## 5 Significant Gaps in the Current Research
### A Two-Chain Model as a General Full-Body Biomechanical Model for Wearable-Data Collection?  
Anthropometrical studies [38; 86] show that an individual full-body model (equivalent to the generic 15-segment model in 3D motion capture) can alternatively be built indirectly using an anthropometrical approach. Using variables such as body weight, body height, age, gender, and race, one can statistically determine the segments' lengths, segments' masses, segments' COGs (center of gravity), and moment of inertia of body segments. Now, the question is how many wearables are needed to reliably determine the joints angles and limbs coordination? Additionally, the locations of wearables can introduce an artificial stimulus to the neurosensory system while measuring human movement, yielding motion patterns that do not reflect natural patterns of movement. Therefore, both the number and location of wearables are vital for developing real-time biomechanical feedback training.  
Studies on these issues could be considered as no-theory-first type of studies. A common research strategy for developing a general method for a no-theory-first topic would be to begin with empirical inquiries on various cases (i.e., case studies) that investigate the subject within its real-life context [87]. The case studies could supply up-close, in-depth, and detailed aspects of the topic, as well as its related contextual conditions. Using inductive reasoning, research hypotheses could be made from these case studies for future studies. As such, hypothesis-driven studies would be a logical, reasonable, and practical choice for making breakthroughs. It may be worth of trying this research path for the development of real-time biomechanical feedback.  
Based on the current cases of 3D motion analysis studies [60; 70; 74; 75; 88; 89], a variety of complicated human movements (both sports and arts performance) could be considered as a model system with two mechanical chains: upper-body chain and lower-body chain (Figure 2). The base segment of the upper-body chain is upper-trunk & head; two sub-chains (i.e., arms) are linked to the base. Similarly, the lower-body chain consists of a base (i.e., lower-trunk) and two sub-chains (i.e., legs). The proposed generic model of a human body would reduce the number of DOFs (degrees of freedom) in human movement quantification. With this abstraction, human motor-skills could be tracked by using fewer IMUs (inertial measurement units), a sensing technology that measures linear and angular motion usually with a triad of gyroscopes and triad of accelerometers [90; 91; 92]. Previous studies have proven the reliability and concurrent validity of the technology in human gait quantification related to clinical and/or physiotherapy applications and feedback training in rehabilitation [93; 94]. It is time to explore the IMUs' potential in sports and arts applications [95].  
For sports and arts applications, it would be possible, if one could apply three IMUs on each chain (one on the base, one on each distal end of the chain) (Figure 2a), to determine the segments'/joints' motion and coordination as well as the orientation-relationship between the two bases of the two chains. As such, human motor-skills can be quasi-naturally tracked (i.e., wearables minimally encumber human motor control), estimated, and recognized for the real-time biomechanical feedback training. Only, the IMUs' inputs are still not enough for using traditional ways (class mechanics and engineering) to quantitatively determine the model system. Yet, artificial intelligent (AI) could clear the barrier due to its \"learning\" ability [96; 97].