# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
###### Abstract  
Sensor-based human activity recognition has been widely used in various fields; however, there are still challenges involving recognition of daily complex human activities using sensors. In order to solve the problem of timeliness and homogeneity of recognition functions in human activity recognition models, we propose a human activity recognition model called 'BSTCA-HAR' based on a long short-term memory (LSTM) network. The approach proposed in this paper combines an attention mechanism and a temporal convolutional network (TCN). The learning and prediction units in the model can efficiently learn important action data while capturing long time-dependent information as well as features at different time scales. Our series of experiments on three public datasets (WISDM, UCI-HAR, and ISLD) with different data features confirm the feasibility of the proposed method. This method excels in dynamically capturing action features while maintaining a low number of parameters and achieving a remarkable average accuracy of 93%, proving that the model has good recognition performance. humanactivity recognition; sensor data; LSTM; deep learning +
Footnote †: journal: Medical  
the tremendous amount of data created by the recent rapid development of wearable sensors [9]. Wide-ranging real-world uses for wearable-sensor-based health analytics include surveillance systems, telemedicine, smart healthcare, assisted living facilities, and predictive health diagnostics [10]. In addition to increasing the precision and effectiveness of human activity detection, wearable sensor technology is portable, low-power, and effective. It also offers creative and useful solutions for safety monitoring and health management. Despite these benefits, modeling and analysis are significantly hampered by the complexity and volume of data produced by wearable sensors. In order to successfully analyze and interpret the data, a variety of techniques must be applied, including deep learning and time series modeling. It is possible to identify human behavior more precisely by analyzing, modeling, comprehending, and producing data from wearable sensors. Consequently, the research presented in this paper explores activity recognition using human gesture recognition algorithms based on wearable mobile sensors. This research is of great significance and value in advancing smart health monitoring, smart wearable devices, human-computer interaction technologies, and other related fields. This study introduces a human activity recognition (HAR) model that combines bidirectional long short-term memory (BiLSTM) with a temporal convolutional network (TCN) and attention mechanism. The purpose of this integration is to overcome existing challenges in HAR by increasing the model's ability to capture temporal patterns and pull out relevant features from the data, thereby enhancing both the accuracy and dependability of the system. The pivotal contributions of this endeavor can be summarized as follows:  
* The BSTCA-HAR architecture is proposed for accepting raw unlabeled sensor data as input and then autonomously extracting features. The adaptability of the model is enhanced through crafting the LSTM and convolutional layers with minimal parameters, which enables the capture of long-term correlations within intricate time series data while mitigating the risk of model overfitting. * We conducted detailed experimental tests on three publicly available mobile sensor datasets in order to demonstrate the effectiveness of the BSTCA-HAR network in human activity recognition research. Specifically, we utilized the WISDM, UCI-HAR, and ISLD datasets to experimentally validate the performance of our model. * To demonstrate the superiority of the introduced architecture, we conducted detailed comparative evaluations, comparing BSTCA-HAR with various techniques described recently in the literature.