Next Article in Journal
Critical Review of Size Effects on Microstructure and Mechanical Properties of Solder Joints for Electronic Packaging
Previous Article in Journal
Analysis of Bio-Based Fatty Esters PCM’s Thermal Properties and Investigation of Trends in Relation to Chemical Structures
Previous Article in Special Issue
Classification of Heart Sound Signal Using Multiple Features
Article Menu
Issue 2 (January-2) cover image

Export Article

Open AccessReview
Appl. Sci. 2019, 9(2), 226;

Wearables, Biomechanical Feedback, and Human Motor-Skills’ Learning & Optimization

1,2,3,†,* , 4
Department of Physical Education, Xinzhou Teachers’ University, Xinzhou 034000, China
Biomechanics Lab, Faculty of Arts & Science, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
School of Physical Education, Shaanxi Normal University, Xi’an 710119, China
Department of Mathematics & Computer Science, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
The authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 9 December 2018 / Revised: 31 December 2018 / Accepted: 6 January 2019 / Published: 10 January 2019
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
Full-Text   |   PDF [1013 KB, uploaded 10 January 2019]   |  
  |   Review Reports


Biomechanical feedback is a relevant key to improving sports and arts performance. Yet, the bibliometric keyword analysis on Web of Science publications reveals that, when comparing to other biofeedback applications, the real-time biomechanical feedback application lags far behind in sports and arts practice. While real-time physiological and biochemical biofeedback have seen routine applications, the use of real-time biomechanical feedback in motor learning and training is still rare. On that account, the paper aims to extract the specific research areas, such as three-dimensional (3D) motion capture, anthropometry, biomechanical modeling, sensing technology, and artificial intelligent (AI)/deep learning, which could contribute to the development of the real-time biomechanical feedback system. The review summarizes the past and current state of biomechanical feedback studies in sports and arts performance; and, by integrating the results of the studies with the contemporary wearable technology, proposes a two-chain body model monitoring using six IMUs (inertial measurement unit) with deep learning technology. The framework can serve as a basis for a breakthrough in the development. The review indicates that the vital step in the development is to establish a massive data, which could be obtained by using the synchronized measurement of 3D motion capture and IMUs, and that should cover diverse sports and arts skills. As such, wearables powered by deep learning models trained by the massive and diverse datasets can supply a feasible, reliable, and practical biomechanical feedback for athletic and artistic training. View Full-Text
Keywords: anthropometry; biomechanical modeling; two-chain body model; joints’ coordination; IMUs; deep learning anthropometry; biomechanical modeling; two-chain body model; joints’ coordination; IMUs; deep learning

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Shan, G.; Wang, Y.; Wan, B.; Li, H. Wearables, Biomechanical Feedback, and Human Motor-Skills’ Learning & Optimization. Appl. Sci. 2019, 9, 226.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top