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- W4382284369 abstract "Abstract Background: The new improvements in hardware and machines showing shrewd qualities includes various procedures comprising software and hardware architectural improvements. A wide range of wearable-sensors, hardware equipment, machine and deep-learning models are being applied in Human Activity Recognition (HAR) oriented systems and applications lately. Whereas, to foster best models for accurate classification of human actions is of critical significance. Results: For the accomplishment of this objective this study utilizes sensor’s data from two less-expensive sensors, accelerometer, and gyroscope alongside the execution of reconstruction based feature encoding approach i.e. Locality-constrained Linear Coding (LLC) for human activity recognition. This research is intended to perform human action classification where LLC is used in this research for encoding the discriminative data of human body movements (acquired through sensors) while performing a specific action. For encoding the hand crafted features the utilization of LLC is legitimized by exhibiting its prevalence over other different approaches e.g. Sparse-Coding etc. Conclusions: Using LLC encoding approach, final classification of the feature vector is performed using different machine learning approaches i.e. Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Support Vector Machine (SVM). The results for activity classification are evaluated in terms of precision, recall, F1-Score against each activity." @default.
- W4382284369 created "2023-06-28" @default.
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- W4382284369 date "2023-06-27" @default.
- W4382284369 modified "2023-09-25" @default.
- W4382284369 title "Advancing Human Activity Recognition: Locality Constrained Linear Coding and Machine Learning Approaches" @default.
- W4382284369 doi "https://doi.org/10.21203/rs.3.rs-3100158/v1" @default.
- W4382284369 hasPublicationYear "2023" @default.
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