cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities
arxiv(2023)
摘要
Human activity recognition is a core technology for applications such as
rehabilitation, health monitoring, and human-computer interactions. Wearable
devices, especially IMU sensors, provide rich features of human movements at a
reasonable cost, which can be leveraged in activity recognition. Developing a
robust classifier for activity recognition has always been of interest to
researchers. One major problem is that there is usually a deficit of training
data, which makes developing deep classifiers difficult and sometimes
impossible. In this work, a novel GAN network called TheraGAN was developed to
generate IMU signals associated with rehabilitation activities. The generated
signal comprises data from a 6-channel IMU, i.e., angular velocities and linear
accelerations. Also, introducing simple activities simplified the generation
process for activities of varying lengths. To evaluate the generated signals,
several qualitative and quantitative studies were conducted, including
perceptual similarity analysis, comparing manually extracted features to those
from real data, visual inspection, and an investigation into how the generated
data affects the performance of three deep classifiers trained on the generated
and real data. The results showed that the generated signals closely mimicked
the real signals, and adding generated data resulted in a significant
improvement in the performance of all tested networks. Among the tested
networks, the LSTM classifier demonstrated the most significant improvement,
achieving a 13.27
scarcity. This shows the validity of the generated data as well as TheraGAN as
a tool to build more robust classifiers in case of imbalanced and insufficient
data problems.
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cgan-based
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