A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors

Maitreyee Wairagkar; Emma Villeneuve; Rachel King; Balazs Janko; Malcolm Burnett; Veena Agarwal; Dorit Kunkel; Ann Ashburn; R. Simon Sherratt; William Holderbaum; William S. Harwin

doi:10.1371/journal.pone.0264126

Peer Review History

Original SubmissionJuly 17, 2021
25 Oct 2021 Decision Letter - Anwar P.P. Abdul Majeed, Editor PONE-D-21-23075A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensorsPLOS ONE Dear Dr. Wairagkar, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Dec 09 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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(Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: In line 240, the authors mention they employed a single neuron ANN model, please clarify if a single hidden layer with single neuron is used. if it is so, please explicitly mention it in the manuscript. What is the learning algorithm used for the ANN model. Reviewer #2: The authors explores the employment of an unsupervised machine learning model in classifying sit-to-stand movements based on kinematics data captured and reconstructed using reduced inertial sensors. The paper is well written, the methodology employed is sound and the results were well discussed. The following are some minor comment(s): 1. Line 192 - what linear classifier was used here? 2. Line 257 - Was a single hidden layer with one hidden neuron used to estimate the thigh angle? You did mention that it was deemed sufficient, how was this conclusion made? Also do report what optimisation algorithm used, e.g. Quasi-Newton, LM? ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". 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Revision 1
22 Dec 2021 Author Response Dear Editor and Reviewers, We would like to thank the reviewers and editor for their comments and feedback on our paper “A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors” submitted to PLOS ONE. We accept and are grateful for all comments, which have resulted in a stronger revised manuscript. A detailed response to all reviewer comments, organised and numbered by reviewer follows. Comments from reviewers are quoted directly in bold, followed with a complete response to each critique. For the convenience of reviewers, we have also quoted new text from manuscript directly relevant to each comment in italics as a part of our responses. All revisions are highlighted in the manuscript text in red. Reviewer #1 (Comments to authors): 1) In line 240, the authors mention they employed a single neuron ANN model, please clarify if a single hidden layer with single neuron is used. if it is so, please explicitly mention it in the manuscript. -- We thank the reviewer for their constructive comments and feedback. We have now clarified that the representation of our model could be generalised to artificial neural network with a single layer with a single neuron with sigmoid activation for regression to estimate thigh movement angular kinematics as follows in the manuscript in line 238 as follows: “To estimate stand-to-sit transition angle �T, we used an adaptable differentiable function with parameters that could be optimised to model the thigh motion kinematics. This function can be generalised and represented as an artificial neural network consisting of a single layer with a single neuron with sigmoid activation function in Eq 8 for regression (see Fig 4).” 2) What is the learning algorithm used for the ANN model. -- We have estimated the angular kinematics of thigh using regression to fit sigmoid curve. The parameters of sigmoid model were estimated by least squares. We have now added the following description in line 244: “In practice, to estimate thigh angular kinematics in this study, we performed regression analysis with sigmoid model described in Eq (8). Our approach can be generalised and represented in the form of a single neuron depicted by the model in Fig 4. The model parameters w and b determined the speed of transition and the centre of the sigmoid curve, the midpoint of the transition segment respectively. Input to the model t is the time window of transition segment. The value of w indicating the speed of transition was estimated, in the range of 0 and 1, by minimising the root mean squared error between the estimated angle and the ground truth reference angle of the thigh using least squares.” Reviewer #2 (Comments to authors): The authors explore the employment of an unsupervised machine learning model in classifying sit-to-stand movements based on kinematics data captured and reconstructed using reduced inertial sensors. The paper is well written, the methodology employed is sound and the results were well discussed. The following are some minor comment(s): -- We thank the reviewer for their encouraging feedback. We have addressed all comments as follows. 1) Line 192 - what linear classifier was used here? -- We simply classified the stationary and transition states based on automatically identified threshold. We have now clarified it in the manuscript in line 191 as follows: “A threshold-based binary linear classification was performed to identify stationary state for feature values below the threshold and transition state for feature values above the threshold.” 2) Line 257 - Was a single hidden layer with one hidden neuron used to estimate the thigh angle? You did mention that it was deemed sufficient, how was this conclusion made? -- Yes, our model can be represented as a single layer ANN with only one neuron to estimate the thigh angle. We have now clarified it in lines 238 and 262 as follows. This model was deemed sufficient for estimating simple sit-to-stand and stand-to-sit transitions based on the results described in Fig 6 comparing ground truth kinematics with estimated kinematics, Fig 7 Bland-Altman plots, and Table 1 root mean squared errors which show that the estimated output was accurate as compared to the ground truth motion capture data and the error between the two was low. Line 238: “To estimate stand-to-sit transition angle �T, we used an adaptable differentiable function with parameters that could be optimised to model the thigh motion kinematics. This function can be generalised and represented as an artificial neural network consisting of a single layer with a single neuron with sigmoid activation function in Eq 8 for regression (see Fig 4).” Line 262: “This model analogous to a single layer with a single neuron was sufficient to estimate thigh angle (results are shown in the next section), however, it can be extended to a more complex artificial neural network with any bounded continuous differentiable activation function to perform regression to estimate the thigh angle.” 3) Also do report what optimisation algorithm used, e.g., Quasi-Newton, LM? -- We have used least squares for regression to fit sigmoid curve which was used to estimate the angular kinematics of thigh. We have now added the following description to clarify this in line 244: “In practice, to estimate thigh angular kinematics in this study, we performed regression analysis with sigmoid model described in Eq (8). Our approach can be generalised and represented in the form of a single neuron depicted by the model in Fig 4. The model parameters w and b determined the speed of transition and the centre of the sigmoid curve, the midpoint of the transition segment respectively. Input to the model t is the time window of transition segment. The value of w indicating the speed of transition was estimated, in the range of 0 and 1, by minimising the root mean squared error between the estimated angle and the ground truth reference angle of the thigh using least squares.” We have addressed all reviewer comments and revised our manuscript based on the reviewers’ feedback and journal style requirements. We thank the reviewers and the editor again and hope the revised manuscript meets their expectations. Thank you, Maitreyee Wairagkar, PhD Department of Mechanical Engineering Imperial College London, London SW7 1AL, UK Attachments Attachment Submitted filename: Response to reviewers-M Wairagkar.pdf https://doi.org/10.1371/journal.pone.0264126.r002
4 Feb 2022 Decision Letter - Anwar P.P. Abdul Majeed, Editor A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors PONE-D-21-23075R1 Dear Dr. Wairagkar, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Anwar P.P. Abdul Majeed Academic Editor PLOS ONE Additional Editor Comments (optional): Nil Reviewers' comments: The author(s) have addressed the queries raised by the reviewers. https://doi.org/10.1371/journal.pone.0264126.r003
Formally Accepted
15 Mar 2022 Acceptance Letter - Anwar P.P. Abdul Majeed, Editor PONE-D-21-23075R1 A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors Dear Dr. Wairagkar: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Anwar P.P. Abdul Majeed Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0264126.r004

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