espressif/esp-radar

0.1.0

uploaded 8 months ago
This package provides algorithmic functionality for ESP-CSI human movement and presence detection for easy integration into products.

readme

# esp-radar Component

[![Component Registry](https://components.espressif.com/components/espressif/esp-radar/badge.svg)](https://components.espressif.com/components/espressif/esp-radar)

- [User Guide](https://github.com/espressif/esp-csi/tree/master/README.md)

WiFi CSI (Channel State Information) refers to the information obtained from analyzing changes in WiFi signals. This project provides examples of the ESP-CSI motion detection algorithm

### Add component to your project
Please use the component manager command `add-dependency` to add the `esp-radar` to your project's dependency, during the `CMake` step the component will be downloaded automatically.

```
idf.py add-dependency "espressif/esp-radar=*"
```

## Example
Please use the component manager command `create-project-from-example` to create the project from example template.

```
idf.py create-project-from-example "espressif/esp-radar=*:console_test"
```

Then the example will be downloaded in current folder, you can check into it for build and flash.

> You can use this command to download other examples. Or you can download examples from esp-radar repository: 

 - [connect_rainmaker](https://github.com/espressif/esp-radar/tree/master/examples/connect_rainmaker): Adding Wi-Fi CSI Functionality in ESP RainMaker
 - [console_test](https://github.com/espressif/esp-radar/tree/master/examples/console_test): This example provides a test platform for Wi-Fi CSI, which includes functions such as data display, data acquisition and data analysis, which can help you quickly understand Wi-Fi CSI

### Q&A
Q1. I encountered the following problems when using the package manager

```
  HINT: Please check manifest file of the following component(s): main

  ERROR: Because project depends on esp-radar (2.*) which doesn't match any
  versions, version solving failed.
```

A1. For the examples downloaded by using this command, you need to comment out the override_path line in the main/idf_component.yml of each example.

Q2. I encountered the following problems when using the package manager

```
Executing action: create-project-from-example
CMakeLists.txt not found in project directory /home/username
```

A2. This is because an older version packege manager was used, please run `pip install -U idf-component-manager` in ESP-IDF environment to update.

changelog

# v0.1.0
This is the first release version for esp-radar component in Espressif Component Registry, more detailed descriptions about the project.

readme of connect_rainmaker example

                                        
                                        # Adding Wi-Fi CSI Functionality in ESP RainMaker [[中文]](./README_cn.md)

## Build and Flashing Instructions
Follow the ESP RainMaker documentation [Getting Started](https://rainmaker.espressif.com/docs/get-started.html) section to build and flash the firmware.

## How to Use This Example

### Parameter Description
- **someone_status**: false - no one, true - someone
- **someone_timeout**: If someone moves within this time in the room, it will be marked as someone present. Time is in seconds.
- **move_status**: false - no movement, true - movement
- **move_count**: Number of times movement is detected between the last ESP RainMaker report.
- **move_threshold**: Threshold value to determine if there is movement.
- **filter_window**: Size of the buffer queue for Wi-Fi CSI waveform jitter values, used for filtering outliers.
- **filter_count**: If the jitter value of the Wi-Fi CSI waveform exceeds the `move_threshold` for `filter_count` times within the buffer queue, it is marked as movement detected.
- **threshold_calibrate**: Enable threshold auto-calibration.
- **threshold_calibrate_timeout**: Auto-calibration timeout, in seconds.

### App Version
- ESP RainMaker App: [v2.11.1](https://m.apkpure.com/p/com.espressif.rainmaker)+
> Note: `ESP RainMaker` App version before 2.11.1 does not support `time series` function, `move_count` cannot be displayed normally

### App Operation
1. Open the RainMaker App.
2. Click on `+` to add a device.
3. Wait for the device to connect to the cloud.
4. Enable `threshold_calibrate` to perform auto-calibration. Ensure there is no one or no movement in the room during calibration.
5. After calibration, the threshold value for movement detection will be displayed in `move_threshold`, and `move_status` will become true when movement is detected.

### Device
- [x] ESP32-S3-Saola-1
- [x] ESP32-C3-DevKitC

### Device Operation
1. **Factory Reset**: Press and hold the `BOOT` button for more than 5 seconds to reset the development board to factory defaults.

## Device Status
- Human Movement Detection
    - Green LED indicates movement detected in the room.
    - White LED indicates no movement detected in the room.
 
- Human Presence Detection
    > The current algorithm for human presence detection is not ideal. Therefore, the presence of someone is determined by whether there has been any movement within 1 minute. If there is movement, it is considered someone is present; otherwise, it is considered no one is present.
    - LED lights up when there is someone in the room.
    - LED turns off when there is no one in the room.

