ﻻ يوجد ملخص باللغة العربية
Flow-like experiences at work are important for productivity and worker well-being. However, it is difficult to objectively detect when workers are experiencing flow in their work. In this paper, we investigate how to predict a workers focus state based on physiological signals. We conducted a lab study to collect physiological data from knowledge workers experienced different levels of flow while performing work tasks. We used the nine characteristics of flow to design tasks that would induce different focus states. A manipulation check using the Flow Short Scale verified that participants experienced three distinct flow states, one overly challenging non-flow state, and two types of flow states, balanced flow, and automatic flow. We built machine learning classifiers that can distinguish between non-flow and flow states with 0.889 average AUC and rest states from working states with 0.98 average AUC. The results show that physiological sensing can detect focused flow states of knowledge workers and can enable ways to for individuals and organizations to improve both productivity and worker satisfaction.
Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effective measure to gather labeled training datasets, is not immune to the
Hand Gesture Recognition (HGR) based on inertial data has grown considerably in recent years, with the state-of-the-art approaches utilizing a single handheld sensor and a vocabulary comprised of simple gestures. In this work we explore the benefit
With the advancements in social robotics and virtual avatars, it becomes increasingly important that these agents adapt their behavior to the mood, feelings and personality of their users. One such aspect of the user is empathy. Whereas many studies
Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health informati
Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a