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Soft strain sensors are widely used for human motion detection due to their remarkable flexibility and lightweight nature. Among them, the soft ultrasonic waveguide stands out for its exceptional strain-sensing capabilities, overcoming conventional sensors’ limitations by providing high stretchability, excellent linearity, negligible hysteresis, outstanding repeatability, and long-lasting durability. However, ultrasonic waves deteriorate quickly while traveling through waveguides, making signals potentially undetectable, and different materials’ associated impact on this phenomenon is unknown. We thus investigated the influence of soft waveguide material type on guided wave signal strength (echo peak), repeatability (consistency of echo), and signal-to-noise ratio (SNR). An initial experiment was performed with Ecoflex series and polydimethylsiloxane (PDMS). A second experiment was performed with waveguides composed of 16 soft materials containing microfillers, i.e., Ecoflex 0030 was mixed with either carbon black (CB) or titanium dioxide (TiO2) at eight mass ratios ranging from 0.05% to 6.4%. Results from the first experiment showed that Ecoflex 0030 waveguides exhibited the strongest signal strength (average echo peak: 0.526 V); PDMS waveguides demonstrated the best SNR (2.83 dB); and repeatability was not significantly different across the material types. Results from the second experiment showed that the addition of CB and TiO2 increased echo peak by about 15% at a mass fraction of 0.1%. As the mass fraction of fillers increased, the echo signal strength and SNR both decreased. Experiments suggested that Ecoflex 0030 was an optimal material and thus waveguides composed of Ecoflex 0030 were placed on the wrist, elbow, shoulder, and knee and demonstrated basic feasibility for monitoring real-time continuous movements. Results inform soft material selection and demonstrate feasibility of soft, wearable ultrasonic waveguides for motion detection, potentially improving home rehabilitation, increasing athlete performance, and enhancing virtual reality interactions.

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.