Self-Powered Biomimetic Pressure Sensor Based on Mn-Ag Electrochemical Reaction for Monitoring Rehabilitation Training of Athletes.

Self-powered pressure detection using smart wearable devices is the subject of intense research attention, which is intended to address the critical need for prolonged and uninterrupted operations. Current piezoelectric and triboelectric sensors well respond to dynamic stimuli while overlooking static stimuli. This study proposes a dual-response potentiometric pressure sensor that responds to both dynamic and static stimuli. The proposed sensor utilizes interdigital electrodes with MnO2/carbon/polyvinyl alcohol (PVA) as the cathode and conductive silver paste as the anode. The electrolyte layer incorporates a mixed hydrogel of PVA and phosphoric acid. The optimized interdigital electrodes and sandpaper-like microstructured surface of the hydrogel electrolyte contribute to enhanced performance by facilitating an increased contact area between the electrolyte and electrodes. The sensor features an open-circuit voltage of 0.927 V, a short-circuit current of 6 µA, a higher sensitivity of 14 mV/kPa, and outstanding cycling performance (>5000 cycles). It can accurately recognize letter writing and enable capacitor charging and LED lighting. Additionally, a data acquisition and display system employing the proposed sensor, which facilitates the monitoring of athletes' rehabilitation training, and machine learning algorithms that effectively guide rehabilitation actions are presented. This study offers novel solutions for the future development of smart wearable devices.

Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning.

Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.