quickly and appropriately. Let's dive into how this technology works and how it's changing the landscape of care.

How Does Context-Aware Abnormality Monitoring Work?

AI network providing safety and security to an elderly person in a park

The core of this technology lies in a sophisticated AI model called a dynamic Bayesian network (DBN). Think of it as a smart system that learns a person's typical routines and habits. It takes into account various data points, including:

The AI model then uses this information to build a picture of what's normal for that individual. By tracking these data points over time, the system learns to recognize patterns and predict future movements. This creates a baseline understanding of the person's typical behavior.

Location: Where the person goes and the routes they take.
Time: When they typically visit certain places.
Date: How their routines vary on different days of the week.
Battery Life: The charge level of their mobile device, which can indicate activity levels or potential issues.

The system doesn't just passively observe; it actively infers potential destinations and routes using a technique called Rao-Blackwellized particle filtering. This allows it to make probabilistic predictions about where the person is likely to go next. The model is constantly updated with new data, allowing it to adapt to changing routines and circumstances. By comparing the predicted path with the actual movements, the system can identify deviations from the norm.

The Future of AI-Assisted Care: Balancing Safety and Independence

AI-powered monitoring systems hold immense potential to enhance the lives of care-needing individuals and their caregivers. By providing a safety net that respects independence, these technologies can promote well-being and offer peace of mind. As AI continues to evolve, we can expect even more sophisticated and personalized solutions that address the unique challenges of caregiving.

About this Article -

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Everything You Need To Know

Can you explain how context-aware abnormality monitoring actually works?

Context-aware abnormality monitoring uses a sophisticated AI model, specifically a dynamic Bayesian network (DBN), to learn and understand an individual's typical routines and habits. It analyzes data points like location, time, date and even battery life of a mobile device to build a picture of what's "normal" for that person. This system actively infers potential destinations and routes using Rao-Blackwellized particle filtering, making probabilistic predictions about where the person is likely to go next. By constantly updating with new data, it adapts to changing routines and identifies deviations from the norm, providing timely alerts when something seems amiss.

What is a dynamic Bayesian network (DBN) and why is it important in this type of system?

A dynamic Bayesian network (DBN) is important because it serves as the core of the context-aware abnormality monitoring system. It's essentially the 'brain' that learns an individual's daily patterns and detects anomalies. By considering various data points such as location, time, and date, the DBN can predict future movements and identify deviations from the norm. Its ability to adapt to changing routines and circumstances makes it a crucial component in ensuring safety without intruding on personal autonomy. Without the DBN's ability to model these relationships, the system would not be able to provide effective, personalized monitoring.

What is Rao-Blackwellized particle filtering and what role does it play?

Rao-Blackwellized particle filtering is a technique used within the AI system to infer potential destinations and routes of an individual. It allows the system to make probabilistic predictions about where the person is likely to go next. This is significant because it enables the system to anticipate movements and compare predicted paths with actual movements, helping to identify deviations from the norm. By actively inferring potential routes, it enhances the system's ability to detect abnormalities and provide timely alerts to caregivers. This contributes to a more proactive and responsive monitoring system. The system also uses other filtering methods like Kalman filters for linear systems and extended Kalman filters for non-linear systems.

What are the broader implications of using AI for monitoring the safety and well-being of individuals?

The implications of using AI-powered monitoring systems are significant for both care-needing individuals and their caregivers. For individuals, it offers a safety net that respects their independence and promotes well-being, allowing them to maintain a sense of freedom while still receiving support. For caregivers, it enhances their ability to provide timely assistance by providing alerts when something seems amiss, enabling them to intervene quickly and appropriately. This approach balances safety and autonomy, improving the quality of life for everyone involved. AI assists but does not replace human care and relationships.

What data points are actually used in the AI monitoring system?

AI-powered monitoring systems use data points such as: location to know where the person is going, time to assess when the person visits certain places, date to understand how the routines vary on different days of the week and battery life of the mobile device to indicate activity levels. By tracking these data points over time, the system learns to recognize patterns and predict future movements. This information allows the system to build a picture of what's normal for that individual, creating a baseline understanding of their typical behavior. This baseline is then used to identify deviations from the norm, enabling timely alerts and support.