Simple python script that can control KingSmith WalkingPad A1. Others report the similar models, such as R1 PRO work on the same principle.
The belt communicates via Bluetooth LE GATT. Only one device can be connected to the belt at a time, i.e., if original app is connected, the controller won't be able to connect.
- Switch mode: Standby / Manual / Automatic
- Start belt, stop belt
- Change belt speed (0.5 - 6.0), all options work, e.g. 1.2 not originally usable with the native interface (permits only 0.5 step)
- Change preferences of the belt
- Max speed
- Start speed
- start type (intelli)
- Sensitivity in automatic mode
- Display
- Child lock
- Units (miles/km)
- Target (time, distance, calories, steps)
- Ask for current state (speed, time, distance, steps)
- Ask for last stored state in the WalkingPad
For the best understanding start jupyter-notebook and take a look at belt_control.ipynb
# Install jupyter-notebook
pip3 install jupyter
# Start jupyter-notebook in this repository
jupyter-notebook .
# open belt_control.ipynbThe main controller class is Controller in pad.py
Controller enables to control the belt via CLI shell.
Install the library:
pip install -U ph4-walkingpadStart controller:
# Note: use module notation to run the script, no direct script invocation.
python -m ph4_walkingpad.main --stats 750 --json-file ~/walking.jsonOr alternatively, if package was installed with pip:
ph4-walkingpad-ctl --stats 750 --json-file ~/walking.jsonThe command asks for periodic statistics fetching at 750 ms, storing records to ~/walking.json.
Output
---------------------------------------------------------------------------
WalkingPad controller
---------------------------------------------------------------------------
$> help
Documented commands (use 'help -v' for verbose/'help <topic>' for details):
===========================================================================
alias help py quit set speed stop
ask_stats history Q run_pyscript shell start switch_mode
edit macro q run_script shortcuts status tasks
$> status
WalkingPadCurStatus(dist=0.0, time=0, steps=0, speed=0.0, state=5, mode=2, app_speed=0.06666666666666667, button=2, rest=0000)
$> start
$> speed 30
$> speed 15
$> status
WalkingPadCurStatus(dist=0.01, time=16, steps=18, speed=1.8, state=1, mode=1, app_speed=1.5, button=1, rest=0000)
$> status
WalkingPadCurStatus(dist=0.01, time=17, steps=20, speed=1.5, state=1, mode=1, app_speed=1.5, button=1, rest=0000)
$> speed 30
$> s
$> WalkingPadCurStatus(dist=0.98, time=670, steps=1195, speed=6.0, state=1, mode=1, app_speed=6.0, button=1, rest=0000), cal: 38.73, net: 30.89, total: 73.65, total net: 57.91
$> stop
$> start
$> speed 30
$> status
Due to nature of the BluetoothLE callbacks being executed on the main thread we cannot use readline to read from the console, so the shell CLI does not support auto-complete, ctrl-r, up-arrow for the last command, etc. Readline does not have async support at the moment.
This project uses Bleak Bluetooth library. It was reported that OSX 12+ changed Bluetooth scanning logic, so it is not possible to connect to a device without scanning Bluetooth first. Moreover, it blocks for the whole timeout interval.
Thus, when using on OSX 12+:
- do not use
-aparameter - if there are more WalkingPads scanned, use
--filterand specify device address prefix - to modify scanning timeout value use
--scan-timeout
Minimal required version of Bleak is 0.14.1
If the process is still crashing, it may be it does not have permissions to access Bluetooth. To fix it, add your Terminal app (in my case iTerm2.app) to System Preferences -> Security & Privacy -> Bluetooth.
Related resources: hbldh/bleak#635, hbldh/bleak#692
If the -p profile.json argument is passed, profile of the person is loaded from the file, so the controller can count burned calories.
Units are in a metric system.
{
"id": "user1",
"male": true,
"age": 25,
"weight": 80,
"height": 1.80,
"token": "JWT-token",
"did": "ff:ff:ff:ff:ff:ff",
"email": "[email protected]",
"password": "service-login-password",
"password_md5": "or md5hash of password, hexcoded, to avoid plaintext password in config"
}didis optional field, associates your records with pad MAC address when uploading to the serviceemailand (passwordorpassword_md5) are optional. If filled, you can callloginto generate a fresh JWT usable for service auth.
