PHYSIOLOGICAL INDICATORS FOR THE USABILITY EVALUATION OF APPROPRIATE TECHNOLOGY IN AGRICULTURAL PROCESSING MACHINES STUDY CASE: EXTRUDER
Appropriate Technology (AT) is designed to substitute manual work and to increase productivity for small-medium businesses. Due to its low-cost technology that affects its design quality, the acceptance of AT appears to be low. Currently, there are no indicators that can be used to determine the qua...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/79853 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Appropriate Technology (AT) is designed to substitute manual work and to increase productivity for small-medium businesses. Due to its low-cost technology that affects its design quality, the acceptance of AT appears to be low. Currently, there are no indicators that can be used to determine the quality of AT designs based on human physiological responses as users, as a representation of AT usability. The purpose of this research is to explore the effectiveness of physiological measurements utilizing Heart Rate Monitor (HRM) and brain waves in evaluating the usability of AT machines as reflected in the workload when operating AT devices. Several parameters were collected from HRM (including frequency domain and time domain) and EEG were used to evaluate two ATs that represented “good” and “poor” agricultural processing machines. Sixteen participants were involved in this study in which they used both ATs. The task performed is the processing of corn flour into raw noodles using an extruder. In addition, subjective indicators are also used, namely the Mental Effort Rating Scale (RSME) questionnaire. The result of this study demonstrated that there is a significant workload experienced by participants when using both AT. The HRV indicators that successfully detected differences in workload both when comparing the two machines and when comparing the initial condition to the processing condition are SNS and pNN50, while in brain waves, the indicators that detected the difference in perceived workload by the participants are alpha, beta, and theta waves. In the future, the findings of this research may provide objective data for evaluating the usability aspects of AT machines.
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