Identification of an individual’s frustration in the work environment through a multi-sensor computer mouse

Abstract

Older adults traditionally face major challenges at work when it comes to dealing with new technological tools. A sense of overwhelm and frustration can quickly arise under these circumstances. Continuous negative feelings in the work environment may lead to the increase of the risks for cognitive decline and threaten independence and quality of life. In this work, we focus on the seamless identification of frustration of older adults at work via physiological sensors embedded in an in-house developed computer mouse, denoted as CogniMouse. For the purpose of this research, we have developed a probabilistic classification algorithm that receives real-time signals and physiological measurement streams as input, and accordingly identifies frustration events. Ultimately, such classification can be leveraged to deliver user interventions and personalized solutions to help reduce user frustration.