Human Activity Recognition Based on Two-Channel Residual–GRU–ECA Module with Two Types of Sensors

Abstract

With the thriving development of sensor technology and pervasive computing, sensor-based human activity recognition (HAR) has become more and more widely used in healthcare, sports, health monitoring, and human interaction with smart devices. Inertial sensors were one of the most commonly used sensors in HAR. In recent years, the demand for comfort and flexibility in wearable devices has gradually increased, and with the continuous development and advancement of flexible electronics technology, attempts to incorporate stretch sensors into HAR have begun. In this paper, we propose a two-channel network model based on residual blocks, an efficient channel attention module (ECA), and a gated recurrent unit (GRU) that is capable of the long-term sequence modeling of data, efficiently extracting spatial–temporal features, and performing activity classification. A dataset named IS-Data was designed and collected from six subjects wearing stretch sensors and inertial sensors while performing six daily activities. We conducted experiments using IS-Data and a public dataset called w-HAR to validate the feasibility of using stretch sensors in human action recognition and to investigate the effectiveness of combining flexible and inertial data in human activity recognition, and our proposed method showed superior performance and good generalization performance when compared with the state-of-the-art methods.

Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices

“…Using wearable sensors built into everyday objects is common practice, even reaching the level of the incorporation of stretch sensors into clothes. Combining stretch sensors and inertial sensors can yield good results with an accuracy rate of 97.51% [28]. This approach implies that the user wears custom add-on modules that can also be built into the clothes, and it requires specialized hardware modules for this, making the system a bit more difficult to wear for extensive periods of time.…”

A Deep Learning Network with Aggregation Residual Transformation for Human Activity Recognition Using Inertial and Stretch Sensors

“…The IS-Data dataset, introduced in publication [14], was obtained through self-collection using a six-axis inertial sensor and a stretch sensor. In this study, six individuals had two stretch sensors installed on their knees while wearing knee protectors to ensure proper placement on the central region of the kneecap.…”

“…One example is the stretch sensor, which shows promise for application in wearable technology. These sensors can be attached to garments to collect data related to body movements, respiration, and cardiac activity [14,15].…”

Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy

“…HAR methods can be categorized into two primary groups [10,[17][18][19][20][21]: sensor-based and vision-based. Using cameras situated in the human environment, utilizing the vision-based method, human activity features are extracted from images and video streams [22]. In the sensor-based method, data is gathered using sensors like accelerometers, gyroscopes, and magnetometers [20].…”

“…In the sensor-based method, data is gathered using sensors like accelerometers, gyroscopes, and magnetometers [20]. Sensors, unlike the equipment required for vision-based methods, are lightweight and portable [22].…”