- Human Movement Detection Threshold
    > - The threshold for human movement detection can be set via the mobile app or obtained through auto-calibration. If not set, the default value will be used.
    > - During calibration, ensure there is no one or no movement in the room. After calibration, the detection sensitivity will be increased. However, if there is movement in the room, it may result in false detection. Therefore, it is recommended to perform calibration when there is no one in the room.
    > - The calibrated threshold will be saved in NVS and will be used after the next reboot.
    - During human movement threshold calibration, the LED will flash yellow.

## Common Issues

### RainMaker Reporting Failure
------
- **Issue**: The device-side logs show the following error:
    ```shell
    E (399431) esp_rmaker_mqtt: Out of MQTT Budget. Dropping publish message.
    ```

- **Cause**: The amount of data being reported by the device exceeds the limit of `ESP RainMaker`.

------
- **Issue**: Continuous movement detection without actual movement or the device does not detect any movement.

- **Solution**:
  1. Incorrect human movement detection threshold leading to false recognition.
     - The default Wi-Fi CSI human movement detection threshold may not meet the actual requirements. Adjust it according to the actual situation or enable auto-calibration through the mobile app.
     - The default outlier filtering window size for Wi-Fi CSI may not meet the actual requirements. Adjust it according to the actual situation through the mobile app.

  2. Unstable network causing inaccurate detection.
     - Check if it works properly after replacing the router.
     - Try placing the router in a more suitable location.

  3. The above methods still cannot solve the problem, modify the LOG level and submit [issue](https://github.com/espressif/esp-csi/issues) on github
     ```c
     esp_log_level_set("esp_radar", ESP_LOG_DEBUG);
     ```
                                    

readme of console_test example

                                        
                                        # esp-csi console_test [[中文]](./README_cn.md)
----------
## 1 Introduction
This example provides a test platform for Wi-Fi CSI, which includes functions such as data display, data acquisition and data analysis, which can help you quickly understand Wi-Fi CSI.
+ **Display**:you can quickly understand the impact of different antennas, human movement and equipment placement on Wi-Fi signals by viewing the real-time data such as Wi-Fi RF noise bottom, CSI, RSSI and noise floor of RF.
+ **Acquisition**:All collected Wi-Fi CSIS will be stored in files. You can also mark the data for different motor behaviors for later neural network and machine learning.
+ **Analysis**:It can realize the detection of human movement and whether there are people in the room, and help you quickly the application scenario of Wi-Fi CSI.
## 2 Equipment preparation
### 2.1 Equipment
![equipment](./docs/_static/2.1_equipment.png)
This example provides two working modes of `esp32-s3 development board` and `router` as Wi-Fi CSI contracting equipment. Using `esp32-s3 development board` as contracting equipment has better adjustment effect on contracting rate, RF size and channel. In both modes, `esp32-s3 development board` is used as the receiving device for Wi-Fi CSI.

### 2.2 Compiler Environment
The esp-idf version of the current project is [ESP-IDF Release v5.0.2](https://github.com/espressif/esp-idf/releases/tag/v5.0.2)
```bash
cd esp-idf
git checkout v5.0.2
git submodule update --init --recursive
./install.sh
. ./export.sh
```
> Note: Since esp-idf v5.0.0 or above supports destination address filtering, the effect will be better, so it is recommended to use v5.0.0 or above

## 3 Starting program
### 3.1 Send Wi-Fi CSI
+ **Use esp32-s3 to send CSI**:Burn project  `csi_send` to esp32-s3 development board
  
    ```bash
    cd esp-csi/examples/get-started/csi_send
    idf.py set-target esp32s3
    idf.py flash -b 921600 -p /dev/ttyUSB0 monitor
    ```
+ **Use router to send CSI**:The router is not connected to other intelligent devices to avoid network congestion affecting the test effect.

### 3.2 Receive Wi-Fi CSI
+ Burn project `console_test` to another esp32-s3 development board
    ```bash
    cd esp-csi/examples/console_test
    idf.py set-target esp32s3
    idf.py flash -b 921600 -p /dev/ttyUSB1
    ```

### 3.3 Start up `esp-csi-tool` ,Open the CSI visualization interface
+ Run `esp_csi_tool.py` in `csi_recv` for data analysis, Please close `idf.py monitor` before running
    ```bash
    cd esp-csi/examples/console_test/tools
    # Install python related dependencies
    pip install -r requirements.txt
    # Graphical display
    python esp_csi_tool.py -p /dev/ttyUSB1
    ```
+ After running successfully, the following CSI data visualization interface is opened. The left side of the interface is the data display interface `Raw data`, and the right side is the data model interface `Raw model`:![csi tool](./docs/_static/3.3_csi_tool.png)

## 4 Interface introduction
The real-time visualization interface consists of two parts: the `Raw data` and the `Radar model`. `Raw data` displays the original CSI sub-carrier data, and `Radar model` uses algorithms to analyze the CSI data. As a result, it can be used for detection of someone/noneone, move/static, by selecting the `Raw data` and `Radar model` buttons in the upper right corner, you can choose to display the two interfaces separately.

