Human activity recognition(HAR)is a method to predict human activities from sensor signals using machine learning(ML)techniques.HAR systems have several applications in various domains,including medicine,surveillance,...Human activity recognition(HAR)is a method to predict human activities from sensor signals using machine learning(ML)techniques.HAR systems have several applications in various domains,including medicine,surveillance,behavioral monitoring,and posture analysis.Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately.Several research studies on HAR have utilizedMel frequency cepstral coefficients(MFCCs)because of their effectiveness in capturing the periodic pattern of sensor signals.However,existing MFCC-based approaches often fail to capture sufficient temporal variability,which limits their ability to distinguish between complex or imbalanced activity classes robustly.To address this gap,this study proposes a feature fusion strategy that merges time-based and MFCC features(MFCCT)to enhance activity representation.The merged features were fed to a convolutional neural network(CNN)integrated with long shortterm memory(LSTM)—DeepConvLSTM to construct the HAR model.The MFCCT features with DeepConvLSTM achieved better performance as compared to MFCCs and time-based features on PAMAP2,UCI-HAR,and WISDM by obtaining an accuracy of 97%,98%,and 97%,respectively.In addition,DeepConvLSTM outperformed the deep learning(DL)algorithms that have recently been employed in HAR.These results confirm that the proposed hybrid features are not only practical but also generalizable,making them applicable across diverse HAR datasets for accurate activity classification.展开更多
基金supported by Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia through the Researchers Supporting Project PNURSP2025R333.
文摘Human activity recognition(HAR)is a method to predict human activities from sensor signals using machine learning(ML)techniques.HAR systems have several applications in various domains,including medicine,surveillance,behavioral monitoring,and posture analysis.Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately.Several research studies on HAR have utilizedMel frequency cepstral coefficients(MFCCs)because of their effectiveness in capturing the periodic pattern of sensor signals.However,existing MFCC-based approaches often fail to capture sufficient temporal variability,which limits their ability to distinguish between complex or imbalanced activity classes robustly.To address this gap,this study proposes a feature fusion strategy that merges time-based and MFCC features(MFCCT)to enhance activity representation.The merged features were fed to a convolutional neural network(CNN)integrated with long shortterm memory(LSTM)—DeepConvLSTM to construct the HAR model.The MFCCT features with DeepConvLSTM achieved better performance as compared to MFCCs and time-based features on PAMAP2,UCI-HAR,and WISDM by obtaining an accuracy of 97%,98%,and 97%,respectively.In addition,DeepConvLSTM outperformed the deep learning(DL)algorithms that have recently been employed in HAR.These results confirm that the proposed hybrid features are not only practical but also generalizable,making them applicable across diverse HAR datasets for accurate activity classification.
