In daily life, many individuals such as elderly people living alone, hostel students, night-shift workers, and individuals with anxiety or medical conditions may experience sudden personal distress (panic, shock, weakness, or medical discomfort) where they are unable to call for help or press an emergency button.
Existing emergency systems depend on:
These approaches fail in situations where the person remains silent, immobile, conscious but frozen, or mentally unable to respond. Currently, there is no automatic system that identifies such silent distress situations using everyday sensors without requiring user action.
The proposed system, Silent Distress Detector (SSD), is a software-based sensor intelligence system that passively monitors abnormal inactivity patterns using sensors already available in smartphones or wearable devices.
The system first learns the user’s normal daily activity pattern.
It continuously monitors:
When a rare and abnormal combination is detected—such as sudden prolonged stillness during normally active periods—the system identifies a possible silent distress event.
If the user does not respond to a soft confirmation alert, the system automatically sends a silent emergency notification to a predefined contact.
The system does not rely on voice, video, or manual input and functions completely in the background.
The novelty of this project lies in its new interpretation of sensor data for emergency detection.
It introduces the concept of “silent distress”, which is not addressed by traditional SOS or fall-detection systems.
The system uses negative sensing, where the absence of expected activity is treated as a critical signal.
Unlike existing solutions, it does not require active user interaction, making it suitable for real-life situations where the user is unable to respond.
The solution is purely software-based, using existing sensors without additional hardware or medical devices.
To the best of our knowledge, no existing system or research work specifically focuses on detecting silent personal distress through inactivity-based sensor fusion.
“This project introduces a novel approach for detecting silent personal distress by analyzing abnormal inactivity patterns using existing device sensors. Unlike traditional emergency systems that rely on active user input or explicit events, the proposed system passively identifies distress situations through software-based negative sensing, which is not addressed in existing solutions.”
Unnoticed personal emergency means a dangerous situation where a person needs help, but no one realizes it.
It happens when a person suddenly feels weak, panics, faints, or is mentally frozen and cannot call or ask for help.
There may be no loud noise, fall, or visible sign of danger.
Because the person stays silent or still, the emergency goes unnoticed by others.
The idea is to detect emergencies when a person cannot ask for help.
The system watches sensor data to notice unusual inactivity.
If the user does not respond, it automatically alerts a trusted contact.