Advanced interdisciplinary approaches for bad posture detection using computer vision and IoT

¹ Department of AIMLE, GRIET, Hyderabad, Telangana, India.
² Department of medical physics, college of medical sciences, Jabir Ibn Hayyan medical university, najaf, Iraq.
³ Department of medical physics, Hilla University College, Babylon, Iraq.
⁴ Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bangalore, Karnataka, India.
⁵ Lovely Professional University, Phagwara, Punjab, India
⁶ Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, India.

^* Corresponding author: sreevani1724@grietcollege.com

Abstract

Addressing the widespread problem of poor posture and its far-reaching health implications, our innovative solution employs advanced interdisciplinary approaches and IoT technology for real-time bad posture detection. By integrating smart sensors and wearables strategically placed to monitor body positioning continuously, our system goes beyond conventional methods. The gathered posture data undergoes analysis by processing units, incorporating advanced algorithms that draw insights from fields like biomechanics and human-computer interaction. This holistic approach not only identifies instances of poor posture with heightened accuracy but also provides immediate feedback to users through visual cues or notifications, fostering self-awareness and encouraging posture correction. The versatility and scalability of our solution make it applicable to diverse settings, including offices, healthcare, and education. This paper delves into the design, implementation, and challenges of our IoT-based system, emphasizing its potential to mitigate health risks linked to prolonged poor posture. By embracing advanced interdisciplinary approaches, we contribute to a more comprehensive understanding of posture-related complexities, paving the way for future advancements in public health through the promotion of better posture habits.