Note that once you use login command, other JWTs become invalid, e.g., on your phone.
If you want to use the service on both devices, login with mobile phone while logging output with adb and capture JWT from logs (works only for Android phones).
The following arguments enable data collection to a statistic file:
--stats 750 --json-file ~/walking.json
In order to guarantee file consistency the format is one JSON record per file, so it is easy to append to a file at any time without need to read and rewrite it with each update (helps to prevent a data loss in cause of a crash).
Example:
{"time": 554, "dist": 79, "steps": 977, "speed": 60, "app_speed": 180, "belt_state": 1, "controller_button": 0, "manual_mode": 1, "raw": "f8a2013c0100022a00004f0003d1b4000000e3fd", "rec_time": 1615644982.5917802, "pid": "ph4r05", "ccal": 23.343, "ccal_net": 18.616, "ccal_sum": 58.267, "ccal_net_sum": 45.644}
{"time": 554, "dist": 79, "steps": 978, "speed": 60, "app_speed": 180, "belt_state": 1, "controller_button": 0, "manual_mode": 1, "raw": "f8a2013c0100022a00004f0003d2b4000000e4fd", "rec_time": 1615644983.345463, "pid": "ph4r05", "ccal": 23.343, "ccal_net": 18.616, "ccal_sum": 58.267, "ccal_net_sum": 45.644}
{"time": 555, "dist": 79, "steps": 980, "speed": 60, "app_speed": 180, "belt_state": 1, "controller_button": 0, "manual_mode": 1, "raw": "f8a2013c0100022b00004f0003d4b4000000e7fd", "rec_time": 1615644984.0991402, "pid": "ph4r05", "ccal": 23.476, "ccal_net": 18.722, "ccal_sum": 58.4, "ccal_net_sum": 45.749}
{"time": 556, "dist": 79, "steps": 981, "speed": 60, "app_speed": 180, "belt_state": 1, "controller_button": 0, "manual_mode": 1, "raw": "f8a2013c0100022c00004f0003d5b4000000e9fd", "rec_time": 1615644984.864169, "pid": "ph4r05", "ccal": 23.608, "ccal_net": 18.828, "ccal_sum": 58.533, "ccal_net_sum": 45.855}
{"time": 557, "dist": 80, "steps": 982, "speed": 60, "app_speed": 180, "belt_state": 1, "controller_button": 0, "manual_mode": 1, "raw": "f8a2013c0100022d0000500003d6b4000000ecfd", "rec_time": 1615644985.606997, "pid": "ph4r05", "ccal": 23.741, "ccal_net": 18.933, "ccal_sum": 58.665, "ccal_net_sum": 45.961}
The benefit of having detailed data is an option to analyze data from the whole run, e.g., how step size varies over the time during one session, collect preferred speeds, etc...
Also, if the original app fails to fetch the final state from the Belt, having continuous data stream is helpful to avoid data loss.
I used the easiest way I found - the original Android application is quite generously logging all Bluetooth requests and responses; and network requests and responses (JWT included).
After few trial/error attempts I managed to reverse binary packet protocol format. See pad.py for protocol internals.
You can query from the belt a status message (app does so each 750 ms, approx). The status contains speed, distance, steps, and very simple CRC code (sum of the payload). Interestingly, calories are not part of the status message and cannot be queried either.
For obtaining logs just plug Android phone via USB, enable development mode on the phone, enable ADB connection and run:
adb logcat(Or use AndroidStudio)
You then can see the app communication with the belt in real-time. When using the app, it logs also requests so you can figure out how commands for e.g., speed change look like.
Should vendor remove the logging from the app and you are unable to find APK in archives with the logging, you can always enable Bluetooth logs in the Phone development settings.
This approach is not that straightforward as from logs as you cannot see belt responses in real-time.
The Bluetooth log can be obtained from the device via adb and opened in Wireshark.
You may need to do own journal with times and commands you issued so you can experiment with the belt (e.g., change speeds), the commands get logged to the Bluetooth log. Then after the experiment, download the Bluetooth log and map your log entries to the packets from the log.
