Core Viewpoint - The article discusses the development of a flexible multichannel muscle impedance sensor (FMEIS) by a research team from Nanyang Technological University, which addresses the limitations of traditional muscle monitoring tools and enhances human-machine interaction capabilities [2][4][24]. Group 1: FMEIS Development and Features - FMEIS is a flexible sensor with a thickness of only 220 μm and an elastic modulus of 212.8 kPa, closely matching human skin's elasticity [4][6]. - The sensor demonstrates high performance, achieving an accuracy of 98.49% in gesture classification and a determination coefficient (R²) of 0.98 in muscle strength prediction [4][10]. - Unlike traditional electromyography (EMG), FMEIS can detect impedance changes in deep muscle tissues, allowing for accurate readings even without significant body movements [4][10][17]. Group 2: Technical Specifications - The FMEIS system consists of a lightweight 4g sensor pad and a 53g control unit [6]. - The sensor pad utilizes a safe alternating current of 50 kHz and 0.4 mA for multi-channel signal injection and collection, ensuring stability during extensive movements [7]. - The design incorporates a modified polydimethylsiloxane substrate and conductive hydrogel electrodes, enhancing adhesion and signal quality over prolonged use [7][24]. Group 3: Performance Validation - FMEIS outperformed traditional EMG sensors in detecting both active and passive muscle movements, with a maximum detection depth of approximately 30 mm [17][24]. - In tests involving three participants, FMEIS achieved an average gesture classification accuracy of 98.49% and an average R² value of 0.98 for muscle strength regression, indicating strong robustness against variations in skin impedance and fat tissue thickness [16][24]. Group 4: Application Scenarios - FMEIS has shown potential in various applications, including human-robot collaboration, exoskeleton control, and virtual surgery [18][24]. - In human-robot collaboration, FMEIS enables natural interaction by interpreting muscle signals to drive robotic actions without visible hand movements, enhancing efficiency and safety [19][24]. - For exoskeleton control, FMEIS demonstrated a response delay of only 756 milliseconds, significantly improving grip strength by 65% during tests [21][24]. - In virtual surgery, FMEIS serves as a bridge between the operator and VR systems, allowing for precise feedback and control of surgical tools based on muscle force predictions [23][24].