Do we need early exit networks in human activity recognition?

Lattanzi, Emanuele; Contoli, Chiara; Freschi, Valerio

doi:10.1016/j.engappai.2023.106035

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…The HAR research field aims to continually enhance recognition performance to its highest level [19], while simultaneously addressing the challenge of attaining high accuracy in recognition using minimal computational resources [23]. Demanding models with increased layers and neurons for deeper and wider models have led to significant improvements in accuracy but have also created challenges related to computational and memory requirements, particularly for mobile and embedded systems with strict constraints, necessitating the development of novel solutions to minimize network size and ensure fast inference without sacrificing accuracy [24].…”

Section: Introductionmentioning

confidence: 99%

Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy

Vazan,

Sharifi,

Farahani

et al. 2024

Preprint

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This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

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Section: Introductionmentioning

confidence: 99%