Advances in Wearable Technologies

**43**

**Chapter 4**

*Gongbing Shan*

feedback training, AI, IMUs

motor-skill learning and optimization [4].

**1. Introduction**

**Abstract**

Challenges and Future of Wearable

Technology in Human Motor-Skill

Learning how to move is a challenging task. Even the most basic motor skill of walking requires years to develop and can quickly deteriorate due to aging and sedentary lifestyles. More specialized skills such as ballet and acrobatic kicks in soccer require "talent" and years of extensive practice to fully master. These practices can easily cause injuries if conducted improperly. 3D motion capture technologies are currently the best way to acquire human motor skill in biomechanical feedback training. Owing to their tremendous promise for a plethora of applications, wearable technologies have garnered great interest in biofeedback training. Using wearable technology, some physical activity parameters can be tracked in real time and a noninvasive way to indicate the physical progress of a trainee. Yet, the application of biomechanical wearables in human motor-skill learning, training, and optimization is still in its infant phase due to the absence of a reliable method. This chapter elaborates challenges faced by developing wearable biomechanical feedback devices and forecasts potential breakthroughs in this area. The overarching goal is to foster

interdisciplinary studies on wearable technology to improve how we move.

**Keywords:** biomechanics, 3D motion capture technology, body model, real time,

For decades, it has been known that the large and widespread anthropometrical

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,

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

Learning and Optimization