### 4.1 Router connection window
The top left is the configuration router connection information window. Use the device to connect to the router and receive the CSI between the router and the device.

![connection window](./docs/_static/4.1_connect_windows.png)

+ **ssid**:router account
+ **password**:router password
+ **auto connect**:If checked, the next time you turn it on, it will automatically connect to the last connected router.
+ **connect / disconnect**:connect/disconnect button
+ **custom**:You can send more control commands such as: restart, version acquisition, or enter custom commands on the device side

### 4.2 CSI data waveform display window
This window can display the waveform of some channel CSI data in real time. If `wave filtering` is checked, the filtered waveform will be displayed.
![csi_waveform window](./docs/_static/4.2_csi_waveform_windows.png)

### 4.3 RSSI waveform display window
This window displays the RSSI waveform information, which can be used to compare with the CSI waveform to observe the changes of RSSI when the personnel in the room are in different states.
![RSSI_waveform window](./docs/_static/4.3_rssi_waveform_windows.png)

### 4.4 log display window
This window displays system logs such as time, memory, etc.
![log window](./docs/_static/4.4_log_windows.png)

### 4.5 Wi-Fi channel data display window
This window displays Wi-Fi channel status information.
![Wi-Fi_data window](./docs/_static/4.5_wi-fi_data_windows.png)

### 4.6 Room status display window
This window is used to calibrate the room status threshold and display room status ( someone/noneone, move/static ).![room_state window](./docs/_static/4.6_room_state_windows.png)

+ **delay**:start calibration delay time, no one is required in the room during calibration, and people can leave the room within the delay time after starting calibration.
+ **duration**:calibration process duration.
+ **add**:if checked, the recalibrated threshold will be accumulated on the basis of the current threshold.
+ **start**:start calibration button.
+ **wander(someone) threshold**:the threshold for judging room presence/absence will be set automatically after calibration, or can be set manually by the user.
+ **jitter(move) threshold**:the threshold for judging the move/static of people will be set automatically after calibration, or it can be set manually by the user.
+ **config**:after the user manually sets the threshold, click the configure button.
+ **display table**:if checked, the room status and people status information table will be displayed on the right side of the waveform box. The specific parameters in the table are as follows.
  
    |status|threshold|value|max|min|mean|std|
    |---|---|---|---|---|---|---|
    |room/people status|Judgment threshold|real-time value|maximum value|minimum value|average value|standard deviation|

### 4.7 People movement data display window
This window displays the specific data of indoor people's movement, the bar graph on the left shows the number of people's movement in real time, and the table on the right records the specific movement time.
![people_movedata window](./docs/_static/4.7_people_movedata_windows.png)

+ **mode**:observation mode, `minute, hour, day` three modes are to record the number of people's movements per minute, hour, and day.
+ **time**:observation time, the default current time, you can manually set the time to start the observation.
+ **auto update**:if checked, the bar graph of the number of people's movements will be automatically updated and displayed in real time.
+ **update**:after clicking, the bar graph of the number of people's movements will be manually updated and displayed.

The meaning of each parameter in the table is as follows:
|room|human|spend_time|start_time|stop_time|
|---|---|---|---|---|
|room status|people status|spend time of movement|start time of movement|stop time of movement|

### 4.8 Action collection window
This window is used to collect CSI data when people perform different actions. The collected data will be stored under the path of `esp-csi/examples/console_test/tools/data`, and the collected data can be used for machine learning or neural network.
![collect window](./docs/_static/4.8_collect_windows.png)

+ **target**:Select the  motor behaviors to collect
+ **delay**:Select the delay time of collection, that is how long to delay to start collection after clicking the `start` button
+ **duration**:The duration of collecting an action
+ **number**:Collection times
+ **clean**:Click to clear all collected data
+ **start**:start collecting button

### 4.9 Model evaluation window
This window is used to evaluate the pros and cons of the adopted thresholds, and to evaluate the accuracy of the room state and people state detection results according to the sent sampling result data.
![model_evaluate window](./docs/_static/4.9_model_evaluate_windows.png)

+ **open folder**:Open the data file of the collecting results.
+ **send**:Send the file, the model will recognize the action after sending, and evaluate the recognition accuracy.

