## 5 Significant Gaps in the Current Research
### AI for Motor-Control Quantification  
The AI systems are performance driven--one focus is on the predictive accuracy, based on known characteristics learned from the previous data/training samples [98; 99]. In the past decades, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data (big data), and theoretical understanding. AI techniques have become a powerful tool for helping to solve many challenging problems in human motor-skills' evaluations and analyses [96; 97; 100; 101; 102].  
The idea of AI prediction is to find a way to learn general features in order to make sense of new data [98; 99]. This description highlights the central role of data for establishing implicit knowledge. The amount of data must be sufficiently large to provide many training examples from which a large set of parameters can be extracted.  
Figure 2: The two-chain model of human motor-skills. **(a)** The possible locations of the six wearables for human motor-skills’ tracking; **(b)** A ballet skill; **(c)** A Indian dance skill; **(d)** Baseball pitch; and, **(e)** Bicycle kick in soccer (the three-dimensional (3D) motion data was generated in [PERSON]’s Biomechanics Lab).  
Among the AI technologies, deep learning is considered as a powerful tool that percolates through to all application areas of machine learning, such as image identification, speech recognition, natural language processing, and, indeed, biofeedback support [103; 104; 105]. The success of deep learning networks encourages their implementation in further applications for the enhancement of human physical activities [106; 107]. Most recently (September 2018), Nature Neuroscience has published the latest developments in the area of markerless, video-based motion tracking, indicating that the power of deep learning will enable motion tracking to human-like accuracy [108]. This study confirms that motion capture/quantification of limbs' coordination will move from an expensive and difficult task restricted to the laboratory to an effortless daily routine for researchers and practitioners.  
From motor learning point of view, wearables would have much higher potential than video shooting in the future practice. This is not only because of the fast advance in miniature of wearables, but also due to two inherited drawbacks of video-shooting approach. Reliable biomechanical feedback should obtain from accurate quantification of human movement in field, with some requiring large space. Even with a multiple-camera setting, unexpected environmental factors (e.g., interactions among athletes) will create data-gap. Further, it is true that we are already sitting on massive movement data (e.g., YouTube, Flickr) for training of deep learning models; but the video datasets are un-calibrated and have very little information on the hardware and conditions used to capture particular videos, which can bias the deep learning recognition algorithms [109]. Currently, the availability of reliable motion capture data for developing deep learning models is significantly limited.  
Summarized from Sections 6.1 and 6.2, the combination of the two-chain full-body model with six wearable IMUs and the deep learning prediction based on IMUs' data shows great potential in developing real-time biomechanical feedback training for an efficient human motor-skill learning and optimization. The missing piece for testing the potential is reliable massive training data.