diff --git a/426_YOLOX-Body-Head-Hand/README.md b/426_YOLOX-Body-Head-Hand/README.md
index d94df7dc30..a2fba1ba4b 100644
--- a/426_YOLOX-Body-Head-Hand/README.md
+++ b/426_YOLOX-Body-Head-Hand/README.md
@@ -10,7 +10,7 @@ Lightweight human detection model generated using a high-quality human dataset.
 
   https://github.com/PINTO0309/PINTO_model_zoo/assets/33194443/ab4c4b1b-6e51-416a-948f-809b3d06eafd
 
-The advantage of being able to detect hands with high accuracy is that it makes it possible to detect key points on the fingers as correctly as possible. Since the MediaPipe tflite files are converted to ONNX for processing, the performance of keypoint detection is not very high. It is assumed that information can be acquired quite robustly when combined with a highly accurate keypoint detection model focused on the hand region. It would be realistic to use the distance in the Z direction, which represents depth, in combination with physical information such as ToF, rather than relying on model estimation. To obtain as accurate a three-dimensional value as possible, including depth, sparse positional information on a two-dimensional plane, such as skeletal detection, is likely to break down the algorithm. This has the advantage that unstable depths can be easily corrected by a simple algorithm by capturing each part of the body in planes, as a countermeasure to the phenomenon that when information acquired from a depth camera (ToF or stereo camera parallax measurement) is used at any one point, the values are affected by noise and become unstable due to environmental noise.
+The advantage of being able to detect hands with high accuracy is that it makes it possible to detect key points on the fingers as correctly as possible. The video below is processed by converting the MediaPipe tflite file to ONNX, so the performance of keypoint detection is not very high. It is assumed that information can be acquired quite robustly when combined with a highly accurate keypoint detection model focused on the hand region. It would be realistic to use the distance in the Z direction, which represents depth, in combination with physical information such as ToF, rather than relying on model estimation. To obtain as accurate a three-dimensional value as possible, including depth, sparse positional information on a two-dimensional plane, such as skeletal detection, is likely to break down the algorithm. This has the advantage that unstable depths can be easily corrected by a simple algorithm by capturing each part of the body in planes, as a countermeasure to the phenomenon that when information acquired from a depth camera (ToF or stereo camera parallax measurement) is used at any one point, the values are affected by noise and become unstable due to environmental noise.
 
   https://github.com/PINTO0309/hand_landmark