Sensor-based human activity recognition with wearable devices has captured the attention of researchers in the last decade. The possibility of collecting large sets of data from various sensors in different body parts, automatic feature extraction, and aiming to recognize more complex activities have led to a rapid increase in the use of deep learning models in the field. More recently, using attention-based models for dynamically fine-tuning the model features and, in turn, improving the model performance has been investigated. However, the impact of using channel, spatial, or combined attention methods of the convolutional block attention module (CBAM) on the high-performing DeepConvLSTM model, a hybrid model proposed for sensor-based human activity recognition, has yet to be studied. Additionally, since wearables have limited resources, analysing the parameter requirements of attention modules can serve as an indicator for optimizing resource consumption. In this study, we explored the performance of CBAM on the DeepConvLSTM architecture both in terms of recognition performance and the number of additional parameters required by attention modules. In this direction, the effect of channel and spatial attention, individually and in combination, were examined. To evaluate the model performance, the Pamap2 dataset containing 12 daily activities and the Opportunity dataset with its 18 micro activities were utilized. The results showed that the performance for Opportunity increased from 0.74 to 0.77 in the macro f1-score owing to spatial attention, while for Pamap2, the performance increased from 0.95 to 0.96 owing to the channel attention applied to DeepConvLSTM with a negligible number of additional parameters. Moreover, when the activity-based results were analysed, it was observed that the attention mechanism increased the performance of the activities with the worst performance in the baseline model without attention. We present a comparison with related studies that use the same datasets and show that we could achieve higher scores on both datasets by combining CBAM and DeepConvLSTM.
Smartwatches and smartphones are extensively used in human activity recognition, particularly for step counting and daily sports applications, thanks to the motion sensors integrated into these devices. Machine learning algorithms are often utilized to process sensor data and classify the activities. There are many studies that explore the use of traditional classification algorithms in activity recognition, however, recently, deep learning approaches are also receiving attention. In this paper, we utilize a dataset that particularly consists of smoking-related activities and explore the recognition performance of three deep learning
architectures, namely Long-Short Term Memory (LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN).We evaluate their performances according to different hyperparameters, different sensor types and device types. The results exhibit that the performance of LSTM is much higher than that of CNN and RNN. Moreover, the use of magnetometer and gyroscope together with accelerometer data improves the performance. Use of data from smartphone sensors also enhances the performance results and the final accuracy with the best parameter combinations is observed to be 98.2%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.