Measurement Error of Markerless Motion Capture Systems Applied to Tracking Movements in Human–Object Interaction Tasks: A Systematic Review with Best Evidence Synthesis
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
- This review on MMC is of interest to the Technologies community, yet the comprehensiveness of the review can be substantially improved.
- Five electronic databases were searched until May 2025. However, only 38 reference papers are cited in this review which do is considered fairly lower.
- Significant efforts on MMC research and their applications can be found in conferences like CVPR, SIGGRAPH, etc., as well books that should not be ignored.
Comments on the Quality of English Language
English should be polished.
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors
This review introduces the validity of markerless motion capture (MMC) systems used for human movement assessment during tasks that involve physical interaction with objects. Simple tools like Kinect V2 can work for basic moves. This review shows that markerless motion capture can work well for lifting tasks. I recommend the acceptance of this manuscript after some revisions for the improvements of the manuscript.
- Should deformation and tracking challenges caused by non-rigid objects (e.g., ropes, bags) be considered in human-object interaction tasks?
- Are calibration errors and synchronization delays of multi-camera systems (e.g., 8–18 RGB cameras) fully discussed?
- Some recent reports about human-object interaction, such as: Technologies, 2025, DOI: 10.3390/technologies13050169; FlexMat, 2024, DOI: 10.1002/flm2.10; Nano Research, 2025, DOI: 10.26599/nr.2025.94907924. should be also cited to enrich the background and help improve the manuscript.
- Have the authors considered NeRF and Gaussian Splatting? These tools may improve occlusion robustness and may help next-generation markerless systems.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors
Author systematic review the validity of markerless motion capture (MMC) systems used for human movement assessment during tasks that involve physical interaction with objects.
Work is good. Before accepting, some revisions should be are completed as follows.
- Title should be revised as "Review on Markerless motion capture systems applied to tracking movements in human–object interaction tasks".
- In Table 1. PECO framework, PECO should be explained.
- Table 2 includes too many information, it is difficult to read and understand. Table 2 should be simplified by using symbols or curves to represent.
- Measuring movement when interacting with objects can increase the usefulness of MMC for many fields. In ergometrics, previous work evaluated the utility of MMC for lifting and handling tasks in adding in-depth detail to distinguish occupational risks and posture assessment. However, robot hands and their dexterity in grasping and manipulating objects have been a significant research direction in robots, tactile sensing is an essential component in the human-robot interaction and object manipulation.
Therefore, the research works on human-robot finger or hand for capturing object should be mentioned based on following works:
Impedance identification using tactile sensing and its adaptation for an underactuated gripper manipulation, Int. J. Control Autom. Syst. 16 (2) (2018) 875–886.
Development and kinematics/statics analysis of rigid-flexible-soft hybrid finger mechanism with standard force sensor. Robotics and Computer Integrated Manufacturing, 67 (2021) 101978
Research on adaptive grasping with object pose uncertainty by multi-fingered robot hand, Int. J. Adv. Rob. Syst. 15 (2) (2018) Article No. 1729881418766783.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for Authors
The manuscript presents interesting and timely review of markerless motion tracking technology. It is clearly written and follows a well-defined review process diligently. As such, the manuscript delivers useful information and will be of interest to different readers.
I would like to make some comments and recommendations that can increase the usefulness of the manuscript even further.
Maybe I missed it, how much of the reviewed work was on 3D vs. 2D tracking?
After restricting the literature based on various criteria, a very small sample of papers remained. Isn't this fact a form of evidence that the field is still very young and most work is poor quality? Please comment on this?
There is a lot of focus on validity, but in my daily practice I'm really concerned with the accuracy and precision of MMC. The authors need to extract more information about that and emphasize it. For example, I'm worried that a joint angle RMSD of 5-7 degrees is huge. This is enough to make the difference between safe motion and injury. And the peak difference hidden behind this number is probably even larger, it may go into the range of impossible joint angles. Hence, I wouldn't be able to trust MMC in such context. Please discuss the accuracy and precision of MMC at multiple points in the manuscript (abstract, tables, discussion) because that's what the readers want to see.
Please say more about the mechanisms of the different MMC systems. How do they work, how do the underlying principles constrain the potential of these systems for accurate motion tracking?
In this context, what is the history of MMC?
What are your recommendations for future developments and testing?
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
Comments and Suggestions for Authors
Author has complected revision.
