A Wearable Co-located Neural-Mechanical Signal Sensing Device for Simultaneous Bimodal Muscular Activity Detection

• Published in: IEEE transactions on biomedical engineering Vol. 70; no. 12; pp. 1 - 11
• Main Authors: Wang, Tiantong, Zhao, Yunbiao, Wang, Qining
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; bimodal sensor; Deformation; Electrodes; Exoskeleton; Exoskeleton Device; Exoskeletons; flexible wearable sensor; Force; Gait; Human engineering; Human performance; Humans; iontronic sensor; Manufacturing engineering; Muscle contraction; Muscles; Muscular activity detection; Robot sensing systems; Robotics; Robots; Sensors; Signal to noise ratio; Skin; Substrates; surface electromyography (sEMG); Walking; Wearable Electronic Devices
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2023.3287729

The co-located and concurrent measurement of both muscular neural activity and muscular deformation is considered necessary in many applications, such as medical robotics, assistive exoskeletons and muscle function evaluations. Nevertheless, conventional muscle-related signal perception systems either detect only one of these modalities, or are made with rigid and bulky components that cannot provide conformal and flexible interface. Herein, a flexible, easy-to-fabricate, bimodal muscular activity sensing device, which collects neural and mechanical signal at the same muscle location, is reported. The sensing patch includes a screen-printed sEMG sensor, and a pressure-based muscular deformation sensor (PMD sensor) based on a highly sensitive, co-planar iontronic pressure sensing unit. Both sensors are integrated on a super-thin (25 μm) substrate. The sEMG sensor shows a high signal-to-noise ratio of 37.1 dB, and the PMD sensor sensor exhibits a high sensitivity of 70.9 kPa<inline-formula><tex-math notation="LaTeX">^{-1}</tex-math></inline-formula>. The responses of the sensor to three types of muscle activities (isotonic, isometric, and passive stretching) were analyzed and validated by ultrasound imaging. Bimodal signals during dynamic walking experiments with different level-ground walking speeds were also investigated. The application of the bimodal sensor was verified in gait phase estimation, and results show that the assembly of both modalities significantly reduce (p<0.05) the average estimation error across all subjects and all walking speeds to 3.82%. Demonstrations show the potential of this sensing device for informative evaluation of muscular activities, and its abilities in human-robot interaction.