Karnasooriya Ragalage Sanjaya Dinuwan Gunawardhana

This work presents a high‐performance, self‐powered triboelectric nanogenerator (TENG) embedded into an origami‐inspired fabric (“fabrigami”) structure for real‐time joint biomechanics monitoring. The device consists of electrospun polyvinylidene fluoride (PVDF) as the electronegative layer and silver‐doped cellulose acetate (Ag‐CA) as the electropositive layer, with enhanced surface charge density and mechanical durability. The fabrigami architecture amplifies contactseparation dynamics, enabling efficient detection of movements during joint motion while preserving conformability and air permeability. The optimized TENG based on 1.5% Ag in CA against PVDF exhibits remarkable electrical output characteristics, including an open‐circuit voltage of 155.9 V, short‐circuit current density of 8.134 mA m ⁻² , and transferred charge density of 65.62 µC m‐, with an instantaneous peak power density of 0.029 W m ⁻² achieved through an 11 MΩ external load resistance. The power conversion efficiency is 4.6–92.8% for 100–5 µm elastic compression of electrospun samples under 10 N, 2 Hz actuation. Sensor stability is observed over 15 000 cycles. The fabrigami knee sleeve includes a Bluetooth‐enabled microcontroller transmitting real‐time motion data wirelessly to measure joint angles and distinguish movement activities. This work demonstrates a novel strategy combining material innovation (Ag‐CA nanofibers) with structural configurability to create a breathable and power‐autonomous smart textile.

Objective. Training plays a significant role in motor imagery (MI), particularly in applications such as Motor Imagery-based Brain–Computer Interface (MIBCI) systems and rehabilitation systems. Previous studies have investigated the intricate relationship between cues and MI signals. However, the medium of presentation still remains an emerging area to be explored, as possible factors to enhance Motor Imagery signals. Approach. We hypothesize that the medium used for cue presentation can significantly influence both performance and training outcomes in MI tasks. To test this hypothesis, we designed and executed an experiment implementing no-feedback MI. Our investigation focused on three distinct cue presentation mediums—audio, screen, and virtual reality(VR) headsets—all of which have potential implications for BCI use in the Activities of Daily Lives. Main results. The results of our study uncovered notable variations in MI signals depending on the medium of cue presentation, where the analysis is based on 3 EEG channels. To substantiate our findings, we employed a comprehensive approach, utilizing various evaluation metrics including Event-Related Synchronization(ERS)/Desynchronization(ERD), Feature Extraction (using Recursive Feature Elimination (RFE)), Machine Learning methodologies (using Ensemble Learning), and participant Questionnaires. All the approaches signify that Motor Imagery signals are enhanced when presented in VR, followed by audio, and lastly screen. Applying a Machine Learning approach across all subjects, the mean cross-validation accuracy (Mean ± Std. Error) was 69.24 ± 3.12, 68.69 ± 3.3 and 66.1 ± 2.59 when for the VR, audio-based, and screen-based instructions respectively. Significance. This multi-faceted exploration provides evidence to inform MI-based BCI design and advocates the incorporation of different mediums into the design of MIBCI systems, experimental setups, and user studies. The influence of the medium used for cue presentation may be applied to develop more effective and inclusive MI applications in the realm of human–computer interaction and rehabilitation.

The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed.

Flexible pressure sensors can be used to predict possible injuries and inform the wearer about their foot posture and landing positions during both walking and running. This work focuses on designing and producing capacitive pressure sensors and integrating them into a smart insole. The suitability of sensors was assessed using a mechanical test rig to measure the change of capacitance under different loads. The effects of introducing micropores into the dielectric layer using two fabrication methods were analysed. The results obtained imply that the use of micropores has the potential to increase the sensitivity and improve the response time of the sensors.

Triboelectric nanogenerators (TENGs) are foreseen as a leading candidate to harvest mechanical energy from ambient sources such as human body movements. However, wearable TENGs, which are used for this purpose, require adequate wearability for long durations, in addition to sufficient electrical outputs. So far, it has been difficult to achieve this through the predominantly plastic-based wearable TENGs constructed using conventional nanogenerator fabrication methods. This Article evaluates the use of textile materials and scalable fabrication techniques to develop TENGs targeting balanced electrical and wearable properties. The fabrication process is conducted using yarn-coating, dip-coating, and screen-printing techniques, which are common textile manufacturing methods, and converted into fabrics using flat-bed knitting, resulting in TENGs with improved wearable and electrical performances. The electrical properties (open circuit voltage (V_oc), short circuit current (I_sc), and short circuit charge (Q_sc)) and wearable properties (air permeability, stretch and recovery, and moisture management) of these structures are evaluated, during which the yarn-coated TENG resulted in maximum electrical outputs recording V_oc ≈ 35 V, I_sc ≈ 60 nA, and Q_sc ≈ 12 nC, under mild excitations. In terms of wearability, the yarn-coated TENG again performed exceptionally during the majority of tests providing the best moisture management, air permeability (101 cm³/cm²/s), and stretch (∼75%), thus proving its suitability for wearable TENG applications.