**1. Introduction**

For decades, it has been known that the large and widespread anthropometrical diversity limits the effectiveness of a universal approach in human motor-skill learning and training; instead, an individualized biofeedback approach would significantly improve the learning process [1–4]. Recently, wearable sensors (wearables) have garnered great interest in biofeedback training, owing to their tremendous promise for a plethora of applications [5–8]. It seems that individualized biofeedback training has the potential to become an immediate reality in the motor learning realm. However, the absence of a reliable method of applying wearables in biomechanical feedback training has greatly hindered their application in human motor-skill learning and optimization [4].

Although wearables in sports are only a few years old, there has already been a consensus that wearable technology is leading a revolution in physical training [5, 8]. Various sensors are now fitted into sport equipment, limbs, wristbands, and/or clothes to collect crucial data in real time, sending it directly to trainers,

allowing them to implement an individualized training plan for increasing athletic competence. Nevertheless, the use of real-time biomechanical feedback in training looks currently not so optimistic. A recent review paper (2019) divulges that the biomechanical development is still in its infancy [4]. The paper reveals that while there are over 5500 published biofeedback articles in Web of Science, there are very few on real-time biomechanical feedback learning or training. Compared to the booming application of wearables in fitness as well as in health industry, the biomechanical investigations seem disproportionately low. The scarcity of biomechanical studies may due to two facts: (1) a general biomechanical body model that is suitable for wearable application in feedback learning and training is missing, and (2) a reliable method for linking biomechanical quantification and human motor learning in real time is still not available [4].

Clearly, the current success of wearables in sports is not yet linked to the human motor-skill learning. The overwhelming use of wearables in sports is mainly in the area of monitoring physical condition. For example, sports injuries are often caused by fatigue, overtraining, or dehydration [9, 10]. Wearables are now able to collect data related to the risk conditions from athletes' physical conditions, muscle activities, and sweat [5–7]. The real-time biofeedback can help coaches to quickly alternate their training or competition strategies in order to decrease injury risk in training and competition [5, 6, 9]. One should note that the locomotion (e.g., distance, speed), physiological (e.g., heart rate, blood pressure), neurological (e.g., muscle activities), and biochemical feedback (e.g., electrolytes, metabolites) are only useful in analyzing the general physical condition of an athlete; however, they do not provide information related to the limbs' control of human motor skills, and as such, the biomechanical feedback for motor control is still missing.

## **2. The uniqueness and challenges of developing biomechanical feedback**

Why is the development of biomechanical feedback understudied? This is because of the uniqueness of biomechanical feedback. Feedbacks obtained from locomotion, physiological, biochemical, and neurological measurements deliver information of one's general changes in speed/location, physiological and physical response, and muscle tension. The common point of these feedbacks is that they can be conserved across human motor skills, i.e., across different movement forms. Therefore, one can universally apply the feedback devices monitoring these parameters of all activities [4, 11]. On the contrary, biomechanical feedback mainly provides information related to the limb control of motor skills, which often differ from one skill to the other. To complicate matters further, skill optimization has to be adjusted depending on one's anthropometry [4, 12–14]. In short, biomechanical feedback must be tailored to an individual activity being examined [15–18].

Ergo, in order to develop a universal biomechanical feedback device, one has first to obtain a thorough understanding of a variety of motor skills in order to determine the general key parameters for monitoring [1, 4]. Further, biofeedback devices (e.g., wearables) must not interfere with the motor skill being executed. This technical limitation alone has proven to be a major hindrance to the development of biomechanical feedback in motor learning and training. Finally, a vital step in device development is to search ways/body models, which should consider the anthropometry-induced motor-control variations.

**45**

*Challenges and Future of Wearable Technology in Human Motor-Skill Learning and Optimization*

Summarized above, there are several challenges that must be overcome during the development of the universal real-time biomechanical feedback. The obvious

• Creating a new generalizable body model that can quantify various human

• Minimizing wearable interference with the motor skill being executed

• Developing wearable-based data analysis and interpretation method

• Adding the anthropometrical variation into motor-control identification

**3. Biomechanical steps in developing wearables for feedback training**

Effective human motor-skill learning can be supported by useful and timely biomechanical feedback to learners, helping them to target at their performance defects. Previous studies have shown that regular, objective, and consistent performance monitoring and assessment through quantitative analysis of biomechanical variables can reinforce the biomechanical feedback training in practice [17, 19]. Therefore, how to increase the spatial and temporal accuracy when performing a quantification of a motor skill (i.e., the limbs coordination) would play a crucial role in developing wearables for biomechanical feedback training [20]. Considering the uniqueness of biomechanical feedback illustrated in the previous sections, the following steps have to be undergone in developing wearables for feedback training:

diverse human motor skills, (2) sensibleness of wearables' application in training environment, and (3) a general method for wearable-based data analysis and

*DOI: http://dx.doi.org/10.5772/intechopen.91356*

interpretation.

motor skills

• Choose a motor skill.

training other motor skills is impossible.

• Perform motion analysis of the skill quantitatively.

• Identify dominate parameters for feedback training.

• Verify the effectiveness of the selected feedback(s) in practice.

• Develop a feedback device for monitoring of the critical/vital parameter(s) (e.g., coordination among certain segments or joints) for the given motor skill.

One should note that wearables developed through the current approach can only be applied to one specific motor skill. A delimitation of application in learning/

Having seen the success of physiological, neurological, and biochemical wearables in practice, it would be a practitioner's desire that the biomechanical one could also be universally applied to all motor skills for their learning and training in sports and arts. One should note that a general application means that a general methodology should exist for motor-control data collection and interpretation, i.e., a wearable system should be able to track a variety of human motor skills and to identify the motorcontrol patterns existing in these motor skills. Unfortunately, we are currently still far away from the goal. All existing studies are specific or isolated ones. So far, only a few studies explored the real-time biomechanical feedback application in practice [21–23].

are:

In short, there are three indispensable linchpin pieces in the development process: (1) expert knowledge obtained from extensive motion analyses of

*Challenges and Future of Wearable Technology in Human Motor-Skill Learning and Optimization DOI: http://dx.doi.org/10.5772/intechopen.91356*

diverse human motor skills, (2) sensibleness of wearables' application in training environment, and (3) a general method for wearable-based data analysis and interpretation.

Summarized above, there are several challenges that must be overcome during the development of the universal real-time biomechanical feedback. The obvious are:

