How to Build: Deploying Real-Time Fall Detection Models in Nursing Homes using Horizon TM-6140-22 Medical Touchscreen Computer

Patient falls represent a major safety risk in healthcare and nursing home facilities. Deploying edge-based AI camera systems to detect falls in real-time allows nurses to respond instantly, potentially reducing serious injuries. By processing feeds at the edge rather than sending video streams to the cloud, patient privacy is maintained.

This guide details how to build and deploy a real-time computer vision Fall Detection Model on Teguar's Horizon TM-6140-22 Medical Touchscreen Computer.

Hardware Choice: Horizon TM-6140-22 Medical Touchscreen Computer

Medical facilities need cleanable, responsive computers with reliable hardware to process multiple camera streams.

• 21.5" Touch Display: The Horizon TM-6140-22's large display offers real-time status grids of multiple room camera feeds.
• IP65 Waterproof Front Bezel: Protected against liquid ingress, making the screen easy to spray and wipe clean during sanitation rounds.
• Compute Capability: Supports modern multi-core processors capable of running localized YOLO object detection pipelines at high frame rates.

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Fall Detection Workflow

+---------------------------------------------------------------------------------+
|                  Horizon TM-6140-22 Medical Touchscreen Computer                |
|                                                                                 |
|  +--------------------+      +--------------------+     +--------------------+  |
|  | RTSP IP Camera     | ---> | Frame Extraction   | --> | YOLOv8-Pose Model  |  |
|  | (Nursing Home Ward)|      | (OpenCV decoding)  |     | (Local inference)  |  |
|  +--------------------+      +--------------------+     +---------+----------+  |
|                                                                   |             |
|                                                                   v             |
|                                                         +---------+----------+  |
|                                                         | Fall Risk Logic    |  |
|                                                         | (Heuristics check) |  |
|                                                         +---------+----------+  |
|                                                                   |             |
|                                                                   v             |
|                                                         +---------+----------+  |
|                                                         | Local Alert Trigger |  |
|                                                         | (Visual & Sound)   |  |
|                                                         +--------------------+  |
+---------------------------------------------------------------------------------+

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Implementation Guide

Step 1: Install OpenCV and Ultralytics YOLO

We will run custom skeleton tracking using the optimized YOLOv8-pose model.

pip install opencv-python ultralytics numpy

Step 2: Fall Detection Logic Using Skeleton Keypoints

We track key joint coordinates (shoulders, hips, knees) and calculate joint angles and vertical velocities to detect falls.

import cv2
import numpy as np
from ultralytics import YOLO

# Load the pose-estimation model (pre-trained / custom-trained)
model = YOLO("yolov8n-pose.pt")

def detect_falls_in_stream(rtsp_url):
    cap = cv2.VideoCapture(rtsp_url)
    
    while cap.isOpened():
        success, frame = cap.read()
        if not success:
            break
            
        # Run local inference on the video frame
        results = model(frame, verbose=False)
        
        for r in results:
            if r.keypoints is not None:
                # Iterate through detected human poses
                for keypoints in r.keypoints.data:
                    # Keypoint layout: [nose, left_eye, right_eye, ... left_shoulder, ... left_hip, ...]
                    # We extract hip and shoulder coordinates
                    try:
                        left_shoulder = keypoints[5].cpu().numpy()
                        left_hip = keypoints[11].cpu().numpy()
                        left_ankle = keypoints[15].cpu().numpy()
                        
                        # Fall detection heuristics:
                        # 1. Height-to-Width bounding box ratio
                        # 2. Angle of the torso (shoulders to hips) relative to vertical axis
                        y_diff = abs(left_shoulder[1] - left_hip[1])
                        x_diff = abs(left_shoulder[0] - left_hip[0])
                        
                        # Torso is horizontal when x_diff is larger than y_diff
                        if x_diff > y_diff and left_hip[1] > left_shoulder[1] - 10:
                            # Verify if the ankle is close to the vertical hip level
                            cv2.putText(frame, "WARNING: FALL DETECTED", (50, 50), 
                                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
                    except IndexError:
                        continue
                        
        # Render visual stream on Horizon screen
        cv2.imshow("Nurses' Station Fall Monitoring Dashboard", frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
            
    cap.release()
    cv2.destroyAllWindows()

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Optimizing Inference Speed

• Model Quantization: Export your YOLOv8-pose model to OpenVINO or ONNX format. Running inference on Intel-optimized runtimes significantly increases frames-per-second (FPS) output on CPU edge devices.
• Skip Frames: Process every 3rd or 4th frame to reduce processing load while maintaining responsive detection times.

Conclusion

Running fall detection models locally on the Teguar Horizon TM-6140-22 Medical Touchscreen PC provides nursing home administrators with instant alerts while keeping resident video feeds private.