A Comprehensive Approach to Intelligent Garment for Posture Correction: A Preliminary Study for Integrating Aesthetics and Sensor Interaction

This study investigates design and evaluation of an intelligent vest integrating Marginal Absolute Relative Gradient (MARG) sensors for posture correction. The research aims to enhance ergonomic interventions through a lightweight, aesthetic garment that provides real-time feedback, addressing both functional and design in wearable technology. The vest incorporates Adafruit FLORA microcontroller boards and MARG sensors to monitor neck and shoulder movements. Experimental setups involved recording roll and pitch angles during various postures. A sample of 10 female participants was recruited for usability testing, and quantitative analyses of sensor data were performed to validate the vest's performance. Moreover, user feedback on the vest's visual (LED) and tactile (vibration) feedback, comfort, and posture correction effectiveness was collected. Data revealed distinct posture patterns, including a mean neck pitch of −61.80° during forward tilts and synchronized neck-shoulder movements when leaning right (correlation coefficient of 0.46 for pitch). Conversely, independent movements were observed in leftward tilts (correlation coefficients of 0.07 for roll and −0.18 for pitch). User feedback indicated high ratings for comfort (mean = 4.4) and LED feedback effectiveness (mean = 4.3), while haptic feedback received moderate scores (mean = 3.6). The intelligent vest successfully integrates posture correction functionality with user-friendly design, providing detailed real-time feedback and maintaining wearability. This study highlights its potential in health-related applications and underscores the importance of aesthetics in wearable technology for enhanced adoption and usability.