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README.md

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-# Hand Tracking Mouse Controller with Kalman Filter
-
-## Overview
-
-#### This project implements a system that controls the mouse cursor using real-time hand tracking with MediaPipe Hands. Kalman filter and win32api are used for high accuracy, low latency tracking and ease of operation.
-
-## Technical and Scientific Background
-
-### MediaPipe Hands
-
-[MediaPipe Hands](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker) is a framework developed by Google that provides high-fidelity hand and finger tracking by employing machine learning (ML) to infer 21 3D landmarks of a hand from just a single frame. This system uses MediaPipe Hands to detect the hand position and its 21 landmarks (e.g., finger joints) in real-time from camera input.
-
-### Kalman Filter
-
-The Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.
-
-The Kalman filter has two distinct phases: **Predict** and **Update**.
-
-*   **Predict phase:** The Kalman filter produces estimates of the current state variables, along with their uncertainties, based on the system model.
-*   **Update phase:** The next measurement is used to refine these predictions by a weighted average, with more weight being given to estimates with higher certainty.
-
-In this system, the hand position is considered the state, and the landmark positions obtained from MediaPipe Hands are the observed values. A constant velocity model is assumed, and the state is estimated using the following equations:
-
-**State Transition Matrix (F):**
-content_copy
-download
-Use code with caution.
-Markdown
-
-```math
-Q = \begin{bmatrix} q1 & 0 & 0 & 0 \\ 0 & q2 & 0 & 0 \\ 0 & 0 & q3 & 0 \\ 0 & 0 & 0 & q4 \end{bmatrix}
-```
-where `dt` is the time interval between frames.
-
-**Observation Matrix (H):**
-content_copy
-download
-Use code with caution.
-
-H = | 1 0 0 0 |
-| 0 1 0 0 |
-
-**Process Noise Covariance Matrix (Q):**
-content_copy
-download
-Use code with caution.
-
-Q = | q1 0 0 0 |
-| 0 q2 0 0 |
-| 0 0 q3 0 |
-| 0 0 0 q4 |
-
-where `q1`, `q2`, `q3`, and `q4` are parameters representing the magnitude of process noise.
-
-**Measurement Noise Covariance Matrix (R):**
-content_copy
-download
-Use code with caution.
-
-R = | r1 0 |
-| 0 r2 |
-
-where `r1` and `r2` are parameters representing the magnitude of measurement noise.
-
-By applying the Kalman filter, the noisy hand position data obtained from MediaPipe Hands is smoothed, resulting in smoother and more accurate mouse cursor movement.
-
-### Mouse Control
-
-The `win32api` library is used for mouse cursor control. The `win32api.SetCursorPos()` function moves the mouse cursor to the hand position estimated by the Kalman filter.
-
-### Sensitivity Adjustment
-
-The sensitivity of the mouse cursor (the amount of mouse cursor movement relative to hand movement) can be adjusted using the scaling factor `scaling_factor`. Increasing this factor increases the sensitivity. Fine-tuning of the sensitivity can also be achieved by adjusting the Kalman filter parameters `Q` and `R`.
-
-## Operating Environment
-
-*   Windows 10/11
-*   Python 3.7+
-
-## Required Libraries
-
-*   mediapipe
-*   opencv-python
-*   numpy
-*   filterpy
-*   pywin32
-
-## Installation
-
-```bash
-pip install mediapipe opencv-python numpy filterpy pywin32
-```
-Place your hand in front of the camera, and the mouse cursor will move according to your hand movements.
-
-### Configuration
-
-DEBUG (Default: False): Set to True to output debug information.
-
-scaling_factor (Default: 2.5): Adjusts the sensitivity of the mouse cursor.
-
-kf.Q (Default: np.eye(4) * 0.4): Process noise covariance matrix for the Kalman filter.
-
-kf.R (Default: np.eye(2) * 0.8): Measurement noise covariance matrix for the Kalman filter.
-
-These parameters can be configured at the beginning of main.py.
-
-### Troubleshooting
-
-Program does not start: Ensure that all required libraries are installed.
-
-Hand is not detected: Verify that the camera is connected correctly and that the operating environment meets the requirements of MediaPipe Hands.
-
-Mouse cursor does not move: Check if win32api is installed correctly. You may need to run the script with administrator privileges.
-
-Performance is slow: Lowering the values of min_detection_confidence and min_tracking_confidence may improve performance but could reduce accuracy.
-
-### Contribution
-
-Contributions to this project are welcome. Whether it's reporting bugs, suggesting features, or submitting pull requests, your contributions are appreciated.
-
-### License
-
-This project is released under the MIT License. See the LICENSE file for details.
-
-Acknowledgements
-
-This project utilizes the MediaPipe Hands and filterpy libraries. We thank the developers of these excellent libraries.
-
-
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 # Hand Tracking Mouse Controller with Kalman Filter
 
 ## Overview
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 # Hand Tracking Mouse Controller with Kalman Filter
 
 ## 概要