This is substantially difficult compared to the easy way - message logs.
The original application is implemented in Flutter, so direct application reversing is quite painful process. Flutter compiles the source language (TypeScript I guess) to a binary form. It runs on top of a Flutter virtual machine, thus compiled binary has only one primary entry point, a dispatch function. Disassembly does not yield anything sensible, it requires special tools. Also, decompilation tools require the Flutter version to precisely match the version used to compile the application.
For those willing to spend time on this: 1, 2, 3, 4.
- Buy Nordic nRF52832 or nRF52870 USB dongle for BLE sniffing
- Install plugin to Wireshark
- In Wireshark, go to View -> Interface Toolbars -> nRF Sniffer for Bluetooth LE
- Let your WalkingPad advertise, then check it in the toolbar
- Connect with the App to the WalkingPad
- Analyze captured packet sequence
Manual sniffer capture:
./nrf_sniffer_ble.sh --extcap-interface /dev/cu.usbserial-0001 --capture --fifo /tmp/fi I was using the WalkingPad app to reverse engineer packet formats:
- https://play.google.com/store/apps/details?id=com.walkingpad.app&hl=en&gl=US
- https://m.apkpure.com/walkingpad/com.walkingpad.app
Other reported apps may be less obfuscated and easier to analyze (didn't test):
Protocol internals are implemented in pad.py.
- Belt communicates over BT LE GATT messages.
- Controlling app sends a simple binary message to the belt for control and status fetch (request)
- App sends periodically status requests (~ 750 ms), belt responds with a binary message containing: current belt state, manual mode indicator, belt running time in seconds, distance in 10 meters (1km = 100 units), number of steps, last set speed, last button pressed on controller (calories are not reported by the belt)
- Large numbers, such as distance, steps and time are encoded in 3 bytes in the following form:
[x0, x1, x2], where integer form isx = x0*65536 + x1*256 + x0(big endian on 3 bytes) - Packet contains a simple checksum. If the checksum is invalid, belt ignores the command. Let
cmdbe the whole received payload, checksum is computed as:cmd[-2] = sum(cmd[1:-2]) % 256. For more, checkWalkingPadCurStatus - Belt stores the last run status in memory. On query from the app the belt returns it in a different status message form, check
WalkingPadLastStatus. Another request from the app clears the last run status. - It seems that the belt stores the last run status only for a limited time and does not survive power cut, thus this might be the reason why users are reporting apps are not fetching the statistics completely from the belt. Final stats are fetched after the belt is stopped, thus if app is not running when belt stops (e.g., auto stop, or by controller), app sometimes does not make the status fetch in time and the run status is lost.
Example of a status message m:
f8a2010f01000fd10000ab0012ae3c0000003afd
When logged by the application, it is printed out as array if bytes:
[248, 162, 1, 15, 1, 0, 15, 209, 0, 0, 171, 0, 18, 174, 60, 0, 0, 0, 58, 253]
[248, 162]orf8a2is a fixed prefix, probably the message ID.m[2] == 1is a belt statem[3] == 15is a belt speed * 10, here 1.5 kmphm[4] == 1is a flag signalizing manual mode (vs automatic = 0)m[5:8] == [0, 15, 209]is encoded time in seconds, here 4049s = 67 min, 29sm[8:11] == [0, 0, 171]is distance in 10 meters, here 171 = 1.71 kmm[11:14] == [0, 18, 174]is number of steps, here 4782m[14] == 60is the last set app speed, 60 units, 6 kmphm[15]unknownm[16]last controller button pressedm[17] == 58is the checksumm[18] == 253is a fixed suffix
Meaning of some fields are not known (15) or the value space was not explored. m[15] could be for example heart rate
for those models measuring it.
Another reverse engineer of the protocol (under GPL, tldr): https://github.com/DorianRudolph/QWalkingPad/blob/master/Protocol.h
Thanks to all contributors and to the community.
This project was awarded by the Google Open Source Peer Bonus in Feb 2022.
Thanks for considering donation if you find this project useful:
1DBr1tfuqv6xphg5rzNTPxqiUbqbRHrM2E
(No Lightning for now, hopefully soon)
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