Analyzing the Variability of RNN Hyperparameters and Architectures for HAR with Wearable Sensor Data

Human activity recognition (HAR) is a rapidly growing field of study with important practical implications in areas as diverse as medicine, sports performance analysis, and the care of the elderly. There has been a lot of interest in studying HAR because of the possibility it offers to improve human performance and quality of life through the use of ubiquitous sensors. Due to their ability to model sequential data and capture temporal dependencies in time-series data, Recurrent Neural Networks (RNNs) have become a useful tool for HAR. In this research, many recurrent neural network (RNN) architectures and hyperparameters are tested on accelerometer data from wearable sensors to determine which is most suited for HAR. To be more specific, we analyze the relationship between the number of hidden units and the accuracy of the models for three distinct RNN architectures (Simple RNN, LSTM, and GRU). The HARUS dataset is used, which contains accelerometer data from 30 people engaged in 6 distinct activities. In comparison to the other two architectures, our results demonstrate that the LSTM architecture performs best on the HARUS dataset, with an accuracy of 95.0%. Furthermore, we find that increasing the number of hidden units generally improves accuracy, with the best results being achieved with 128 hidden units. Accuracy improves alongside sequence length, while exceeding a specific value can cause overfitting. In a separate study, researchers used accelerometer data from a wearable sensor to train an RNN model for activity recognition, and found that it achieved an overall accuracy of 99.54 percent on the test set. The model did fairly well for more complex activities like walking up and down stairs, but it excelled at the simpler tasks of walking and jogging, standing, and sitting. Our research shows that LSTM is an effective architecture for HAR applications, and that hyperparameters like the number of hidden units and sequence length are particularly important. Our results add to the existing HAR literature by disclosing the best architecture and hyperparameters for recognising human activities from wearable sensor data obtained via accelerometers.