A Computer Mouse for Stress Identification of Older Adults at Work

Stress is an unpleasant condition that entails negative emotions such as fear, worry and nervousness. Motivated by existing research that accompanies stress with physical reactions like increased heart rate, blood volume, pupil dilation and skin conductance, this work builds on the premise that measuring such reactions in real-time could implicitly identify stress of older adults at work while interacting with a system. For this purpose, an inhouse computer mouse was built with embedded sensors for measuring the users’ heart rate, skin conductance, skin temperature, and grip force. We have developed a probabilistic classification algorithm that receives as input these physiological measurements, and accordingly identifies emotional stress events. This work contributes to a large body of research in user modeling, aiming to identify when computer users are stressed, and accordingly provide intelligent interventions and personalized solutions to help reduce their frustration and prevent negative health conditions.

[1]  Mary Czerwinski,et al.  Under pressure: sensing stress of computer users , 2014, CHI.

[2]  D. Kaufer,et al.  Stress, social behavior, and resilience: Insights from rodents , 2014, Neurobiology of Stress.

[3]  Charalampos Rizopoulos,et al.  Deducing User States of Engagement in Real Time by Using a Purpose Built Unobtrusive Physiological Measurement Device: An Empirical Study and HCI Design Challenges , 2014, HCI.

[4]  David Sun,et al.  MouStress: detecting stress from mouse motion , 2014, CHI.

[5]  Marios Belk,et al.  Identification of an Individual's Frustration in the Work Environment Through a Multi-sensor Computer Mouse , 2016, HCI.

[6]  Marios Belk,et al.  CogniWin: An Integrated Framework to Support Older Adults at Work , 2016, UMAP.

[7]  D. Kirsch The Sentic Mouse: Developing a tool for Measuring Emotional Valence , 1997 .

[8]  Christine L. Lisetti,et al.  A User Model of Psycho-physiological Measure of Emotion , 2007, User Modeling.

[9]  Jens Wahlström,et al.  Ergonomics, musculoskeletal disorders and computer work. , 2005, Occupational medicine.

[10]  Veikko Surakka,et al.  Emotions and heart rate while sitting on a chair , 2005, CHI.

[11]  Marios Belk,et al.  CogniWin - A Virtual Assistance System for Older Adults at Work , 2015, HCI.

[12]  O. Wolf,et al.  Impaired Memory Retrieval after Psychosocial Stress in Healthy Young Men , 2005, The Journal of Neuroscience.

[13]  Neil B. Rappaport,et al.  A daily stress inventory: Development, reliability, and validity , 1987, Journal of Behavioral Medicine.

[14]  P. Lovibond,et al.  Manual for the Depression Anxiety Stress Scales. 2 , 1995 .

[15]  Akane Sano,et al.  Stress Recognition Using Wearable Sensors and Mobile Phones , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[16]  Robert D. Ward,et al.  Physiological responses to different WEB page designs , 2003, Int. J. Hum. Comput. Stud..

[17]  Michael Minge,et al.  Measuring multiple components of emotions in interactive contexts , 2006, CHI Extended Abstracts.

[18]  Marios Belk,et al.  CogniMouse: On Detecting Users' Task Completion Difficulty through Computer Mouse Interaction , 2015, CHI Extended Abstracts.

[19]  Costas Mourlas,et al.  A Real Time Attachment Free, Psycho Physiological Stress and Heart Rate Measurement System , 2011, Int. J. Meas. Technol. Instrum. Eng..

[20]  Matthew S. Goodwin,et al.  iCalm: Wearable Sensor and Network Architecture for Wirelessly Communicating and Logging Autonomic Activity , 2010, IEEE Transactions on Information Technology in Biomedicine.

[21]  Guillén Fernández,et al.  Stressed Memories: How Acute Stress Affects Memory Formation in Humans , 2009, The Journal of Neuroscience.