RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition

A novel physical activity recognition approach using deep ensemble optimized transformers and reinforcement learning

In recent years, human physical activity recognition has increasingly attracted attention from different research fields such as healthcare, computer-human interaction, lifestyle monitoring, and athletics. Deep learning models have been extensively employed in developing physical activity recognition systems. To improve these models, their hyperparameters need to be initialized with optimal values. However, tuning these hyperparameters manually is time-consuming and may lead to inaccurate results. Moreover, the application of these models to different data resources and the integration of their results into the overall data processing pipeline are challenging issues in physical activity recognition systems. In this paper, we propose a novel ensemble method for physical activity recognition based on a deep transformer-based time-series classification model that uses heart rate, speed, and distance time-series data to recognize physical activities. In particular, we develop a modified arithmetic optimization algorithm to automatically adjust the optimal values of the classification models' hyperparameters. Moreover, a reinforcement learning-based ensemble approach is proposed to optimally integrate the results of the classification models obtained using heart rate, speed, and distance time-series data and, subsequently, recognize the physical activities. Experiments performed on a real-world dataset demonstrated that the proposed method achieves promising efficiency in comparison to other state-of-the-art models. More specifically, the proposed method increases the performance compared to the second-best performer by around 3.44 %, 9.45 %, 5.43 %, 2.54 %, and 7.53 % based on accuracy, precision, recall, specificity, and F1-score evaluation metrics, respectively.

Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors

Background

In digital medicine, human activity recognition (HAR) can be used to track and assess a patient's progress throughout rehabilitation, enhancing the quality of life for the elderly and the disabled.

Methods

A patch-type flexible sensor that integrated dynamic electrocardiogram (ECG) and acceleration signal (ACC) was used to record the signals of the various behavioral activities of 20 healthy volunteers and 25 patients with pneumoconiosis. Seven HAR tasks were then carried out on the data using four different deep learning methods (CNN, LSTM, CNN-LSTM and GRU).

Results

When ECG and ACC were obtained simultaneously, the overall accuracy rates of HAR for healthy group were 0.9371, 0.8829, 0.9843 and 0.9486 by the CNN, LSTM, CNN-LSTM and GRU models, respectively. In contrast, the overall accuracy rates of HAR for the pneumoconiosis patients' group were 0.8850, 0.7975, 0.9425 and 0.8525 by the four corresponding models. The accuracy of HAR for both groups using all four models is higher than when only ACC signal is detected.

Conclusion

The addition of the ECG signal significantly improves HAR outcomes in the group of healthy individuals, while having relatively less enhancing effects on the group of patients with pneumoconiosis. When ECG and ACC signals were combined, the increase in HAR accuracy was notable compared to cases where no ECG data was provided. These results suggest that the combination of ACC and ECG data can represent a novel method for the clinical application of HAR.

Deep fit_predic: a novel integrated pyramid dilation EfficientNet-B3 scheme for fitness prediction system

This study introduces novel deep learning (DL) techniques for effective fitness prediction using a person's health data. Initially, pre-processing is performed in which data cleaning, one-hot encoding and data normalization are performed. The pre-processed data are then fed into the feature selection stage, where the useful features are extracted using the enhanced chameleon swarm (ECham-Sw) optimization technique. Then, a clustering process is performed using Minkowski integrated gravity center clustering (Min-GCC) to cluster the health profiles of each individual. Finally, the Pyramid Dilated EfficientNet-B3 (PyDi-EfficientNet-B3) technique is proposed to predict the fitness of each individual efficiently with enhanced accuracy of 99.8%.