Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks  
[PERSON]\({}^{\text{\textregistered}}\)  
\({}^{\text{\textregistered}}\) and [PERSON]\({}^{\text{\textregistered},\text{\textregistered},\text{\textregistered}}\)  
examines human activity recognition (HAR), which accurately determines a person's activity using sensors embedded in wearable devices worn on the body. HAR analyzes people's movements using technology and sensors. Automatically identifying physical activities has become vital in ubiquitous computing, interpersonal communication, and behavioral analysis research. Extensively applying HAR can enhance human safety and well-being. In hospitals, patients can wear devices that detect mobility, heart rate, and sleep quality, enabling remote monitoring without constant expert oversight. Such tools also facilitate tracking elderly and young patients. HAR-powered smart home solutions provide energy savings and personalized comfort by recognizing whether individuals are present and adapting lights or temperature accordingly. Some personal safety gadgets can automatically notify emergency responders or designated contacts when required. Additionally, many other applications need automated coordination based on identifying user activities. Technological advances have enabled the creation of wearable devices with sensing, processing, and communication capabilities that are more compact, affordable, and power-efficient. Sensors embedded in these wearables, commonly leveraged for Internet of Things (IoT) applications, can rapidly capture various body motions and recognize human behaviors. In particular, inertial measurement unit (IMU) sensors containing accelerometers and gyroscopes have improved drastically in recent years, simplifying the tracking and detection of human movements. Undoubtedly, sensor-based HAR furnishes a highly versatile and cost-effective approach for continual monitoring while better addressing privacy concerns. HAR is not a new research field, with over a decade of discussions. Consequently, numerous published studies have focused on developing activity recognition systems. Earlier systems relied on machine learning approaches, which yielded some success. However, these machine learning methods depended heavily on manual feature engineering techniques [5; 6]. As such, they were time-intensive and laborious, necessitating substantial human input. Additionally, they failed to guarantee generalization to novel, unseen scenarios [7; 8]. Due to these drawbacks, the field has witnessed a progressive shift from machine learning to deep learning techniques, creating many new research opportunities. A key advantage of deep learning is its layered data processing architecture, which enables models to learn from raw, unprocessed input data without complex preprocessing. This facilitates the analysis of multimodal sensor data [9]. A primary reason for the prevalence of deep learning in HAR is that machine learning algorithms depend greatly on human input and preprocessing of data before implementation [10]. In contrast, deep learning models can autonomously extract knowledge and insights directly from sensor data gathered by devices [11] without manual effort or domain expertise. Currently, many sensor-based HAR systems require wearable devices to be placed in fixed positions on the body [12; 13; 14]. These systems are referred to in research as position-specific HARs. However, people often move their wearable gadgets to different on-body locations. This decreases recognition accuracy since sensor readings for the same motion can vary substantially across body placement sites. As such, the primary challenge for sensor-based HAR platforms is developing position-independent solutions that work reliably, regardless of a device's location [15]. The key objective of this research is to develop a position-independent HAR system using deep learning techniques.