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Human Behavior Recognition in Outdoor Sports Based on the Local Error Model and Convolutional Neural Network.

Xia Hua¹, Lei Han¹, Yang Jiang²

¹Department of Physical Education, China University of Petroleum (East China), Qingdao, Shandong 266580, China.

Computational Intelligence and Neuroscience

|July 8, 2022

Summary

This summary is machine-generated.

This study introduces a local error convolutional neural network for human action recognition on wearable devices. This approach improves memory efficiency by training layers independently, overcoming limitations of traditional deep learning models.

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Area of Science:

Computer Vision
Machine Learning
Embedded Systems

Background:

Human action recognition is crucial for computer vision applications.
Deep convolutional neural networks offer high performance but face memory constraints on wearable devices.
Traditional training methods lead to low memory utilization and backhaul locking issues.

Purpose of the Study:

To design a novel local error convolutional neural network model for human motion recognition.
To address the memory limitations of deep convolutional neural networks on embedded wearable devices.
To improve memory utilization efficiency and overcome backhaul locking problems.

Main Methods:

Developed a local error convolutional neural network model.
Implemented layer-by-layer training using local errors.
Enabled independent parameter training without relying on adjacent layer gradients.

Main Results:

The local error approach allows for early release of memory used by hidden layer parameters.
Successfully avoided the backhaul locking problem inherent in traditional methods.
Significantly improved memory utilization for convolutional neural networks on wearable sensor devices.

Conclusions:

The proposed local error convolutional neural network is effective for human action recognition on resource-constrained devices.
This method enhances the feasibility of deploying advanced deep learning models on wearable technology.
The study offers a viable solution for efficient deep learning on embedded systems.