Skin-core-fiber-based fabric integrated with pressure sensing and deep learning for posture recognition

The aggravation of subhealth caused by contemporary lifestyles boosts the innovation of sensing systems that can acquire and analyze physiological signals in a real-time, high-efficiency, and even smart way. However, reliable and universal fabrication for these smart sensing units in a bulk or film form remains challenging and thus restricts their widespread applications. Herein, we developed a smart fabric system for accurate real-time sitting posture recognition based on the combination of wet-spun skin-core aerogel fibers, a signal conversion module, and deep learning model. The sensing fibers show a high sensitivity of 23.59 kPa⁻¹, fast response/recovery time of 45/40 ms, and excellent stability over 10,000 cycles, suggesting good pressure-sensing ability. After being assembled them with a signal conversion module, the resultant fabric sensing system could capture physiological signals from human motions and sitting posture in a precise and real-time fashion. Such systems function well even integrated into different parts of clothing and provide long-term wearing comfort. When introducing a deep learning model, 98 % sensing accuracy is demonstrated. This fabric sensing system not only provides reliable solutions to subhealth status monitoring and unhealthy lifestyle correction, but also inspires the mass production of next-generation smart textiles.