UStEMG: an Ultrasound Transparent Tattoo-based sEMG System for Unobtrusive Parallel Acquisitions of Muscle Electro-mechanics

Abstract

Human machine interfaces follow machine learning approaches to interpret muscles states, mainly from electrical signals. These signals are easy to collect with tiny devices, on tight power budgets, interfaced closely to the human skin. However, natural movement behavior is not only determined by muscle activation, but it depends on an orchestration of several subsystems, including the instantaneous length of muscle fibers, typically inspected by means of ultrasound (US) imaging systems. This work shows for the first time an ultra-lightweight (7 g) electromyography (sEMG) system transparent to ultrasound, which enables the simultaneous acquisition of sEMG and US signals from the same location. The system is based on ultrathin and skin-conformable temporary tattoo electrodes (TTE) made of printed conducting polymer, connected to a tiny, parallel-ultralow power acquisition platform (BioWolf). US phantom images recorded with the TTE had mean axial and lateral resolutions of 0:90±0:02 mm and 1:058±0:005 mm, respectively. The root mean squares for sEMG signals recorded with the US during biceps contractions were at 57±10 mV and mean frequencies were at 92±1 Hz. We show that neither ultrasound images nor electromyographic signals are significantly altered during parallel and synchronized operation. Clinical relevance- Modern prosthetic engineering concepts use interfaces connected to muscles or nerves and employ machine learning models to infer on natural movement behavior of amputated limbs. However, relying only on a single data source (e.g., electromyography) reduces the quality of a fine-grained motor control. To address this limitation, we propose a new and unobtrusive device capable of capturing the electrical and mechanical behavior of muscles in a parallel and synchronized fashion. This device can support the development of new prosthetic control and design concepts, further supporting clinical movement science in the configuration of better simulation models.