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From Virtual to Real World: Applying Animation to Design the Activity Recognition System (CHI2021 LBW)

CHI2021 LBW

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From Virtual to Real World: Applying
Animation to Design the
Activity Recognition System
Keio University
Chengshuo Xia, Sugiura Yuta
Late Breaking Work
• Data-driven Human Activity Recognition (HAR) system relies on
the training dataset which is collected from the significant signal
variation regions.
• Such regions limited the flexibility to alter the sensor position.
• Change the position requires the system to re-collect the sensory
data from desired position.
2
Background
Specific position
sensory data
collection
HAR Training Application
• A virtual platform combined with sensor simulation to design the
non-visual HAR systems.
• Provide the accuracy as the feedback of examined positions via
cross-validation.
• Virtual sensor can be tested to check the corresponding accuracy.
• Classifier interface trained by simulation signal can be generated
forwarding to the real activity recognition.
3
System
UNITY 3D based platform
• Input data stream:
• Generated from Xsens MVN taken by subjects.
• Sensor Simulation:
• HAR system based on virtual dataset:
• Down-sampled to decrease frame rate influence
• Segmentation
• Augmentation
4
System
• Case-a:
Wearable acceleromters system for daily activity recognition
Testing & Application
Several types of daily activity recognition with an accelerometer
worn on the body.
User can have customized demand of sensor position and
activity recognized.
• Case-b:
Wearable distance sensor system for exercises recognition
Testing & Application
Design a wearable distance sensor system which the sensor is
attached on the lower limbs of an individual.
Ad

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From Virtual to Real World: Applying Animation to Design the Activity Recognition System (CHI2021 LBW)

  • 1. From Virtual to Real World: Applying Animation to Design the Activity Recognition System Keio University Chengshuo Xia, Sugiura Yuta Late Breaking Work
  • 2. • Data-driven Human Activity Recognition (HAR) system relies on the training dataset which is collected from the significant signal variation regions. • Such regions limited the flexibility to alter the sensor position. • Change the position requires the system to re-collect the sensory data from desired position. 2 Background Specific position sensory data collection HAR Training Application
  • 3. • A virtual platform combined with sensor simulation to design the non-visual HAR systems. • Provide the accuracy as the feedback of examined positions via cross-validation. • Virtual sensor can be tested to check the corresponding accuracy. • Classifier interface trained by simulation signal can be generated forwarding to the real activity recognition. 3 System
  • 4. UNITY 3D based platform • Input data stream: • Generated from Xsens MVN taken by subjects. • Sensor Simulation: • HAR system based on virtual dataset: • Down-sampled to decrease frame rate influence • Segmentation • Augmentation 4 System
  • 5. • Case-a: Wearable acceleromters system for daily activity recognition Testing & Application Several types of daily activity recognition with an accelerometer worn on the body. User can have customized demand of sensor position and activity recognized.
  • 6. • Case-b: Wearable distance sensor system for exercises recognition Testing & Application Design a wearable distance sensor system which the sensor is attached on the lower limbs of an individual.
  • 7. • Case-c: Distance sensing system for motion recognition in a bathroom Testing & Application Distance map into grayscale figure
  • 8. • Result: Testing & Application
  • 9. 9 Conclusion • Virtual simulation environment to produce the simulated multi- modal sensor data. • Drop back the position choice to the end-user side. • Arbitrarily to place the sensor in the virtual scenario. • Combine with virtual sensory, improve the process of HAR system design. • Recognize the activity in the real-world • Future work: • Virtual motion generator • VR/AR combined for serious/somatosensory game
  • 10. Thank you very much!