This repository contains the files for the activity recognition demo. It uses the Xsens MVN motion tracking data as input to trained models, based on HMM, to recognize the current posture/activity defined by a known taxonomy [see Malaise et al, IEEE RA-L 2019].
It requires three main modules.
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Xsens_to_Yarp_streamer: this is the only module that must necessarily run on a Windows PC, precisely the one where the SDK of the Xsens MVN is installed. It retrieves the measurements of the Xsens system and streams the data on YARP ports.
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SensorProcessingModule: this can run on an Ubuntu PC. It is coded in sensor_processing.py. It is used to retrieve the data from the Xsens streamer and process the data with a sliding window: the Xsens is at 240Hz, the window enables to lower the Hz and computes a mean of the signals over the window. It reads the data from a YARP port and outputs the result on another YARP port.
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ActivityRecognitionModule: this can also run on an Ubuntu PC. It is coded in activity_recognition.py. It is the main module for online recognition. It reads the processed data from a YARP port connected to SensorProcessingModule, then outputs the result on two YARP ports: the first is /activity_recognition/probabilities:o, which contains the probabilities of every possible state (i.e., action label); the second is /activity_recognition/state:o is the most probable state, i.e., the action label with the highest probability.
The config files for these modules are YARP context files (to put in <PATH_WHERE_YOU_HAVE_YARP_CONTEXT_FILES>/build/share/yarp/context/online_recognition) and the trained models.
- MVN link suit from Xsens (MVN studio 4.4 used)
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The software was tested on Windows 10 and Ubuntu 16
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python3.6
sudo add-apt-repository ppa:deadsnakes/ppa sudo apt-get update sudo apt-get install python3.6
- numpy: http://www.numpy.org/
sudo apt-get install python3-pip pip3 install numpy
python3.6 -m pip install numpy python3.6 -m pip install --upgrade pip
- hmmlearn : https://github.com/hmmlearn/hmmlearn
python3.6 -m pip install hmmlearn
- yarp: http://www.yarp.it/
it is used to read data from the Xsens system through a yarp server. it is not used for training HMM models, so it is not necessary for visualization or local testing. It is however necessary for online use of the HMM models for action recognition using the Xsens system. You must install it in your Ubuntu machine (where you run the activity recognition modules in python) and in the Windows machine where you have your Xsens software. Follow the yarp instructions to install it.
You must configure your Windows machine where you have your Xsens software installed. In our lab it is "AnDyExpe".
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Setting up yarp namespace :
Folder C:/Users/Researcher/AppData/Roaming/yarp/config/
Create a file containing the IP address of the PC from which the yarp server is launched
For example: _demo_andy.conf :
192.168.137.130 10000 yarp 192.168.137.1 10000 yarpChange the configuration from a terminal: yarp namespace /demo_andy
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Configure the Xsens MVN software to enable streaming :
Open the Xsens MVN software and open in the tab Option > Preferences > Network Streamer. Check the table with IP and ports, and check the IP with your HotSpot IP.
add/enable configuration with HotSpot IP (192.168.137.1)In the list of datagram to stream, check that the following are selected:
Position + Orientation (Quaternion) Linear Segment Kinematics Angular Segment Kinematics Joint Angles Center of Mass -
Configure our Xsens streamer:
Edit the streamer configuration file C:/ProgramData/yarp/xsens.ini :
IP_this_machine 192.168.137.1.
server_port_xsens 9763
On the Windows machine (AnDyExpe):
- Connect the Windows laptop to a network
- Open Network Settings -> Mobile Hotspot, and enable the HotSpot network. This is important because the Ubuntu laptop running the activity recognition modules must be connected to the Windows machine running the Xsens software, so that the demo can be executed independently of the wireless network, and both computers are on the same YARP network.
On the Ubuntu PC with the activity recognition module:
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Connect to the AnDyExpe HotSpot Network
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Start Yarp Server:
yarp server [--write]
On the Windows machine (AnDyExpe):
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verify that you are on the same yarp network (should be, if you configured yarp correctly) by typing:
yarp detect --write -
if you have problems, check that you are in the same yarp namespace:
yarp namespace yarp where
You can run the demo with pre-recorded data from Xsens simply by charging a Xsens sequence. On your Windows machine where you have installed your Xsens MVN software (MVN Analyze 2018): run the Xsens MVN software, open the file with your sequence, then click on "Play". You can also click on "Toggle repeat" so that the recording will loop.
On the Windows machine (AnDyExpe):
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Launch MVN 2018 software
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Open sequence file
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Click "Play"
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Click "Toggle repeat" to loop the recording
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Launch the Xsens streamer:
Desktop/andy/sensors/xsens/yarp/build/Release/xsens.exe
You need to start by preparing the Xsens suit.
