SensePred: Guiding Video Prediction by Wearable Sensors

Video prediction has been studied in several earlier works. However, there are two inherent limitations in existing solutions, one being the ambiguity concern and the other being the degradation problem in lengthy predictions. To overcome these limitations, this work studies using wearable inertial...

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Bibliographic Details
Published in:IEEE internet of things journal 2023-03, Vol.10 (6), p.1-1
Main Authors: Li, Jia-Yan, Lin, Jaden Chao-Ho, Wu, Kun-Ru, Tseng, Yu-Chee
Format: Article
Language:English
Subjects:
Cameras
Deep Learning
GAN
Inertial guidance
Inertial platforms
Inertial sensing devices
Internet of Things
Interpolation
Legged locomotion
Mobile Computing
Pedestrians
Predictive models
Sensors
Video data
Video Prediction
Wearable Device
Wearable sensors
Wearable technology
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Summary:Video prediction has been studied in several earlier works. However, there are two inherent limitations in existing solutions, one being the ambiguity concern and the other being the degradation problem in lengthy predictions. To overcome these limitations, this work studies using wearable inertial sensors to guide video prediction. We create a dataset called Ped-IMU that records people walking around with the wearable devices to collect the relation between IMU and video data, and propose a SensePred model to conquer these limitations. The model takes full wearable sensor information and partial video information as inputs and predicts the missing video information. Simulation results validate the effectiveness of our model.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3219163