Predicting Team Well-Being through Face Video Analysis with AI

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In this manuscript, the authors proposed a framework for predicting individual happiness in team collaboration based on video data, which is a very interesting and valuable work. However, the following content can be further improved:

(1) The framework proposed in this manuscript are mainly based on regression and classification of features extracted from videos. Well-being itself is difficult to describe clearly. Is there a more effective method for evaluating happiness based on sequence video data？

(2) The data in this manuscript is based on personal survey results, which are too subjective. Is there an objective data annotation method？

(3) The division between the Experiments section and the Results section is not clear enough, and the Experiments section should include the results of the Experiments;

Comments on the Quality of English Language

I am satisfied with the quality of english language.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Could you explain more details about the answers of three questions:

RQ1 : Which features of videos taken in a team setting will be predictive of indi- 40

 

vidual and team well-being measured with PERMA (Positive Emotion, Engagement, 41

 

Relationships, Meaning, and Accomplishments) surveys? 42

 

• RQ2 : How can the relevance of attributes for predicting individual well-being in a 43

 

collaborative work context be measured? 44

 

• RQ3 : How can theories and hypotheses relevant to positive psychology be derived 45

 

from AI-driven team video analysis?

Comments on the Quality of English Language

You written English should be clearly.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript concerns an interesting and current topic related to understanding of non-verbal communication in teamwork based on video data processing. The topic of the article is interdisciplinary because it combines video data processing techniques with the analysis of non-verbal communication, which is important in the social sciences.

The article includes an introduction, formulation of research problems, discussion of the state of the art, presentation of the methods and results of research involving humans, as well as formulation of conclusions and directions for further work.

The approach proposed by the authors seems to be generally correct and practical.
The main contribution of the paper, if I understand correctly, is related to the implementation of research using a variant of the PERMA model and face video analysis. However, the introduction should more broadly and clearly emphasize what is the main contribution of the article.
The authors, however, clearly formulated the research questions they tried to answer in their research.

The authors refer to the current state of the art and technology in the field of work, however, they did it in too little detail, citing several items collectively. In my opinion, the state of the art in section 2. Related work should be described in more detail.

In terms of the discussion of the methods used in the work, it is good that the authors illustrated them in Figure 1, but in my opinion they should be described in more detail in the following subsections.

Similarly, in terms of presenting experimental research, the authors have well illustrated the methodology in Figure 4, but it would be worth describing its individual elements in more detail.

Based on the adopted face video analysis methods and research methodology, the authors presented the obtained quantitative research results, which allowed for an objective assessment of the results and formulation of answers to the research questions.

To sum up, in my opinion, the article is suitable for publication after being expanded in accordance with the above remarks.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

No comment.

Comments on the Quality of English Language

Written English can be updated for good paper contribution.