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Open AccessArticlePost Publication Peer ReviewVersion 4, Approved

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines (Version 4, Approved)

Machine Learning Algorithms Team, Glassdoor, Mill Valley, CA 94941, USA
Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA 92866, USA
Department of Electrical Engineering and Computer Sciences, Fowler School of Engineering, Chapman University, Orange, CA 92866, USA
School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
Author to whom correspondence should be addressed.
Received: 30 March 2020 / Accepted: 8 May 2020 / Published: 3 August 2020
(This article belongs to the Section Wearable Biomedical Systems)
Peer review status: 4th round review Read review reports
Version 4, Approved
Published: 3 August 2020
DOI: 10.3390/sci2030062
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Version 3, Revised
Published: 18 July 2020
DOI: 10.3390/sci2030060
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Version 2, Revised
Published: 5 July 2020
DOI: 10.3390/sci2030050
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Version 1, Original
Published: 3 June 2020
DOI: 10.3390/sci2020038
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Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults. View Full-Text
Keywords: locomotion; machine learning; support vector machines; activity classification; activity of daily life (ADL) locomotion; machine learning; support vector machines; activity classification; activity of daily life (ADL)
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Zhang, J.; Soangra, R.; E. Lockhart, T. Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines. Sci 2020, 2, 62.

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Reviewer 1

Sent on 04 Jun 2020 by Rodrigo Martín San Agustín | Approved with revisions
University of Valencia

General comments:

The paper makes a well-structured and interesting analysis of the use of a Wearable Sensor and Support Vector Machines to autodetect Dynamic and Static Activities of elderly individuals. Although the present study is interesting, some aspects must be improved for its final publication.

My main concern is related to the introduction. These details are developed later. On the other hand, I recommend consistently maintaining the order between Dynamic and Static Activities. For the title, dynamics appear first, but in methods, statics are first analyzed. Also, static activity can lead to confusion, since an activity is understood as something dynamic. I suggest reformulating the idea. In addition, English has to be revised, especially the use of the Oxford comma.

Specific comments:


Two main recommendations I suggest to the authors.

First, the second paragraph where it analyzes the literature, considers that it must be reformulated and changes the detected structure "Najafi et al. Used ... Lee et al. Proposed ..." A possible alternative would be: "Numerous classification algorithms exist to provide human motion classification patterns, such as gyroscope data and the wavelet method, a linear discriminant analysis method, ... "A second sentence would be used for the uses of these methods. "... to analyze the" sit-to-stand "transition in relation to fall risk ...".

Second, the information collected on page 3 and beginning of page 4, I believe should be described in methods. The end of the introduction should focus more on the gap and not on the SVM methodology.

Other minor comments are:

Second paragraph. Cvetković et al. not underlined.

Fourth paragraph. Support Vector Machines change for SVMs.

Materials and Methods

Step 1-5. I suggest putting the title of each step in bold.

Response to Reviewer 1

Sent on 20 Sep 2020 by Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Thank you for your comment. These insightful comments have helped us to further improve the manuscript. We have tried our best to improve the manuscript as per your suggestions.

Thank you for this comment. We agree that order should be followed throughout manuscript and we have revised it as per your suggestion. -We did some research on the word “Static Activity” and found several researchers have used this word and thus we planned to keep the word as it is to bring consistency in the definition. Please find some references below. 1. STATIC AND DYNAMIC PHYSICAL ACTIVITY ARE INDEPENDENTLY CARDIOPROTECTIVE THROUGHOUT ADULTHOOD Maia P. Smith Journal of the American College of Cardiology Volume 73, Issue 9 Supplement 1, March 2019 DOI: 10.1016/S0735-1097(19)32736-6 2. Static and Dynamic Activity Detection with Ambient Sensors in Smart Spaces. Sensors (Basel). 2019 Feb; 19(4): 804. Published online 2019 Feb 16. doi: 10.3390/s19040804

Thank you for this comment, we have separated sentences as suggested.

Thank you for your comment. We have corrected it now.

Thank you for your comment. We have corrected it now.

Reviewer 2

Sent on 13 Jun 2020 by Veralia Gabriela Sanchez | Approved with revisions
USN School of Business Department of Business, Marketing and Law Campus Ringerike (A 313)

  • The use of the word “elderly” has a negative connotation in English. Are the authors targeting “older people” or “elderly”? In section 2.1, the participants' age suggest they are in the older people category. However, the authors do not state if they were healthy or not.  Please make sure to use the correct term in the article.
  • The sentence “Finally, Begg et al. used the SVM classifier to analyze the minimum foot clearance owing to aging [17].” The word “finally” suggest that the only research in this area are the few mentioned in this paragraph, which is not the case. Please revise.

  • Sec2.1: The data collection was “approved by the Institutional Review Board at Virginia Tech”. Were there other ethical consideration taken into account? If so, please report them.

  • Section 3.2 says “Firstly, 60 sets of data were split into training data and test data evenly, and then LibSVM with random SVM parameters was used to obtain a classification accuracy of 60%.” It is not clear what the other 40% was used for. Please clarify.
  • English language needs to be revised, there were several minor mistakes.

  • The last paragraph of the discussion “ Conclusions based on this study should be considered in the context of its limitations. First…” should be moved to a new section called “limitations". If possible a section "limitation and strengths" would strengthen this article.

  • References (in main text and reference section) need to be re-formatted and consistent.  Some article titles are italics, while sometimes the journals are italic and the article title is not.

  • The article would strengthen if the authors find more research to cite. SVM and older people activitie recognition is a very common area of research.

Response to Reviewer 2

Sent on 20 Sep 2020 by Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Thank you for your comment. We are sorry, and have corrected it now.

Thank you for your comment. We have revised it now and the word is removed now.

Thank you for your comment. All investigators were CITI certified and followed as per IRB approved protocol to protect the identity of the participants. The details of the IRB are provided in the dissertation document ( ).

Thank you for your comment. We agree the sentence was not clear. We have rephrased it now. It reads 60% accuracy instead of 60% dataset.

Thank you for your comment. We have made limitations section

Thank you for your comment. We have formatte them consistently now.

Thank you for your comment. we have few articles added now.

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