Core Viewpoint - The article discusses the emergence of "soft human-machine interfaces" based on flexible electronic technology, which aims to enhance the interaction between humans and machines through intuitive and natural means. The technology faces challenges such as accurately interpreting physiological signals and achieving cost-effective, scalable manufacturing [1][2]. Group 1: Flexible Electronic Technology - A research team from Shanghai University of Science and Technology has developed a printed human-machine interface that includes electronic skin for surface electromyography (sEMG) collection and feedback, multimodal tactile sensing soft robots, and machine learning algorithms for gesture classification and material recognition [2]. - The core breakthrough of this technology is the electronic skin (e-skin), a thin electronic sensing device that can be attached to the human body to monitor various physiological signals in real-time [5]. Group 2: Production Techniques - The research team utilized an efficient integrated printing technology, including direct ink writing (DIW), infrared laser engraving, and laser cutting, to achieve large-scale production of multi-material, high-density sensor arrays [7]. - Various functional inks, such as silver ink, carbon ink, and PDMS/C, were used to print electrical circuits as narrow as 40 micrometers on flexible substrates [7]. Group 3: Intelligent Algorithms - The challenge of enabling machines to accurately understand human intentions is addressed through an adaptive machine learning method that combines linear mapping networks (LMN) and initial time models (ITM) [11]. - The LMN adjusts the weights of signals from different channels to adapt signals from various users to a unified standard distribution, while the ITM captures local features in time series with low latency and high accuracy [12]. Group 4: Multimodal Sensing - The soft human-machine interface integrates a "sensory system" for robots, allowing them to recognize object characteristics through touch by incorporating temperature, pressure, thermal conductivity, and electrical conductivity sensors [14]. - The pressure sensor features a capacitive design with a sensitivity of 10.5 pF/kPa, maintaining stable performance over 2000 tests, while the combination of thermal and electrical conductivity sensors improved material recognition accuracy from 63.99% to 98.03% [16]. Group 5: Application Prospects - This technology has broad application prospects, establishing a complete interactive ecosystem that includes signal collection, intention recognition, action execution, and sensory feedback, forming a closed-loop human-machine interaction cycle [18]. - In the medical field, it offers new hope for upper limb amputees, achieving an average accuracy of 94.36% in recognizing 11 hand and finger gestures, even with significant time delays and reduced signal strength [18].