Published on Sat Jul 08 2017

Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition

Hailin Chen, Shengping Cui, Sebastian Li

We researched on transfer learning methods and their applications on real world problems. By implementing and modifying various methods, we obtained an insight in the advantages and disadvantages of these methods. We believe that an ensemble-learning approach combining the different methods should yield a better performance.

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Abstract

Through this project, we researched on transfer learning methods and their applications on real world problems. By implementing and modifying various methods in transfer learning for our problem, we obtained an insight in the advantages and disadvantages of these methods, as well as experiences in developing neural network models for knowledge transfer. Due to time constraint, we only applied a representative method for each major approach in transfer learning. As pointed out in the literature review, each method has its own assumptions, strengths and shortcomings. Thus we believe that an ensemble-learning approach combining the different methods should yield a better performance, which can be our future research focus.

Sat Dec 05 2020
Machine Learning
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks
Human activity recognition (HAR) research has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier with known users and then using the same classifier for new users.
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Sun Jul 12 2020
Machine Learning
Transfer Learning for Activity Recognition in Mobile Health
TransFall's design contains a two-tier data transformation, a label estimation layer, and a model generation layer. We validate TransFall analytically and empirically.
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Wed Jan 10 2018
Machine Learning
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person. This paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.
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Sun Jan 03 2021
Machine Learning
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network
Deep neural network is an effective choice to automatically recognize human actions utilizing data from various wearable sensors. These networks automate the process of feature extraction relying completely on data. An efficient deep CNN architecture is proposed that can be individually trained to extract features from different transformed spaces.
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Mon Dec 14 2020
Machine Learning
Invariant Feature Learning for Sensor-based Human Activity Recognition
Wearable sensor-based human activity recognition (HAR) has been a research focus in the field of ubiquitous and mobile computing for years. Many deep models have been applied to HAR problems. However, deep learning methods typically require a large amount of data for models to generalize well.
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Tue Apr 02 2019
Machine Learning
Easy Transfer Learning By Exploiting Intra-domain Structures
Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Existing learning-based methods often involve intensive model selection and hyperparameter tuning to obtain good results.
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