| Detection Method |
Uses wearable devices (pendants, wristbands, etc.) with motion sensors (accelerometers, gyroscopes) |
Uses CCTV cameras with AI-powered computer vision and machine learning to analyze video feeds |
| Accuracy |
Prone to false positives/negatives due to reliance on sensor-based data (e.g., quick movements mistaken for falls) |
Higher accuracy, with continuous learning to reduce false alarms and better distinguish real falls from normal activities |
| Privacy |
Privacy concerns are minimal as only the wearer is monitored, but there’s still data collection |
Potential privacy concerns due to continuous surveillance of environments; however, anonymization and local data processing mitigate risks |
| Invasiveness |
Wearables can be intrusive, especially for elderly individuals who may resist wearing them |
Non-intrusive for users, but surveillance can be perceived as invasive depending on the environment |
| Response Time |
Relies on the wearer triggering an alert (e.g., pressing a button), which may be delayed or missed if the wearer is unconscious or immobilized |
Instant, automated detection with real-time alerts sent to caregivers, improving response time significantly |
| Reliability |
Dependent on the user wearing the device consistently; can fail if the device is removed or not worn properly |
Consistently monitors the environment, unaffected by whether the person is wearing a device, providing continuous reliability |
| Coverage |
Limited to the area within which the wearer moves and can be affected by the device’s range and battery life |
Offers comprehensive coverage of multiple rooms or areas without reliance on wearables. Detection occurs throughout the monitored space |
| Cost |
Generally affordable for personal use; however, costs increase for larger facilities or advanced features |
Higher initial setup cost due to the need for smart cameras, AI software, and network infrastructure; costs may decrease over time |
| Installation |
Simple setup—just distribute and wear the devices; minimal technical infrastructure required |
Requires installation of cameras, network configuration, and ongoing system maintenance; more complex than wearable-based systems |
| Scalability |
Limited scalability as it requires wearable devices for each individual, especially in large facilities |
Easily scalable; adding cameras for broader coverage is straightforward, making it ideal for large settings like healthcare facilities |
| Adaptability |
Adaptable to individual needs but may struggle in environments with high movement or multiple individuals |
Highly adaptable to different environments and room layouts; AI algorithms improve over time for better fall detection |
| Detection Trigger |
Device-triggered alert, often relying on the user to press a button or for the system to detect a fall |
Automated, AI-driven detection of falls without the need for user interaction |
| Privacy Safeguards |
Typically, no surveillance or continuous monitoring; the device only tracks wearer’s movements |
Privacy-conscious systems that can anonymize video data, use local processing, or allow for area masking to protect privacy |