Tsuyoshi Okayama

Kansei’s Physiological Measurement in Small-Medium Sized Enterprises Using Profile of Mood States and Heart Rate

Kansei’s physiological measurement were pursued in 4 (four) production systems of small-medium sized enterprises in in Special Region of Yogyakarta (DIY), Indonesia. These SMEs produces indigenous food product of Bakpia, Cracker, Fish chips and Tempe. Profile of Mood States (POMS) was used as the verbal parameter to measure Total Mood Disturbance (TMD). Heart rate was used as the non-verbal parameter. The measurement was pursued in daily check-in before working and check-out after working. The research results indicated TMD and heart rate are sensible to measure physiological response to workplace environmental parameters. Workplace environment has greater impact to the sensibility of worker mood and heart rate in Bakpia and Tempe’s SMEs, while it has less impact in Cracker and Fish Chips’s SMEs.

The “Smart Garden” System using Augmented Reality

Abstract The goal of this research is to build an advanced gardening system called “Smart Garden”. The goal of this research is to build an advanced gardening system called “Smart Garden”. The Smart Garden support beginners in farming operations, such as home gardeners. The objectives of this paper are to develop a support system of the Smart Garden which can 1) visualize guidance of farming operations using CGs and overlay them on a field where the operator is working, and 2) record the operators positions and viewpoints during their operations.

Identification of environmental ergonomics control system for Indonesian SMEs

This paper identified the environmental ergonomics control system for Indonesian SMEs (Small Medium-sized Enterprises). The system was defined that workstation environment could be controlled using worker workload and workstation temperature difference. The research objectives were: 1) To analyze the relationship between workstation temperature difference and workload; 2) To identify the environmental ergonomics control system for Indonesian SMEs. 380 data set of heart rate, indoor temperature and temperature set points were collected from Indonesian SMEs. Workloads were classified based on heart rate. Temperature difference were determined using set point temperature (Before working) and indoor temperature (After working). Temperature difference were categorized to 14 quadrants. Actors of system were identified. Research result indicated that temperature difference generated various workload. System identification was expected to support further development of environmental ergonomics control.

Affective Temperature Control in Food SMEs using Artificial Neural Network

ABSTRACT This paper highlights modeling affective temperature control in food small and medium-sized enterprises (SMEs). Modeling defined that workstation temperature set point could be controlled based on worker heart rate and workstation environment using Artificial Neural Network (ANN). The research objectives were: 1) to propose modeling affective temperature control in food SMEs based on heart rate and workstation environment; and 2) to develop an ANN model for predicting workstation temperature set point. Training and validation data were collected from six food SMEs in Yogyakarta Special Region, Indonesia. The data of temperature set points were verified using a simulated confined room. The inputs of the ANN model were worker heart rate, workstation temperature, relative humidity distribution and light intensity. The output was temperature set point. Research results concluded satisfactory performance of ANN. The model could be used to provide environmental ergonomics in food SMEs.