Soft Electronics Enabled Ergonomic Human-Computer Interaction for Swallowing Training

Abstract

We introduce a skin-friendly electronic system that enables human-computer interaction (HCI) for swallowing training in dysphagia rehabilitation. For an ergonomic HCI, we utilize a soft, highly compliant ("skin-like") electrode, which addresses critical issues of an existing rigid and planar electrode combined with a problematic conductive electrolyte and adhesive pad. The skin-like electrode offers a highly conformal, user-comfortable interaction with the skin for long-term wearable, high-fidelity recording of swallowing electromyograms on the chin. Mechanics modeling and experimental quantification captures the ultra-elastic mechanical characteristics of an open mesh microstructured sensor, conjugated with an elastomeric membrane. Systematic in vivo studies investigate the functionality of the soft electronics for HCI-enabled swallowing training, which includes the application of a biofeedback system to detect swallowing behavior. The collection of results demonstrates clinical feasibility of the ergonomic electronics in HCI-driven rehabilitation for patients with swallowing disorders.

Figure 1. Overview of skin-like electronics enabled HCI for swallowing training.

(a) Photo of a game-based HCI (‘jumping’ the ball) for swallowing training using skin-like electrodes. (b) Illustration of targeted submental muscles on the chin and photos capturing the movement of muscles upon swallowing activity. (c) Swallowing EMG signals detected by the skin-mounted electrode in (b).

Figure 2. Computational modeling and experimental quantification of a fractal structured electrode upon mechanical deformation.

(a,b,e,f) Comparison between the FEA results and experimental observations with (a,b) biaxial tensile stretching upto 100% and (e,f) bending upto 180 degrees with the radius of curvature of R = 500 μm. (c,d,g) Quantification of electrical resistance according to the (c,d) applied strains and (g) bending; (c) cyclic mechanical test with repetition of loading and unloading, (d) continuous loading with 10% strain increment to reveal fracture points, and (g) cyclic mechanical bending test. The scale bars in the FEA data indicate the maximum principal strain applied on the electrode.

Figure 3. Fabrciation process of a skin-like electrode and printing on the skin.

(a) Fabricated gold electrode on a Si wafer. (b) Retrieved electrode from the wafer by usig a water-soluble tape. (c) Tranfer printing onto a thin elastomeric membrane by dissolving the tape with water. (d) Printing of the electrode on the target location of the skin by dissolving a supporting sheet of the polyvinylalcohol film. (e) Connection of a ribbon cable for wireless transmission of the recorded EMG signals.

Figure 4. Performance comparison between gel-based rigid and skin-like electrodes.

(a,c) Photos of paired electrodes mounted on the submental muscles; (a) skin-like electrodes mounted by an elastomeric membrane and (c) rigid electrodes mounted by an adhesive pad. (b,d) Swallowing EMG signals (top graph: filtered data and bottom graph: RMS data), recorded by (b) skin-like electrodes and (d) rigid electrodes. (e) Significantly elongated tissue by the applied stress from the rigid electrode, while the soft electrode shows a negligible effect. (f) Comparison plot showing the issue of the rigid electrode with substantial motion artifacts (~6 times higher than the soft electrode).

Figure 5. Data acquisition system and biofeedback interface.

(a) Flow chart describing the entire process from data acquisition to signal processing. (b) Flow chart of the real-time biofeedback interface for a HCI control. A series of parameters including filters and thresholds are adjustable for individual calibration.

Figure 6. Processing flow of swallowing EMG signals.

Three plots show a set of representative swallowing detection; raw EMG data (top), bandpass filtered data (middle), and smoothed RMS data (bottom).

Figure 7. Data classification algorithm.

(a) Flow chart describing a classification algorithm with a three-part threshold technique. (b) Plot of a classification result for swallowing EMG signals obtained from skin-like electrodes.

Figure 8. HCI for swallowing training: biofeedback game.

(a) Screenshot of the biofeedback game, controlled by swallowing EMG signals. Binary data from the main application indicate activity or no activity, which are used in the game to jump the white ball. (b) Real-time display plots showing raw, filtered, and processed signals. The processed signals represent active signal segments according to the thresholds.