Instructor Activity Recognition Using Smartwatch and Smartphone Sensors

During a classroom session, an instructor performs several activities, such as writing on the board, speaking to the students, gestures to explain a concept. A record of the time spent in each of these activities could be valuable information for the instructors to virtually observe their own style of instruction. It can help in identifying activities that engage the students more, thereby enhancing teaching effectiveness and efficiency. In this work, we present a preliminary study on profiling multiple activities of an instructor in the classroom using smartwatch and smartphone sensor data. We use 2 benchmark datasets to test out the feasibility of classifying the activities. Comparing multiple machine learning techniques, we finally propose a hybrid deep recurrent neural network based approach that performs better than the other techniques

Experimental Studies On Drivers Distractions Investigating the Effects of Distractions on Driving Performance

Driving hazards encompass a range of factors, such as poor road conditions, weather, and human behavior. They represent a significant threat to the safety of individuals on the road and are very likely one of the leading causes of traffic accidents. These hazards exist alongside well-known risk factors like driving under the influence of drugs, extreme fatigue, drowsiness, and excessive speeding. Impaired driving, which includes behaviors such as drunk or drugged driving, has persistently plagued American roads for many years. It poses an ongoing risk, not only to the impaired driver but also to other road users and the surrounding infrastructure. The primary objective of this paper is to investigate specific forms of distractions that drivers encounter. These distractions encompass activities such as texting or using a mobile device, eating, conversing with passengers, and paying attention to advertising signs along the roadside. The research aims to understand how each of these distractions affects a driver’s ability to operate a vehicle safely. In essence, this chapter delves into the various ways in which these distractions can compromise a driver’s attention and, consequently, impact road safety</p

Ionically modified magnetic nanomaterials for arsenic and chromium removal from water

10.1016/j.cej.2013.03.114Chemical Engineering Journal225607-615CMEJ