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While machine learning models perform well on offline data, assessing their performance in real-world, resource-constrained environments-considering accuracy, prediction time, power consumption, and memory usage-is crucial for practical applications. This research implements a mobile-based Human Activity Recognition solution to classify three postures-sitting, standing, and walking-using smartphone sensors, specifically accelerometer, gyroscope, and magnetometer. Time-domain features extracted from these sensors were used, with Random Forest employed for feature selection. One traditional machine learning model, Logistic Regression, and one deep learning model, Convolutional Neural Network, were trained and deployed via an Android application for real-time evaluation. While the Convolutional Neural Network achieved higher accuracy and better memory efficiency, Logistic Regression demonstrated faster prediction times during real-time use. Both models showed reduced accuracy for standing and walking postures in real-world conditions, emphasizing the challenges of deploying machine learning models in dynamic environments. This study highlights the importance of evaluating machine learning models in real-world settings to ensure reliability and efficiency, particularly in resource-constrained environments.
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PACE: Providing Authentication through Computational Gait Evaluation with Deep learning
This research presents PACE (Providing Authentication through Computational Gait Evaluation), a novel methodology for gait-based authentication leveraging the power of deep learning algorithms. The primary objective of PACE is to enhance the security and efficiency of user authentication mechanisms by capitalizing on the unique gait patterns exhibited by individuals. This study delineates the development and implementation of a deep learning model, which was trained on a set of extracted features. These features, including mean, variance, standard deviation, kurtosis, and skewness, were derived from accelerometer and gyroscope data, serving as descriptors of users' gait patterns for the deep learning model. The model's performance was evaluated based on its ability to classify and authenticate users accurately using these features. For the purpose of this study, twelve participants were enlisted, with sensors affixed to their back hip and right ankle to collect the requisite accelerometer and gyroscope data. The experimental results were highly promising, with the model achieving an exceptional accuracy rate of 99% in authenticating users. These findings underscore the potential of PACE as a viable alternative to conventional machine learning methods for gait authentication. The implications of this research are far-reaching, with potential applications spanning a multitude of scenarios where security is of paramount importance.
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- Award ID(s):
- 2308741
- PAR ID:
- 10484375
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- MobiHoc '23: Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
- ISBN:
- 9781450399265
- Page Range / eLocation ID:
- 442 to 446
- Format(s):
- Medium: X
- Location:
- Washington DC USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3565287.3617618
