A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors

Chikhaoui, Belkacem; Gouineau, Frank; Sotir, Martin

doi:10.1007/978-3-319-96133-0_23

Cited by 14 publications

(3 citation statements)

References 21 publications

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“…Ding et al (2019) perform an empirical study to analyze the performance of transfer learning methods for HAR and find that maximum mean discrepancy method is most suitable for HAR. A CNN-based method to transfer learned knowledge to new users and sensor placements is presented in (Chikhaoui, Gouineau, and Sotir 2018). The authors empirically determine the number of layers to transfer based on the accuracy obtained after transferring.…”

Section: Related Researchmentioning

confidence: 99%

Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks

An¹,

Bhat²,

Gümüşsoy³

et al. 2020

Preprint

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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 offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this approach if their activity patterns are different than those in the training data. At the same time, training from scratch for new users is not feasible for mobile applications due to the high computational cost and training time. To address this issue, we propose a HAR transfer learning framework with two components. First, a representational analysis reveals common features that can transfer across users and user-specific features that need to be customized. Using this insight, we transfer the reusable portion of the offline classifier to new users and fine-tune only the rest. Our experiments with five datasets show up to 43% accuracy improvement and 66% training time reduction when compared to the baseline without using transfer learning. Furthermore, measurements on the Nvidia Jetson Xavier-NX hardware platform reveal that the power and energy consumption decrease by 43% and 68%, respectively, while achieving the same or higher accuracy as training from scratch.

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Section: Related Researchmentioning

confidence: 99%

Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks

An¹,

Bhat²,

Gümüşsoy³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Physiological signals (e.g., electroencephalography) are used to detect certain diseases, such as seizures [44]. Besides, many studies focus on sensorbased human activity recognition and fall detection due to their wide range of application domains including assisted living, sport, human-computer interaction and healthcare [35], [45]. In these studies, machine learning approaches, such as support vector machine (SVM), random forest (RF), are widely used for health status monitoring [34].…”

Section: In-home Health Monitoringmentioning

confidence: 99%

FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring

Chen

Zhou

et al. 2020

Preprint

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In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user's monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user's personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.

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“…LSTM models are a type of recurrent neural networks (RNN) for processing, classifying and making predictions based on times-series data. In contrast to other deep learning models such as Convolutional Neural Networks [19,20], LSTM models are able to extract temporal features from data. This is a very important characteristic, particularly in driving context where driving speed tend to change over time due to different environmental factors.…”

Section: Lstm Modelmentioning

confidence: 99%

A Deep Learning Method for Automatic Visual Attention Detection in Older Drivers

Chikhaoui

Ruer

Vallières

2019

How AI Impacts Urban Living and Public Health

Self Cite

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This paper addresses a new problem of automatic detection of visual attention in older adults based on their driving speed. All state-of-the-art methods try to understand the on-road performance of older adults by means of the Useful Field of View (UFOV) measure. Our method takes advantage of deep learning models such as Long-short Term Memory (LSTM) to automatically extract features from driving speed data for predicting drivers' visual attention. We demonstrate, through extensive experiments on real dataset, that our method is able to predict the driver's visual attention based on driving speed with high accuracy.

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A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors

Cited by 14 publications

References 21 publications

Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks

Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks

FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring

A Deep Learning Method for Automatic Visual Attention Detection in Older Drivers

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