Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first converted the radio sensing outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body postures tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many future applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface

Strain and field modulation in bilayer graphene band structure

Using an external electric field, one can modulate the bandgap of Bernal stacked bilayer graphene by breaking A-~B symmetry. We analyze strain effects on the bilayer graphene using the extended Huckel theory and find that reduced interlayer distance results in higher bandgap modulation, as expected. Furthermore, above about 2.5 angstrom interlayer distance, the bandgap is direct, follows a convex relation to electric field and saturates to a value determined by the interlayer distance. However, below about 2.5 angstrom, the bandgap is indirect, the trend becomes concave and a threshold electric field is observed, which also depends on the stacking distance.Comment: 3 pages, 5 figures - v1 and v2 are the same, uploaded twice - v3, some typos fixed and a reference adde

Armchair graphene nanoribbons: Electronic structure and electric field modulation

We report electronic structure and electric field modulation calculations in the width direction for armchair graphene nanoribbons (acGNRs) using a semi-empirical extended Huckel theory. Important band structure parameters are computed, e.g. effectives masses, velocities and bandgaps. For the three types of acGNRs, the pz orbital tight-binding parameters are extracted if feasible. Furthermore, the effect of electric field in the width direction on acGNRs dispersion is explored. It is shown that for the two types of semiconducting acGNRs, an external electric field can reduce the bandgap to a few meV with different quantitative behavior.Comment: 5 pages, 5 figure

Nonvolatile memory with molecule-engineered tunneling barriers

We report a novel field-sensitive tunneling barrier by embedding C60 in SiO2 for nonvolatile memory applications. C60 is a better choice than ultra-small nanocrystals due to its monodispersion. Moreover, C60 provides accessible energy levels to prompt resonant tunneling through SiO2 at high fields. However, this process is quenched at low fields due to HOMO-LUMO gap and large charging energy of C60. Furthermore, we demonstrate an improvement of more than an order of magnitude in retention to program/erase time ratio for a metal nanocrystal memory. This shows promise of engineering tunnel dielectrics by integrating molecules in the future hybrid molecular-silicon electronics.Comment: to appear in Applied Physics Letter

An Extended Huckel Theory based Atomistic Model for Graphene Nanoelectronics

An atomistic model based on the spin-restricted extended Huckel theory (EHT) is presented for simulating electronic structure and I-V characteristics of graphene devices. The model is applied to zigzag and armchair graphene nano-ribbons (GNR) with and without hydrogen passivation, as well as for bilayer graphene. Further calculations are presented for electric fields in the nano-ribbon width direction and in the bilayer direction to show electronic structure modification. Finally, the EHT Hamiltonian and NEGF (Nonequilibrium Green's function) formalism are used for a paramagnetic zigzag GNR to show 2e2/h quantum conductance.Comment: 5 pages, 8 figure

Objective dyspnea evaluation on COVID-19 patients learning from exertion-induced dyspnea scores

Objective: Dyspnea is one of the most common symptoms for many pulmonary diseases including COVID-19. Clinical assessment of dyspnea is mainly performed by subjective self-report, which has limited accuracy and is challenging for continuous monitoring. The objective of this research study is to determine if dyspnea progression in COVID patients can be assessed using a non-invasive wearable sensor and if the findings are comparable to a learning model of physiologically induced dyspnea on healthy subjects. Methods: Non-invasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight (~16h) respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea were also performed for blind comparison. The learning model was built from the respiratory features with self report on 32 healthy subjects under exertion and airway blockage. Results: High similarity between dyspnea on COVID patients and physiologically induced dyspnea on healthy subjects was established. COVID patients have consistently high objective dyspnea scores in comparison with normal breathing of healthy subjects. We also exhibited continuous dyspnea scoring capability for 12-16 hours on patients. Conclusion: This paper validates the viability to use our objective dyspnea scoring for clinical dyspnea assessment on COVID patients. Significance: The proposed system can help the identification of dyspneic exacerbation in conditions such as COVID, leading to early intervention and possibly improving their outcome. This approach can be potentially applied to other pulmonary disorders such as asthma, emphysema, and pneumonia