A novel ViT-BILSTM model for physical activity intensity classification in adults using gravity-based acceleration.

ABSTRACT:

Aim

The aim of this study is to apply a novel hybrid framework incorporating a Vision Transformer (ViT) and bidirectional long short-term memory (Bi-LSTM) model for classifying physical activity intensity (PAI) in adults using gravity-based acceleration. Additionally, it further investigates how PAI and temporal window (TW) impacts the model' s accuracy.

Method

This research used the Capture-24 dataset, consisting of raw accelerometer data from 151 participants aged 18 to 91. Gravity-based acceleration was utilised to generate images encoding various PAIs. These images were subsequently analysed using the ViT-BiLSTM model, with results presented in confusion matrices and compared with baseline models. The model's robustness was evaluated through temporal stability testing an

SUBMITTER: Wang L 

PROVIDER: S-EPMC11786420 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature