Smart mobile devices are being widely used to identify and track human behaviors in simple and complex daily activities. The evolution of wearable sensing technologies pertaining to wellness, living surveillance, and fitness tracking is based on the accurate analysis of people’s behavior from the data acquired through different sensors embedded in smart devices, especially wrist-worn wearable technologies such as smartwatches. Many deep learning techniques have been developed to realize human activity recognition (HAR), with simple daily activities being focused on. However, several challenges remain to be addressed in complex HAR research involving specific human behaviors in different contexts. To address the problems pertaining to complex HAR, a deep neural network composed of convolutional layers and residual networks was developed in this work. Additional attention was incorporated in the system by using a squeeze-and-excite mechanism. The model effectiveness was investigated considering three publicly available datasets, (WISDM-HARB, UT-Smoke, and UT-Complex). The proposed network achieved overall accuracies of 94.91%, 98.75%, and 97.73% over WISDM-HARB, UT-Smoke, and UT-Complex, respectively. The results showed that deep residual networks are more durable and superior at activity recognition than the existing models.

The creation of the Internet of Things (IoT), along with the latest developments in wearable technology, has provided new opportunities in human activity recognition (HAR). The modern smartwatch offers the potential for data from sensors to be relayed to novel IoT platforms, which allow the constant tracking and monitoring of human movement and behavior. Recently, traditional activity recognition techniques have done research in advance by choosing machine learning methods such as artificial neural network, decision tree, support vector machine, and naive Bayes. Nonetheless, these conventional machine learning techniques depend inevitably on heuristically handcrafted feature extraction, in which human domain knowledge is normally limited. This work proposes a hybrid deep learning model called CNN-LSTM that employed Long Short-Term Memory (LSTM) networks for activity recognition with the Convolution Neural Network (CNN). The study makes use of HAR involving smartwatches to categorize hand movements. Using the study based on the Wireless Sensor Data Mining (WISDM) public benchmark dataset, the recognition abilities of the deep learning model can be accessed. The accuracy, precision, recall, and F-measure statistics are employed using the evaluation metrics to assess the recognition abilities of LSTM models proposed. The findings indicate that this hybrid deep learning model offers better performance than its rivals, where the achievement of 96.2% accuracy, while the f-measure is 96.3%, is obtained. The results show that the proposed CNN-LSTM can support an improvement of the performance of activity recognition.