This chapter stands at the forefront of an innovative intersection between artificial intelligence (AI) and human kinetics, focusing on the transformative realm of skeleton-based movement recognition. At its core, this chapter investigates the sophisticated technologies and methodologies that are pivotal in accurately identifying and analyzing human movements through the lens of skeletal data. This exploration is not just a mere analysis of motion but a deep dive into the intricate dance between the mechanical precision of AI and the fluid complexity of human movement. The chapter meticulously dissects how AI algorithms can interpret skeletal data to recognize and predict human actions, illuminating our physical expressions' nuances. It delves into the myriad of applications this synergy can unlock, from enhancing athletic performance to revolutionizing healthcare and rehabilitation practices. Additionally, the study critically examines the challenges ahead, such as ensuring accuracy in diverse scenarios and addressing ethical concerns related to privacy and data security. By encapsulating the current achievements and envisioning the future landscape, this study contributes significantly to the academic discourse. It paves the way for groundbreaking developments in understanding and augmenting human movement through the power of AI. This interdisciplinary approach promises to redefine our interaction with technology, blurring the lines between the digital and physical realms and unlocking new possibilities in human motion analysis and beyond.
TopIntroduction
The introductory section of the research delves into the fascinating realm of skeleton-based movement recognition. This burgeoning field lies at the intriguing crossroads of artificial intelligence (AI) and human kinetics (Wang et al., 2017). This innovative study area is rapidly gaining prominence due to its vast potential applications across diverse sectors, including sports, healthcare, and entertainment (Tabassum et al., 2021). In sports, the precise analysis of athletes’ movements can lead to enhanced performance, injury prevention, and more effective training methods (Kim et al., 2018). By leveraging AI algorithms, coaches and trainers can obtain detailed insights into athletes’ biomechanics, allowing for more personalized training regimens and real-time performance feedback (Aditya Komperla, 2023). In healthcare, skeleton-based movement recognition is promising in rehabilitation and physical therapy (Qing et al., 2018). It can assist in accurately assessing patients’ motor skills (Angeline et al., 2023), track recovery progress, and even aid in the early detection of movement disorders. AI-driven analysis in this context enhances the accuracy of diagnoses and treatment plans and contributes to developing more interactive and engaging rehabilitation programs (Bala Kuta & Bin Sulaiman, 2023). The entertainment industry, particularly gaming and virtual reality (VR), also reap the benefits of this technology (Guido et al., 2019). Advanced movement recognition systems enable more immersive and interactive experiences, allowing for natural and intuitive user interfaces that respond to the user’s physical movements and gestures (Shafiabadi et al., 2021).
The introduction section underscores the role of AI as a transformative force in this field (Boina, 2022). AI’s ability to process and analyze large volumes of data at an unprecedented speed and accuracy underpins the advancement of skeleton-based movement recognition (Wang et al., 2016). Machine learning models, especially deep learning techniques, have become instrumental in understanding complex movement patterns (Abualkishik & Alwan, 2022). These models can learn from vast datasets of human movements, capturing the nuances and variations that characterize individual motion styles (Yang et al., 2015). This learning capability is critical in developing systems that can recognize, interpret, and even predict human movements with high precision (Rowlands et al., 2016).
The research also touches upon the technical challenges and opportunities in this field. One of the primary challenges lies in accurately capturing and modeling the three-dimensional structure of the human skeleton and its dynamic movements (Boopathy, 2023). This involves the technical aspects of data collection through sensors and cameras and the sophisticated computational models needed to process and interpret this data (Dodvad et al., 2012). Integrating AI with advanced sensing technologies, like motion capture systems, inertial measurement units (IMUs), and even wearable technologies, opens new frontiers for more detailed and accurate movement analysis (Elaiyaraja et al., 2023).
The ethical considerations and implications of AI in human movement recognition are discussed. As with any AI application, privacy, data security, and the potential misuse of sensitive information are paramount (Dodwad et al., 2010). The research emphasizes the importance of developing ethical guidelines and robust security measures to protect individuals’ privacy and ensure the responsible use of AI in this context (Rowlands et al., 2017).
The section further explores the interdisciplinary nature of skeleton-based movement recognition. It is not just a technological endeavor but also involves insights from biomechanics, physiology, psychology, and even sociology to fully understand and interpret human movements (Hasan Talukder et al., 2023). This interdisciplinary approach is vital in creating AI systems that are not only technically proficient but also attuned to the complexities and variabilities of human movement and behavior (Kadhem & Alshamsi, 2023).