Canon’s AI-powered Fall Detection System
Detect Falls Instantly. Protect Lives Proactively:
Real-time alerts, privacy-first monitoring, and intelligent analytics — without wearables or false alarms.
Proactive Safety for Elderly Care, Hospitals, and High-Risk Facilities
Falls are one of the most common causes of injury among seniors and vulnerable individuals, especially in hospitals, care homes, and high-risk facilities.
Canon’s AI-powered Fall Detection solution combines advanced video and non-video based technology (LiDAR + Sound detection) with behavioural analytics, offering a non-intrusive, accurate, and privacy-conscious way to detect and respond to incidents immediately.
With real-time alert across multiple devices and seamless integration and scalability, it empowers healthcare providers to manage risks better and ensure safer environments for all.
Why Fall Detection Matters
Protect At-Risk Populations
Falls pose serious risks to the elderly, disabled, or post-op patients due to limited mobility and slower reflexes. Timely fall detection or preventing one is crucial.
Prevent Escalating Injuries
Early fall detection enables prompt response, reducing unattended time and lowering the risk of worsening injuries, infections, or fatalities.
Reduce Staffing Pressure
Free up healthcare staff from constant manual monitoring, allowing team to focus on critical care with confidence that incidents won't go unnoticed.
Enable Data-Driven Prevention
Identify fall hotspots to focus preventive efforts on high-risk areas. Recognise pre-fall behaviour patterns to help prevent future incidents.
Uphold Dignity & Privacy
Avoid invasive wearables and ensure privacy with object masking on video records and alerts—all while retaining real-time accuracy and detection.
Support Independent Living
Empower individuals to maintain autonomy and confidence by providing discreet monitoring without restricting their daily activities.
Key Features of Canon Ai-powered Fall Detection Solution
- Detects falls instantly using AI-powered video analytics, ideal for open areas.
- Deep learning algorithms distinguish falls from everyday actions like sitting, bending, or tying shoelaces to minimise false positives.
- Provides accurate detection even in complex settings with varied lighting or occlusions.
- Uses LiDAR depth sensing and sound recognition to detect falls—especially useful in low to none-visibility areas like bedrooms and washrooms.
- Recognises sounds such as screams, coughing, or impact noise, adding another layer of detection without relying on cameras.
- Non-visual detection preserves dignity and privacy, enabling deployment in sensitive environments without wearables.
- Automatic real-time notifications to caregivers and staff via mobile apps.
- Prioritised alerts based on incident type or environmental needs.
- Instant video playback and event review on mobile for faster response.
- Tracks fall patterns, high-risk zones, and user movements pre/post-incident.
- Detects other safety-related behaviours: loitering, distress, aggression.
- Generates actionable insights to prevent future incidents and improve facility operations.
- Privacy masking maintains patient confidentiality.
- Non-visual acoustic detection ensures dignity in sensitive areas like bathrooms.
- Footage of incidents can be shared with visual masking to meet data protection standards.
- Works with leading Video Management Systems (VMS) and central monitoring platforms.
- Scales effortlessly from single-room setups to multi-building campuses.
- Easily integrates into existing infrastructure for centralised control and event archiving.
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