- Turn on the router/Access Point of the Xsens suit.
- Wear the Xsens suit
- Install the Battery and BodyPack asking the help of a colleague.
- Turn on the BodyPack Xsens
On the Windows machine (AnDyExpe):
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Launch MVN 2018 software
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Start New session
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Wait for connection to the access point
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Perform calibration of the Xsens suit (with the walking calibration phase, just follow instructions)
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Launch the Xsens streamer:
Desktop/andy/sensors/xsens/yarp/build/Release/xsens.exe
You can now check that the yarp ports are streaming data. The list of available yarp port is:
yarp name list
To read the content of a port do:
yarp read ... <NAME_OF_THE_PORT>
For example:
yarp read ... /xsens/JointAngles
On the Ubuntu machine with the activity recognition module, launch the two scripts for the modules:
python3 sensor_processing.py --from [context_file]
python3 activity_recognition.py --from [context_file]
The context_file is the same for both modules. It is the YARP context folder where you have your configuration files. Example:
python3 sensor_processing.py --from demo_andy_final_general_posture.ini
python3 activity_recognition.py --from demo_andy_final_general_posture.ini
Check that you have all the YARP ports. For the sensor processing module:
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input:
/processing/xsens/"NameSignal":i -
output:
/processing/xsens/"NameSignal":o
For the activity recognition module:
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input:
/activity_recognition/"NameSignal":i -
output:
/activity_recognition/state:o /activity_recognition/probabilities:o
Note that Activity Recognition is automatically connecting the ports of Sensor Processing at startup. So You must launch it imperatively after the other. If all the YARP ports are ok, then you can connect Sensor processing to the Xsens streamer and the demo will run automatically. IMPORTANT: do the yarp connect from the Windows machine only!!
yarp connect /xsens/Signal /processing/xsens/signal:i
You can check that the ports are sending the processed signals. For example:
yarp read ... /processing/xsens/Position/Pelvis_z:o
yarp read ... /activity_recognition/state:o
To visualize the output of the demo, you can connect the YARP port of activity recognition to any GUI.
To visualize the signals of the processing module, do:
python3 plot_signals.py --name_port <NAME_OF_THE_YARP_PORT> --size_window <N_DURATION>
This will open a plot for the signal streamed in the yarp port NAME_OF_THE_PORT, plotting only the last N_DURATION values.
Example:
python3 plot_signals.py --name /processing/xsens/Position/Pelvis_z:o --size_window 3000
To visualize the output of the activity_recognition module, do:
python3 plot_probabilities.py
Without argument, it automatically connects to the ports of activity_recognition (so don't change names) and it shows the probabilities for each state.
There is a first version of a GUI for the detailed posture only. Go to src/qt and launch
python3 reco_gui.py
It automatically connects to the ports of activity_recognition but only works with detailed posture. It shows a subset of possible states.
It outputs 4 main states (walking, standing, crouching, kneeling).
Check that the xsens streamer is streaming (on the Windows machine):
yarp read ... /xsens/COM
Launch processing (on the Ubuntu machine):
python3 sensor_processing.py --from demo_andy_final_general_posture.ini
Connect ports (from the Windows machine):
yarp connect /xsens/LinearSegmentKinematics /processing/xsens/LinearSegmentKinematics:i
yarp connect /sens/AngularSegmentKinematics /processing/xsens/AngularSegmentKInematics:i
<DO NOT CONNECT THE INIT COM PORT>
(Optional) Check that the module is streaming (from the Ubuntu machine):
yarp read ... /processing/xsens/Position/Pelvis_z:o
Launch activity recognition:
python3 activity_recognition.py --from demo_andy_final_general_posture.ini
Check that the activity recognition module is working:
yarp read ... /activity_recognition/state:o
It outputs 5 main states (overhead work, work above shoulder, upright, forward bent, strongly forward bent).
Check that the xsens streamer is streaming (on the Windows machine):
yarp read ... /xsens/COM
Launch processing (on the Ubuntu machine):
python3 sensor_processing.py --from demo_andy_final_details.ini
Connect ports (from the Windows machine):
yarp connect /xsens/LinearSegmentKinematics /processing/xsens/LinearSegmentKinematics:i
yarp connect /sens/AngularSegmentKinematics /processing/xsens/AngularSegmentKInematics:i
<DO NOT CONNECT THE INIT COM PORT>
Check that the module is streaming (from the Ubuntu machine):
yarp read ... /processing/xsens/Position/RightHand_x:o
Launch activity recognition:
python3 activity_recognition.py --from demo_andy_final_details.ini
Check that the activity recognition module is working:
yarp read ... /activity_recognition/state:o