Bayesian and Genetic Optimization for Human Activity Recognition with CNN and LSTM
Shamima Sultana, Taimur Rahman, Halima Khatun, Nayeema Sultana, Md. Rashedul Islam, Nadeem Ahmed · 2024
Recent advancement of wearable sensor technology causes an increase use on human activity recognition (HAR) due to its superior performance and portability. HAR is applied on potential applications such as ambient assisted living, home automation, healthcare, games, monitoring, and inquiring activities. This study focuses on predicting human activities from the PAMAP2 dataset utilizing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. Both models are optimized using Bayesian Optimization (BO) and Genetic Algorithm (GA) techniques precise classification. The research started with a comprehensive Exploratory Data Analysis (EDA) to discover the fundamental construction of activity data. CNN and LSTM models are then implemented and optimized for hyperparameter tuning, enhancing predictive performance. The proposed study is assessed on various metrics, with accuracy, which ranged from 93.8% to 95.35%. Furthermore, a comparative analysis is conducted to identify the most effective model for human activity prediction, highlighting the strengths and weaknesses of each approach.