older adults report a fall each year
More than 14 million U.S. adults age 65+ report falling annually.
CDC Older Adult Falls Data ↗CV_01 // DESKTOP PYTHON PROTOTYPE
YOLO fall detection.
A pose-estimation prototype that follows body geometry over time, evaluates fall-like movement, renders a privacy-aware skeleton, and records or forwards an incident. The recorded demo shows the desktop Python system without requesting a visitor's camera.
DEMO_01 // RECORDED SYSTEM RUN
See the detector in motion.
This recording replaces the former technical slideshow. It shows the real desktop workflow while keeping the public portfolio page lightweight and browser-safe.
DEMO FILE OFFLINE
Add Yolloproject.mp4 to the video
folder. The player will connect automatically.
WHAT TO WATCH Pose keypoints, state changes, confidence, incident logging, and the optional alert path.
PRIVACY The webpage plays a recording; it does not activate the viewer's webcam or execute the Python model.
PROCESS_01 // FIVE CLEAR STEPS
From camera frame to alert.
The code is organized as a sequence of small responsibilities. Each stage can be tested, tuned, or replaced without rewriting the entire application.
-
01
CAPTURE
Read one camera frame.
camera.pyopens the selected camera, checks that each frame is valid, and releases the hardware cleanly when the program closes. -
02
DETECT
Locate the person and joints.
pose.pyruns YOLO pose inference and returns body keypoints with confidence values. Unreliable detections are rejected before they reach the fall logic. -
03
COMPARE
Measure change over time.
fall_detector.pycompares body angle, hip height, vertical movement, and recent posture history. A single horizontal frame is not enough to declare a fall. -
04
CONFIRM
Hold the state long enough to matter.
Configurable thresholds confirm that fall-like evidence lasts long enough, then use recovery and cooldown states to reduce duplicate or one-frame alerts.
-
05
RESPOND
Render, log, and optionally notify.
renderer.pydraws the privacy-aware display,alert_logger.pyrecords the event, and an optional adapter can send a notification with secrets kept outside the public code.
DOWNLOAD STATUS
The architecture and research brief is available now. A public Python package should be released only after credentials are removed, dependencies are pinned, and the prototype is tested in a clean environment.
Download briefIMPACT_01 // UNITED STATES, AGE 65+
Why fall awareness matters.
Falls are the leading cause of injury for adults 65 and older. The figures below are from current CDC fall data and prevention resources, reviewed August 2, 2026.
fall injuries are estimated each year
About 37% of older adults who fall report an injury that needs medical treatment or restricts activity for at least a day.
CDC Older Adult Falls Data ↗emergency visits / hospitalizations
Falls lead to roughly 3 million emergency-department visits and about 1 million hospitalizations among older adults each year.
CDC Facts About Falls ↗increase in the fall death rate
The age-adjusted rate increased from 64.7 deaths per 100,000 in 2018 to 78.4 in 2024.
CDC Older Adult Falls Data ↗THE DANGERS
A fall can change health and independence.
Older-adult falls can cause traumatic brain injury, hip fracture, hospitalization, loss of mobility, and death. CDC identifies falls as the most common cause of traumatic brain injury and estimates roughly 300,000 hip-fracture hospitalizations related to falls each year.
Even without a major injury, fear of another fall can lead a person to move less. Reduced activity can weaken strength and balance, which may increase future risk.
PROTOTYPE LIMITS
This project is a research and portfolio demonstration, not a medical device, diagnostic tool, or emergency-response service. Computer vision can miss events or produce false alerts. It should not replace professional care, a validated monitoring system, or calling emergency services when immediate help is needed.