Multi-location Smartphone-based Activities of Daily Life Recognition using Deep Learning Techniques

Abstract

The rapid growth of the elderly population has underscored the importance of accurately recognizing Activities of Daily Living (ADLs) for effective health monitoring and timely interventions, particularly in regions such as Korea, where the aging demographic presents distinct challenges. Traditional methods often struggle with processing complex sensor data and optimizing model performance, especially when dealing with varied activities captured from multiple body locations. To address these limitations, we propose an advanced deep learning framework that utilises Long Short-Term Memory (LSTM) networks to analyze time-series sensor data, enabling the model to capture temporal dependencies and patterns within the signals. Additionally, we transform these time-series signals into Short-Time Fourier Transform (STFT) spectrogram images, which are subsequently processed using Convolutional Neural Network (CNN), EfficientNet_B0, and Vision Transformer (ViT) models through transfer learning techniques. To mitigate the class imbalance inherent in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is employed to generate synthetic samples for underrepresented classes. Furthermore, a Butterworth low-pass filter is applied to remove background noise, ensuring higher quality data for model training. The proposed models exhibited robust performance across multiple sensor placements, with the Efficient- Net_B0 model demonstrating superior accuracy, achieving 99%, 98%, 99%, and 97% for sensor placements on the bag, belly, hand, and thigh, respectively. These results highlight the potential of our approach for enhancing ADL recognition in health monitoring systems