TY - GEN
T1 - Towards Social Enterprise with Internet of Office Desks
AU - Vildjiounaite, Elena
AU - Kallio, Johanna
AU - Kantorovitch, Julia
AU - Kyllönen, Vesa
AU - Räsänen, Pauli
AU - Ronkainen, Jussi
PY - 2020
Y1 - 2020
N2 - Social enterprises are organisations, combining profit making with support of their employees and environment. Employee stress is harmful for both society (due to increased risk of mental health disorders and cardiovascular diseases) and for enterprise performance (due to increased risk of presenteeism, employee turnover and early retirement). This work aims at helping enterprises to improve employee wellbeing. To this end, we introduce a concept of IoT-based privacy-aware “team barometer” and present a first study into using inexpensive PIR (passive infrared) motion detection sensors in such barometers. The study was conducted as follows: first, we deployed IoT system in real offices and collected employee data in the course of everyday work during several months. Second, we developed a machine learning method to classify human conditions on the basis of collected PIR data. In the tests, this method recognised employees’ stress with 80% accuracy and dissatisfaction with indoor environmental quality - with 75% accuracy. Third, we integrated stress detection results into a “team barometer” and conducted interviews of line managers. Interview results suggest that the proposed IoT-based team barometer can be beneficial for both employees and enterprises because of its potential to discover and mitigate workplace problems notably faster than with current practice to use periodic surveys.
AB - Social enterprises are organisations, combining profit making with support of their employees and environment. Employee stress is harmful for both society (due to increased risk of mental health disorders and cardiovascular diseases) and for enterprise performance (due to increased risk of presenteeism, employee turnover and early retirement). This work aims at helping enterprises to improve employee wellbeing. To this end, we introduce a concept of IoT-based privacy-aware “team barometer” and present a first study into using inexpensive PIR (passive infrared) motion detection sensors in such barometers. The study was conducted as follows: first, we deployed IoT system in real offices and collected employee data in the course of everyday work during several months. Second, we developed a machine learning method to classify human conditions on the basis of collected PIR data. In the tests, this method recognised employees’ stress with 80% accuracy and dissatisfaction with indoor environmental quality - with 75% accuracy. Third, we integrated stress detection results into a “team barometer” and conducted interviews of line managers. Interview results suggest that the proposed IoT-based team barometer can be beneficial for both employees and enterprises because of its potential to discover and mitigate workplace problems notably faster than with current practice to use periodic surveys.
UR - http://www.scopus.com/inward/record.url?scp=85097425005&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62803-1_29
DO - 10.1007/978-3-030-62803-1_29
M3 - Conference article in proceedings
SN - 978-3-030-62802-4
T3 - IFIP Advances in Information and Communication Technology
SP - 361
EP - 374
BT - Human-Centric Computing in a Data-Driven Society - 14th IFIP TC 9 International Conference on Human Choice and Computers, HCC14 2020, Proceedings
A2 - Kreps, David
A2 - Komukai, Taro
A2 - Gopal, T. V.
A2 - Ishii, Kaori
PB - Springer
T2 - 14th IFIP TC9 Human Choice and Computers Conference, CFP
Y2 - 9 September 2020 through 11 September 2020
ER -