We present a novel design for an e-textile based surface electromyography (sEMG) suit that incorporates stretchable conductive textiles as electrodes and interconnects within an athletic compression garment. The fabrication and assembly approach is a facile combination of laser cutting and heat-press lamination that provides for rapid prototyping of designs in a typical research environment without need for any specialized textile or garment manufacturing equipment. The materials used are robust to wear, resilient to the high strains encountered in clothing, and can be machine laundered. The suit produces sEMG signal quality comparable to conventional adhesive electrodes, but with improved comfort, longevity, and reusability. The embedded electronics provide signal conditioning, amplification, digitization, and processing power to convert the raw EMG signals to a level-of-effort estimation for flexion and extension of the elbow and knee joints. The approach we detail herein is also expected to be extensible to a variety of other electrophysiological sensors.

Freezing of gait (FOG) is a disabling yet poorly understood paroxysmal gait disorder affecting the vast majority of patients with Parkinson’s disease (PD) as they reach advanced stages of the disorder. Falling is one of the most disabling consequences of a FOG episode; it often results in injury and a future fear of falling, leading to diminished social engagement, a reduction in general fitness, loss of independence, and degradation of overall quality of life. Currently, there is no robust or reliable treatment against FOG in PD. In the absence of reliable and effective treatment for Parkinson’s disease, alleviating the consequences of FOG represents an unmet clinical need, with the first step being reliable FOG prediction. Current methods for FOG prediction and prevention cannot provide real-time readouts and are not sensitive enough to detect changes in walking patterns or balance. To fill this gap, we developed an sEMG system consisting of a soft, wearable garment (pair of shorts and two calf sleeves) embedded with screen-printed electrodes and stretchable traces capable of picking up and recording the electromyography activities from lower limb muscles. Here, we report on the testing of these garments in healthy individuals and in patients with PD FOG. The preliminary testing produced an initial time-to-onset commencement that persisted > 3 s across all patients, resulting in a nearly 3-fold drop in sEMG activity. We believe that these initial studies serve as a solid foundation for further development of smart digital textiles with integrated bio and chemical sensors that will provide AI-enabled, medically oriented data.