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1. The effects of limb position and grasped load on hand gesture classification using electromyography, force myography, and their combination

   https://doi.org/10.1371/journal.pone.0321319  

   Young, Peyton R; Hong, Kihun; Winslow, Eden J; Sagastume, Giancarlo K; Battraw, Marcus A; Whittle, Richard S; Schofield, Jonathon S (April 2025, PLOS ONE)

   Tigrini, Andrea (Ed.)

   Hand gesture classification is crucial for the control of many modern technologies, ranging from virtual and augmented reality systems to assistive mechatronic devices. A prominent control technique employs surface electromyography (EMG) and pattern recognition algorithms to identify specific patterns in muscle electrical activity and translate these to device commands. While being well established in consumer, clinical, and research applications, this technique suffers from misclassification errors caused by limb movements and the weight of manipulated objects, both vital aspects of how we use our hands in daily life. An emerging alternative control technique is force myography (FMG) which uses pattern recognition algorithms to predict hand gestures from the axial forces present at the skin’s surface created by contractions of the underlying muscles. As EMG and FMG capture different physiological signals associated with muscle contraction, we hypothesized that each may offer unique additional information for gesture classification, potentially improving classification accuracy in the presence of limb position and object loading effects. Thus, we tested the effect of limb position and grasped load on 3 different sensing modalities: EMG, FMG, and the fused combination of the two. 27 able-bodied participants performed a grasp and release task with 4 hand gestures at 8 positions and under 5 object weight conditions. We then examined the effects of limb position and grasped load on gesture classification accuracy across each sensing modality. It was found that position and grasped load had statistically significant effects on the classification performance of the 3 sensing modalities and that the combination of EMG and FMG provided the highest classification accuracy of hand gesture, limb position, and grasped load combinations (97.34%) followed by FMG (92.27%) and then EMG (82.84%). This points to the fact that the addition of FMG to traditional EMG control systems offers unique additional data for more effective device control and can help accommodate different limb positions and grasped object loads. 

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2. Carbon Nanotube CMOS Analog Circuitry

   https://doi.org/10.1109/TNANO.2019.2902739  

   Ho, Rebecca; Lau, Christian; Hills, Gage; Shulaker, Max (March 2019, IEEE Transactions on Nanotechnology)

   Carbon nanotube (CNT) field-effect transistors (CNFETs) promise significant energy efficiency benefits versus today's silicon-based FETs. Yet despite this promise, complementary (CMOS) CNFET analog circuitry has never been experimentally demonstrated. Here we show the first reported demonstration of full CNFET CMOS analog circuits. For characterization, we fabricate analog building block circuits: multiple instances of two-stage op-amps. These CNFET CMOS op-amps achieve gain >700 (maximum derivative of output voltage with respect to differential input voltage), operate at a scaled sub- 500 mV supply voltage, achieve high linearity (even when operating at these scaled voltages), and are robust over time (minimal drift over >10,000 cycled measurements over 12 hours). Additionally, we demonstrate a front-end analog sub-system that integrates a CNFET-based breath sensor with an analog sensor interface circuit (transimpedance amplifier followed by a voltage follower to convert resistance change of the chemoresistive CNFET sensor into a buffered output voltage). These experimental demonstrations are the first reports of CNFET CMOS analog functionality that is essential for a future CNT CMOS technology. 

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3. Multi-gesture drag-and-drop decoding in a 2D iBCI control task

   https://doi.org/10.1088/1741-2552/adb180  

   Gusman, Jacob T; Hosman, Tommy; Crawford, Rekha; Singer-Clark, Tyler; Kapitonava, Anastasia; Kelemen, Jessica N; Hahn, Nick; Henderson, Jaimie M; Hochberg, Leigh R; Simeral, John D; et al (April 2025, Journal of Neural Engineering)

   Abstract Objective. Intracortical brain–computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as ‘click-and-hold’ or ‘drag-and-drop’.Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel ‘latch decoder’ that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task.Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control.Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI. 

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4. 3D muscle networks based on vibrational mechanomyography

   https://doi.org/10.1088/1741-2552/ad017c  

   Mancero_Castillo, C Sebastian; Atashzar, S Farokh; Vaidyanathan, Ravi (November 2023, Journal of Neural Engineering)

   Abstract Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers.Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force.Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical. 

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5. Self-Powered Motion Tracking Sensor Integrated with Low-Power CMOS Circuitry

   https://doi.org/10.1109/ISCAS51556.2021.9401440  

   Tasneem, Nishat T.; Biswas, Dipon K.; Mahbub, Ifana; Adhikari, Pashupati R.; Reid, Russell (May 2021, 2021 IEEE International Symposium on Circuits and Systems (ISCAS))

   null (Ed.)

   This paper presents a motion-sensing device with the capability of harvesting energy from low-frequency motion activities that can be utilized for long-term human health monitoring. The energy harvester used in the proposed motion sensor is based on the mechanical modulation of liquid on an insulated electrode, which utilizes a technique referred to as reverse electrowetting-on-dielectric (REWOD). The generated AC signal from the REWOD is rectified to a DC voltage using a Schottky diode-based rectifier and boosted subsequently with the help of a linear charge-pump circuit and a low-dropout regulator (LDO). The constant DC voltage from the LDO (1.8 V) powers the motion-sensing read-out circuitry, which converts the generated charge into a proportional output voltage using a charge amplifier. After amplification of the motion data, a 5-bit SAR-ADC (successive-approximation register ADC) digitizes the signal to be transmitted to a remote receiver. Both the CMOS energy harvester circuit including the rectifier, the charge-pump circuit, the LDO, and the read-out circuit including the charge amplifier, and the ADC is designed in the standard 180 nm CMOS technology. The amplified amplitude goes up to 1.76 V at 10 Hz motion frequency, following linearity with respect to the frequency. The generated DC voltage from the REWOD after the rectifier and the charge-pump is found to be 2.4 V, having the voltage conversion ratio (VCR) as 32.65% at 10 Hz of motion frequency. The power conversion efficiency (PCE) of the rectifier is simulated as high as 68.57% at 10 Hz. The LDO provides the power supply voltage of 1.8 V to the read-out circuit. The energy harvester demonstrates a linear relationship between the frequency of motion and the generated output power, making it suitable as a self-powered wearable motion sensor. 

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