Ahmad Jalal 교수

Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing s

Gestures have been used for nonverbal communication for a long time, but human-computer interaction (HCI) via gestures is becoming more common in the modern era. To obtain a greater recognition rate, the traditional interface comprises various devices, such as gloves, physical controllers, and markers. This study provides a new markerless technique for obtaining gestures without the need for any barriers or pricey hardware. In this paper, dynamic gestures are first converted into frames. The noi

Furthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers and gyroscopes. Initially, these sensors were used for device feature advancement, but now, they can be used for a variety of applications. Human activity recognition (HAR) is an interesting research area that can be used for many applications like health monitoring, sports, fitness, medical purposes, etc. In this

Findings demonstrate considerable enhancements in object detection and semantic analysis when compared to the methodologies that are currently being utilized.

Teachers, students, and medical professionals can all efficiently transmit and comprehend information by utilizing our suggested system. The obtained accuracy rates highlight how our method might improve communication and make information exchange easier in various domains.

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human

Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions.

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their r

Human activity recognition (HAR) has attracted significant attention in various fields, including healthcare, smart homes, and human-computer interaction. Accurate HAR can enhance user experience, provide critical health insights, and enable sophisticated context-aware applications. This paper presents a comprehensive system for HAR utilizing both RGB videos and inertial measurement unit (IMU) sensor data. The system employs a multi-stage processing pipeline involving preprocessing, segmentation