Human Factors Research
Pain Assessment and prediction application prototype
Using Figma, I built this preliminary prototype of an application that allows users to document pain. With sensors recording physiological readings like heart rate, respiration rate, Blood volume pulse (BVP) etc., the software also attempts to predict their pain level, which users can correct.
This prototype includes a task flow diagram for each screen, as well as one for the entire application right from the login page. The information architecture is also shown.
This was a part of my Human Factors engineering coursework in my graduate studies.
Assessment of the impact of Logo Design on Brand Perception
The paper researched the impact of logo design on brand perception. After an exhaustive review of past research and existing literature, 3 design aspects were chosen for future study (color, shape, typography). Other factors like naturalness, repetitiveness were marked for future study.
The 3-member team designed a series of surveys to gather public responses on existing logo designs.
The collected data was analyzed to assess the statistical significance of common logo design aspects (color, shape, typography) and choices on brand perception through qualitative (questionnaire based consumer response recording) and quantitative assessments (Minitab, ANOVA), with increased emphasis on logo ability on effective intended brand messaging.
The study also explored (through survey) a specific aspect of logo design (color), showing logos with modified color (designed by the team) to survey participants, to assess its individual and combined statistical impact on brand messaging. As a conclusion, the logo of a company was redesigned based on collected data, and polled, with a significant increase in brand perception and popularity.
Analysis of the effect of siren noise and manual distractors on EMT and ambulance driving performance
The paper researched the effect of siren noise and manual distractors on the cognitive workload, and subsequently performance, of EMT personnel and ambulance drivers.
The 3-member team designed an experiment using the driving simulator at the IHMS lab at Northeastern University, with human subjects, to understand the effect of secondary tasks on cognitive workload. We used physiological sensors like ECG and skin conductance sensors to record physiological data, and recorded driving performance using metrics like lane deviation and speed variation.
We designed a secondary task called radio relay, to simulate ambulance operations, with the intent on studying a reduction in driving performance due to increased cognitive workload. The data collected was both quantitative (physiological data, driving performance data) as well as qualitative (self reported questionnaires) to assess correlation between driving performance and self-assessment.
The collected quantitative data was tested for statistical significance using Minitab (ANOVA, Regression analysis, R-squared values).
An analysis of braking performance among bicycle users
An anthropometric analysis of professional athletes
Using EEG to measure smartphone user experience and cognitive workload
Cognition and reaction time in professional gamers