## 5 Operating procedures
Here, taking connecting the router as an example, the operation flow of the visualization system interface is introduced.
### 5.1 connect router
+ Enter the router account and password in turn in the router connection window
+ (option)check `auto connect`
+ Click `connect`

After the connection is successful, you will see the router status information in the "log print window", and the "CSI data waveform display window" will display the CSI data waveform.

### 5.2 Calibration threshold
The purpose of calibration is to let the device know the CSI information when there is no one in the room, and to obtain the threshold of personnel movement. If the current version does not calibrate, only personnel movement detection can be performed. The longer the calibration time, the lower the probability of false touches.
![calibration threshold](./docs/_static/5.2_calibration_threshold.png)

+ Set the delay time of `delay`, here is 10 seconds for example (ie 00:00:10), so that people can leave the room.
+ Set the duration of `duration` calibration, here is 10 seconds as an example.
+ Click `start` and select `yes`, the person leaves the room within 10 seconds (`delay` time), ensure that there is no one in the room within 10 seconds during the calibration period (`duration` calibration duration), and return to the room after the calibration.
+ ( option ) to manually adjust the room status threshold and the person status threshold based on the calibration results.

### 5.3 Observe room status and people status

After the calibration is completed, the room status will be displayed in the room status display window, and it will be judged that there is noneone static, someone moves, and someone static three status.![room_and_people state](./docs/_static/5.3_observe_room_and_people_state_en.png)

+ In `filter outliers`, set how many times the threshold is reached in a row to determine the room/people state, so as to filter outliers.
+ Click `config` to configure.
+ ( option ) In the room status waveform window, click the horizontal line in front of `wander` to hide its waveform, which is convenient for observing other waveforms. Similarly, other waveforms can also be hidden by the following method.
+ ( option ) Check `display table` to view the indoor status and people status information table.

### 5.4 Observer movement times and time
In these observation windows, the number of people's movements per minute will be displayed in a histogram according to our settings. The time information of each movement is recorded in the table on the right.
![observe people move_data](./docs/_static/5.4_observe_people_move_data_en.png)

+ In the `mode` of the people's movement data display window, select the observation mode to view the movement of the people in the room in one minute, one hour or one day. Here, choose `minute` as an example.
+ ( option ) Set the time to start the observation in `time`, the default is the current time start.
+ Check `auto update` to automatically update the test results, if not checked, each time you click `update`, the test results will be updated once.

### 5.5 Collect CSI data for a specific action
![collect csi_data](./docs/_static/5.5_collect_csi_data_en.png)
+ ( option ) Clear previous acquisition data records in `clean` in the motion acquisition window.
+ In `target`, select the action to be collected, here select `move` as an example.
+ In `delay`, set the delayed acquisition time after clicking to start, here is an example of setting a delay of 5 seconds.
+ In `duration`, set the duration of collecting an action in, here we choose 500 ms as an example.
+ In `number`, set the number of times to be collected, here is 1 collection as an example.
+ Click `start`, the system will start collecting data after the delay time, and the personnel will complete the corresponding action within the set time.

After the collection is over, it will stop automatically. We can see the data we collected under the path of `esp-csi/examples/console_test/tools/data`.
![save csi_data](./docs/_static/5.5_save_csi_data_en.png)

### 5.6 Use collected data to identify actions and evaluate accuracy
Collect real-time CSI information, identify the action in real time through the sent data, and display the accuracy rate to evaluate the pros and cons of the set threshold. If the accuracy rate is low, the threshold can be adjusted again.
![select collected_data](./docs/_static/5.6_select_collected_data_en.png)
![model evaluation](./docs/_static/5.6_model_evaluation_en.png)

+ Click `open folder` to select the collected data file
+ Click `send` and select `yes`
+ Click on `csi start`

### 5.7 Window zoom in and out
![zoom in_and_out window](./docs/_static/5.7_zoom_in_and_out_windows_en.png)
+ By selecting `Raw data` and `Radar model` in the upper right corner of the interface, the `Raw data` interface and `Radar model` interface can be displayed separately.
+ Select the critical line between different windows with the mouse, and drag and drop to zoom in/out of each window.

                                    

Links

License: Apache-2.0

To add this component to your project, run:

idf.py add-dependency "espressif/esp-radar^0.1.0"

or download archive

Dependencies

  • espressif/cmake_utilities 0.*
  • ESP-IDF >=4.4.1
  • Examples:

    connect_rainmaker

    more details

    To create a project from this example, run:

    idf.py create-project-from-example "espressif/esp-radar^0.1.0:connect_rainmaker"

    or download archive

    console_test

    more details

    To create a project from this example, run:

    idf.py create-project-from-example "espressif/esp-radar^0.1.0:console_test"

    or download archive

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