HKU Movie Psychology Study

2021

Emotion-Driven Film Editing: Crafting Trailers Based on Audience Arousal

No. of Participants	350
​Nature of Event:	Event (Research study)
Products Used:	Upmood Band, Upmood Insight
Metrics Collected:	Mood, Stress Level, Valence Level, HRV (Heart Rate Variability)

The University of Hong Kong (HKU) is a leading international university, and its Psychology Department is at the forefront of innovative research into human behavior and cognition. Professors Tsz Yan So, Man Yi Erica Li, and Hakwan Lau conducted a groundbreaking study exploring the intersection of physiological responses, emotion, and media consumption. Their research aimed to objectively identify the most emotionally arousing segments of movies, with the practical application of creating more impactful short trailers. This pioneering work sought to move beyond subjective audience feedback, leveraging objective emotion monitoring to understand and predict viewer engagement.

Introduction

This research project embarked on an exciting journey to understand how human physiological responses correlate with emotional arousal during movie viewing. The primary goal was to develop an objective method for identifying emotionally impactful moments, which could then be used to inform creative processes like film editing. By integrating Upmood's wearables and emotion detection algorithm, the professors aimed to scientifically pinpoint segments of movies that elicited the strongest emotional responses. Emotion monitoring was critical for this project as it provided the precise, real-time physiological data necessary to quantify arousal levels, moving beyond traditional survey methods to uncover the true emotional landscape of the audience.

Capturing Emotions in Motion Pictures 

The study involved 350 participants who watched various movies while wearing Upmood bands. These wearables continuously collected physiological data, specifically Heart Rate Variability (HRV). Upmood's emotion detection algorithm then processed this HRV data to identify moments of high emotional arousal, marked by upward and downward spikes in HRV. Researchers pinpointed a remarkable 63% synchrony of HRV upward and downward spikes correlated to specific movie timestamps, indicating a collective emotional response. To facilitate in-depth analysis, detailed emotion data in CSV format was exported from Upmood Insight. This data was then further analyzed by the researchers using Python (Pandas) and SciPy, allowing for sophisticated statistical examination and the creation of "most aroused" and "least aroused" video cuts, which were then compared against random cuts.

Why Upmood? 

Upmood's technology was essential for the success and scientific rigor of this research. The Upmood Band provided the non-intrusive, continuous, and accurate PPG data required to measure Heart Rate Variability (HRV) in real-time during movie viewing, a critical biomarker for emotional arousal. The Upmood emotion detection algorithm was key to translating this raw physiological data into identifiable emotional spikes. Furthermore, the ability to export detailed emotion data in CSV format directly from Upmood Insight was crucial, enabling the professors to conduct advanced statistical analysis using Python and SciPy. This seamless data collection and export capability allowed the researchers to objectively validate their hypotheses and push the boundaries of emotion-driven media analysis.

Key Outcomes and Findings

The integration of Upmood's emotion monitoring technology yielded significant and compelling results:

• Objective Identification of Arousing Segments: The study successfully utilized HRV data to objectively identify the most emotionally arousing segments of movies, providing a scientific basis for content analysis.

  
  

• High Synchrony in Emotional Response: Researchers observed a remarkable 63% synchrony of HRV upward and downward spikes correlated to movie timestamps, indicating a shared emotional experience among participants.

  
  

• Superiority of HRV-based Editing: HRV-based editing generally performed better than control methods in creating impactful movie trailers.

  
  

• Audience Preference for Arousal-Driven Cuts: The bar chart illustrates audience preferences for different video cuts. A chi-square goodness of fit test was performed to examine whether participants preferred the "most aroused" cut over a random cut (2A) or the "least aroused" cut (2B). Both relations were statistically significant. Participants were significantly more likely to prefer the "most aroused" cut over the random cut (χ2(1,N=24)=10.7,p=.001). They were also more likely to prefer the "most aroused" cut over the "least aroused" cut (χ2(1,N=16)=4.00,p=.046). These findings statistically validate the effectiveness of using emotion data to create more engaging media content.

Conclusion

The collaborative research by Psychology Professors at The University of Hong Kong, powered by Upmood's emotion monitoring technology, successfully demonstrated a novel and objective method for identifying emotionally impactful moments in media. By correlating physiological data with audience preferences, this study has opened new avenues for emotion-driven content creation, particularly in film editing and advertising. This project highlights Upmood's immense potential to provide deep, measurable insights into emotional states, enabling creators and researchers to craft more impactful, personalized, and emotionally resonant experiences across various industries.

Thank you!

Our deepest gratitude goes to Professors Tsz Yan So, Man Yi Erica Li, and Hakwan Lau at The University of Hong Kong. Their research and collaborative spirit were instrumental in advancing the science of emotion and media, and their partnership has been truly invaluable to this groundbreaking work.

If you're interested in conducting your emotion-driven research or trying out Upmood products, please send us an email at support@upmood.com.