Personal Productivity Management in the Digital Age: Measures from Research and Use of Conventional Tools

Abstract

In view of the ongoing alarming numbers of incapacity to work due to mental illness, it is important to pay attention to the factors that maintain long-term productivity of the individual. Recent research is concerned with examining relevant parameters that are measurable through technology and play a role for recognizing productivity factors such as cognitive performance or stress. However, in practice there are constraints regarding the available data sources and motives of people to use tools for self-tracking and management. In this article, we first present results from a literature review on productivity measures from research and then, complement it with initial results from an online questionnaire, which asked for the use of conventional tools by individuals. Besides frequencies of usage, we highlight major drivers for people to use applications for collecting data and managing oneself.

Machine Learning models for the Cognitive Stress Detection Using Heart Rate Variability Signals

“…Of particular concern, hence, is to detect cognitive load using a single HRV sensor and HRV features only according to the HRV measurement in psychophysiological research, which can be easily further deployed into portable wearable devices. Moreover, it allows for real-time physiological data collection, an essential feature of personal productivity management in the digital age [32].…”