Intelligent Interruption Management using Electro Dermal Activity based Physiological Sensor for Collaborative Sensemaking (original) (raw)
Related papers
Employing Consumer Wearables to Detect Office Workers' Cognitive Load for Interruption Management
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Office workers' productivity and well-being are reduced by interruptions, especially if they occur during an inconvenient moment. Interruptions in phases of high cognitive load are more disruptive than in phases of low cognitive load. Based on an explorative study, we suppose the presence of social codes that signal office workers' interruptibility. We propose a system that utilizes the cognitive load of an office worker to indicate situations suitable for interruptions. The cognitive load is inferred from office workers' physiological state measured by a consumer smartwatch. The system adapts an externally mounted smart device to indicate if the office worker is interruptible. To predict the cognitive load, we trained a classifier with ten office workers and achieved an accuracy between 66% and 86%. In order to validate the classifier's accurateness in an office setting, we performed a verification study with five office workers: We systematically triggered interrup...
Awareness Displays and Interruptions in Teams
SSRN Electronic Journal, 2000
Work life is filled with interruptions, most of which benefit the interrupter at the expense of the one being interrupted. We conducted an experiment to determine whether peripheral awareness information about a remote collaborator's workload aids in timing interruptive communication. Results indicate motivation to use the display exists, irrespective of whether both parties are rewarded as part of a team or not. When an informational display was present, a majority of participants used it to time their communication sensitively. We found that a display with an abstract representation of a collaborator's workload is best; it leads to better timing of interruptions without overwhelming the interrupter.
Measuring Task Engagement as an Input to Physiological Computing
Task engagement is a psychological dimension that describes effortful commitment to task goals. This is a multidimensional concept that combines cognition, motivation and emotion. This dimension may be important for the development of physiological computing systems that use real-time psychophysiology to monitor user state, particularly those systems seeking to optimise performance (e.g. adaptive automation, games, automatic tutoring). Two laboratory-based experiments were conducted to investigate measures of task engagement, based on EEG, pupilometry and blood pressure. The first study exposed participants to increased levels of memory load whereas the second used performance feedback to either engage (success feedback) or disengage (failure feedback) participants. EEG variables, such as frontal theta and asymmetry, were sensitive to disengagement due to cognitive load (experiment 1) whilst changes in systolic blood pressure were sensitive to feedback of task success. Implications for the development of physiological computing systems are discussed.
Predicting human interruptibility with sensors: a Wizard of Oz feasibility study
2003
Abstract A person seeking someone else's attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today's computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user.
Lecture Notes in Computer Science, 2014
Human emotion is a psycho-physiological state in most cases not obvious to the subject. Different permutations of emotional constituents sometimes cause similar outward expressions; therefore facial expression methods cannot achieve reliable estimates. Sensing physiological manifestations of hormonal and neural stimulations instigated by emotion and affect is widely accepted as a credible method of detecting psycho-physiological states. A major impediment in interactive environments employing physiological sensing affecting the credibility of measurements is the physical and psychological impairment caused by electrodes and wiring used for the acquisition of signals. In the system described in this paper, the above obstacle has been overcome. Physiological signals acquired via an in-house developed computer mouse and coinciding physiological patterns were investigated as reactions to emotion raising events. A classification algorithm analyzed herein produced a real time allocation model of states of engagement. Experiments have revealed strong correlations between events and respective emotional states.
Your skin resists exploring electrodermal activity as workload indicator during manual assembly
ACM Proceedings, 2019
Production lines are increasingly defined by smaller lot sizes that require workers to memorize frequent changes of assembly instructions. Previous research reports positive results of using assistive systems that compensate increments of workload by providing "just-in-time" instructions. However, there is rare evidence to which degree workload is alleviated by using assistive technologies. This work explores the potential of electrodermal activity (EDA) as a real-time monitoring tool for workload that is placed by two different assistive systems during manual assembly. In a preliminary user study (N=18), participants were induced with temporal and mental workload while conducting an assembly task with two different assistive systems: paper instructions and in-situ projections. Our preliminary findings indicate that EDA measures and working performance correlate to workload levels when using both assembly systems. Based on our results, we discuss future research in the area of smart factories that implicitly evaluate workload through EDA in real-time to adapt assistive technologies at workplaces individually during manual assembly.
Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference
ACM Transactions on Interactive Intelligent Systems, 2017
Experiencing high cognitive load during complex and demanding tasks results in performance reduction, stress, and errors. However, these could be prevented by a system capable of constantly monitoring users’ cognitive load fluctuations and adjusting its interactions accordingly. Physiological data and behaviors have been found to be suitable measures of cognitive load and are now available in many consumer devices. An advantage of these measures over subjective and performance-based methods is that they are captured in real time and implicitly while the user interacts with the system, which makes them suitable for real-world applications. On the other hand, emotion interference can change physiological responses and make accurate cognitive load measurement more challenging. In this work, we have studied six galvanic skin response (GSR) features in detection of four cognitive load levels with the interference of emotions. The data was derived from two arithmetic experiments and emoti...
Lecture Notes in Computer Science
Over the last decade, there has been a renewed interest in capturing 21st century skills using new data collection tools. In this paper, we leverage an existing dataset where multimodal sensors (mobile eye- trackers, motion sensors, galvanic skin response wristbands) were used to identify markers of productive collaborations. The data came from 42 pairs (N = 84) of participants who had no coding experience. They were asked to program a robot to solve a variety of mazes. We explored four different measures of physiological synchrony: Signal Matching (SM), Instantaneous Derivative Matching (IDM), Directional Agreement (DA) and Pearson’s Correlation (PC). Overall, we found PC to be positively associated with learning gains and DA with collaboration quality. We compare those results with prior studies and discuss implications for measuring collaborative process through physiological sensors.
A research agenda for physiological computing
Interacting with computers, 2004
Physiological computing involves the direct interfacing of human physiology and computer technology, i.e. brain-computer interaction (BCI). The goal of physiological computing is to transform bioelectrical signals from the human nervous system into real-time computer input in order to enhance and enrich the interactive experience. Physiological computing has tremendous potential for interactive innovation but research activities are often disparate and uneven, and fail to reflect the multidisciplinary nature of the topic. This paper will provide a primer on detectable human physiology as an input source, a summary of relevant research and a research agenda to aid the future development of interactive systems that utilise physiological information. q