Hello everyone I'm Xia Chengshuo from Keio University.
Today I would like to give the presentation of my submission to the Late Breaking Work:
From Virtual to Real World Applying Animation to Design
Activity Recognition System", Co-authored with Sugiura, Yuta from Keio University.
Nowadays, the data-driven human activity recognition system relies on the training
dataset which is collected from the significant signal variation regions. These regions limited
the flexibility to alter the sensor positions, and if the positions need to be changed the system
required to recollect the sensory data from the desired position and re-establish the HAR system.
Our system proposed a virtual platform combined with a sensor simulation to design a non-visual
HAR system. It provides accuracy as the feedback of examined position via the cross validation.
And the virtual sensor can be tested to check the corresponding accuracy.
The user can determine whether use these sensor positions or not. The classifier interface trained
by the simulation signal can also be generated forwarding to the real activity recognition.
We establish our system based on the Unity 3D. We input data stream from the Xsens MVN
system as the motion. And simulated the sensor of accelerometer and distance sensor in the Unity.
The HAR system is built based on the virtual dataset.
We used the down-sampling, the data segmentation and data augmentation in the development
We conduct the three different cases to validate. Wearable accelerometer system for daily activity
recognition. Several types of daily activity recognition with the accelerometer worn on
the body. We applied eight different motions in the virtual environment and the user can
have customized in mind of the sensor position and activity recognized. For example the user
is able to propose the sensor position demand and the number and can have the accuracy of the
proposed sensor position based HAR system to determine whether use this position or not.
Second case is the variable distance sensor system for exercise recognition. We can design
a variable distance sensor system which the sensor is attached on the lower limbs of an individual.
We apply the three different exercise motions, heel up and down squatting and hip stretch.
We use the real distance sensor to recognize the real activity where the
classifier trained by the simulation data in the virtual environment.
The last case related to the non-variable system. It is a distance sensing system
for motion recognition in a bathroom. We first map the bathroom into the virtual environment
and install the virtual distance sensor. And conduct
three different bathroom activities like washing hands brushing teeth and washing face.
The distance sensor's data is mapped into a gray scale figure. And finally we can map the
the sensor data and position in a real world to recognize the real activity in a bathroom.
The result shows that our system can good map the virtual dataset
trained classifier into the real world to successfully recognize the real activity.
In this study we proposed a virtual simulation environment to produce the simulated
sensor data. It can draw back the position choice to the end-user side. The user
arbitrarily to place the sensor in a virtual environment. Combined with the virtual sensory,
the process of HAR system design can be improved. And it can output the classifier interface to
recognize the activity in the real world. Our future work related to the virtual
motion generator to generate the more realistic motions in the virtual
and combined with the VR and AR technique to design a game with the virtual sensor.
Thank you very much!