wearable computers at work case evaluation

Results indicate that Blink outperforms Dwell and Voice in completion time. Our exploration involves a user study with 24 participants to investigate the performance, user experience, and workload of three hands-free selection mechanisms (Dwell, Blink, Voice) to complement head-based pointing.

This research focuses on both, text selection via hands-free interaction. Similar to hands-free interaction, text selection is underexplored. As VR HMDs become ubiquitous, people will need to do text editing, which requires selecting text segments. In contrast, hands-free interaction avoids the need for users' hands and although it can afford additional benefits, there has been limited research in exploring and evaluating hands-free techniques for these HMDs. Hand-based interaction, such as using a handheld controller or making hand gestures, has been widely adopted as the primary method for interacting with both virtual reality (VR) and augmented reality (AR) head-mounted displays (HMDs). Results reveal that the suggested method performs well in categorizing both typical daily activities and athletic endeavors. Furthermore, the study contrasts three machine learning models-support vector machines, decision trees, and multilayer perceptron -with respect to their usability, controllability, and classifier performance. From a comparison of the various sensor modalities, we can deduce that magnetometers, followed by accelerometers and gyroscopes, provide the best classification results when only a single sensor type is employed. When it comes to body location, sensor devices worn on the legs provide the most valuable data. Classifiers' proper differentiation rates and computational costs are compared to help you choose the best one for your needs. The classifiers are checked for accuracy using three distinct cross-validation strategies. The optimization technique is a combination of the sine cosine and particle swarm optimization algorithms, and the ensemble model is made up of three classifiers: support vector machines, decision trees, and multilayer perceptron. Voting ensemble classification models, where votes are weighted and optimized with a new optimization technique, are offered as a means to actualize this classification problem. Each device has tri-axial sensors, including a gyroscope, accelerometer, and magnetometer. This research analyzes the effectiveness of several methods for categorizing human actions captured by inertial and magnetic sensor units worn on the chest, arms, and legs.

Wearable computers at work case evaluation