Evaluation of a Remote-Controlled Drone System for Bedridden Patients Using Their Eyes Based on Clinical Experiment

Round 1

Reviewer 1 Report

This paper presents the evaluation of a remote-controlled drone system for bedridden patients, where the eyes motion of the patient is used as the control input to the drone system. This paper is technically correct, whereas the contribution is not clear to the reviewer. The following major revisions are required:

1. The necessity of the current research is not persuasive. Where does the need of remote-controlled drone system come from? For bedridden patients, what are the most desired needs? What is the priority of the remote-controlled drone system for the patients? Some questionnaire surveys or other related statistics results are necessary. 

2. The successful operation of the drone is not an easy work. For the patients with limited experience, how to guarantee the safety of operation, especially when the drone comes close to the local poeple?

3. The patient is able to talk with the local poeple. In this case, how to deal with the noise generated by the drone system? Should the local people shout to the drone?

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

In this paper, authors presented clinical experimental results to verify the effectiveness of this drone system. Extensive experiments verified the effectiveness and efficiency of the proposed method. This paper investigates an interesting problem, and the structure is relative good. However, minor revisions are needed before the acceptance.

1. It seems in the proposed framework in Figure 1, the key components (e.g., Eye-tracking device) are well-studied by others. Therefore, what is the main novel contribution of this work when adapting the well-studied techniques to this work.

2. How different areas in Control Screen impact the performance, authors can make is clearer.

3. For the evaluation, the dataset/algorithm choosing reasons, the detailed platform configurations and the discussion on other untested datasets should be introduced in the revised paper.

4. Please go through the paper carefully and double check whether the right template are used. Correct some typos and formatting issues (e.g., “4.2. Could the Patients Enjoy Vewing the Scenary and Talking with the Experimental staff in the Distant Place?” -> “4.2. Could the Patients Enjoy Vewing the Scenary and Talking with the Experimental Staff in the Distant Place?”?).

5. Some references lack the necessary information (e.g., [6]), please provide all information according to the right template.

6. Make the References more comprehensive, besides this work, some other promising scenarios (e.g., Big data, other IoT systems) can be covered in this work. If the above related work can be discussed, it can strongly improve the research significance. For the improvement, the following papers can be considered to make the references more comprehensive.

Mingxing Duan, Kenli Li, Xiangke Liao, Keqin Li: A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine. IEEE Trans. Neural Networks Learn. Syst. 29(6): 2337-2351 (2018)

Bin Pu, Kenli Li, Shengli Li, Ningbo Zhu: Automatic Fetal Ultrasound Standard Plane Recognition Based on Deep Learning and IIoT. IEEE Trans. Ind. Informatics 17(11): 7771-7780 (2021)

J. Wang, Y. Yang, T. Wang, R. Sherratt, J. Zhang. Big Data Service Architecture: A Survey. Journal of Internet Technology, 2020, 21(2): 393-405

He Li, Kaoru Ota, Mianxiong Dong, Minyi Guo: Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points. IEEE Commun. Mag. 56(5): 124-129 (2018)

J. Zhang, S. Zhong, T. Wang, H.-C. Chao, J. Wang. Blockchain-Based Systems and Applications: A Survey. Journal of Internet Technology, 2020, 21(1): 1-14

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have addressed all my previous concerns. This paper can be accepted now.