The functional components of vibrating mesh nebulizers are a piezoelectric ceramic with a mesh mounted on one side, a reservoir, and a driving circuit. The piezoelectric material vibrates at a specific intrinsic frequency, and when the mechanical resonance frequency of the piezoelectric ceramic and the frequency of the applied electrical signal match, the vibration amplitude of the ceramic is greatest. In the present study, nebulizing performances were tested with respect to driving voltage amplitude after automatic resonance frequency tuning (ARFT) and/or impedance matching (IM) for salbutamol and glycerol solutions. A 1% mismatch of resonance frequency reduced the output rate by 11.0~30.1% and increased particle size by 1.6~7.7% and power consumption increased by 6.6~13.6%. Driving at 30 V_pp after ARFT and IM increased output rate by 45% and decreased power consumption by 31% compared with operation at nominal resonance frequency without IM at 50 V_pp. Nebulization of viscous solutions was also enhanced by applying ARFT with IM. The study shows the application of ARFT with IM improves vibrating mesh nebulizer performance and reduces power consumption.

A combinatorial biofilm treatment involving a low dose of antibiotics along with a small amount of electricity is known as the bioelectric effect (BE). When an external electric field or current is applied, biofilms can be affected by the resulting electrostatic force. Our group is researching the maximization of electrostatic force through the integration of both alternating and direct currents, with a voltage below the electrolysis threshold (0.82 V). To validate the efficacy of this technology, in the present work we investigated two major biofilm applications: (1) dentistry for oral biofilm infection and (2) hygiene for aerobic biofilm contamination. For each application, testing devices were developed in the form of a toothbrush and an evaporator cleaner, respectively. The dental clinical results demonstrated a 75% reduction in gingivitis compared to the non-BE applied group (n = 40, ANOVA, paired t-test, p < 0.05). Meanwhile, the hygiene testing result demonstrated an 81.8% increase in biofilm removal compared to the initial untreated sample (n = 6, ANOVA, paired t-test, p < 0.05). In conclusion, this new BE technology showed efficacy in both dental- and hygiene-associated biofilms without causing electrolysis. Further investigation and development of the BE system should continue in both the medical and hygiene fields.

Effective oral care is a critical requirement to maintain a high quality of life. Most oral diseases are caused by plaque (oral biofilm), which is also correlated with systemic diseases. A common method to remove biofilm is brushing teeth with toothpaste. However, 3.5 billion people in the world have oral diseases, meaning that more efficient methods of removing biofilms are needed. We have developed a toothbrush that applies a bioelectric effect (BE) utilizing an electric force for biofilm removal. It demonstrated significantly higher biofilm removal efficiency than non-BE manual toothbrushes. Tests were performed in saline and toothpaste conditions using various pressures. Results showed that the BE toothbrush had a significantly higher biofilm removal efficiency in saline (0.5 N: 215.43 ± 89.92%, 2.5 N: 116.77 ± 47.02%) and in a toothpaste slurry (0.5 N: 104.96 ± 98.93%, 2.5 N: 96.23 ± 35.16%) than non-BE manual toothbrushes. Results also showed that BE toothbrushes were less dependent on toothpaste. This study suggests that the application of BE can be a new solution to plaque problems in oral care.

Effective oral care is a critical requirement to maintain high quality of life. Most oral diseases are caused by plaque (oral biofilm), which is also correlated with systemic diseases. A common method to remove biofilm is brushing teeth with a toothpaste. However, 3.5 billion people in the world have oral diseases, meaning that more efficient methods of removing biofilms are needed. We have developed a toothbrush applying bioelectric effect (BE) utilizing an electric force for biofilm removal. It demonstrated significantly higher biofilm removal efficiency than non-BE manual toothbrushes. Tests were performed in saline and toothpaste conditions using various pressure. Results showed that the BE toothbrush had a significantly higher biofilm removal efficiency in saline (0.5 N: 215.43 ± 89.92 %, 2.5 N: 116.77 ± 47.02 %) and in a toothpaste slurry (0.5 N: 104.96 ± 98.93 %, 2.5 N: 96.23 ± 35.16 %) than non-BE manual toothbrushes. Results also showed that BE toothbrushes were less dependent on toothpaste. This study suggests that the application of BE can be a new solution to plaque problems in oral care.

Falls are a major cause of severe injury and mortality among older adults, requiring rapid state recognition and alerts in healthcare and caregiving environments. However, many existing fall-recognition approaches primarily focus on post-fall detection or assume access to cloud/GPU computing, leaving limited evidence for short-term pre-fall recognition on low-end CPU-based edge devices. This study proposes a lightweight CNN–TCN–Attention framework for recognizing fall and pre-fall states on resource-constrained edge devices. Using MediaPipe, three-dimensional coordinates and visibility scores of 33 human body landmarks are extracted from input videos. Raw RGB frames are not directly used by the classifier, thereby reducing the processing of visually identifiable information. A total of 2300 fall-related videos from the AI-Hub dataset were reorganized into three classes: normal, pre-fall, and fall. The pre-fall onset was defined as the point at which the downward vertical velocity of the nose landmark exceeded a dataset-specific threshold. The model uses a CNN to extract frame-level skeletal patterns, a temporal convolutional network (TCN) to learn temporal changes in posture, and an attention module to aggregate temporal features. It was deployed on a Raspberry Pi Zero 2 W using ONNX Runtime. The proposed model achieved 94.71% accuracy and a macro F1-score of 93.63%, with an average state-decision latency of 325.88 ms/decision. It improved accuracy by 2.83 percentage points over the lightweight CNN–LSTM–Attention baseline while maintaining comparable latency and achieved approximately 1.75× faster processing than the MobileNetV3–TCN–Attention model.

Efficient microcontroller execution does not ensure reliable electrocardiogram classification across patients and databases. We conducted a controlled benchmark of three convolutional architectures under a corrected subject-disjoint MIT-BIH split, external INCART testing, Noise Stress Test Database artifacts, 16-bit floating-point (FP16) and full-integer 8-bit (INT8) conversion, and classifier-only invocation on an ESP32 without external RAM. Five fixed seeds were used. On the MIT-BIH test cohort (DS2), INT8 four-class macro-F1 was 0.474-0.498 and supraventricular (S) recall was 0.150-0.180. Mean S-class area under the receiver operating characteristic curve (AUROC) was below 0.5 in all six architecture-regime groups, although all corresponding record-cluster intervals included 0.5. Within-record ranking could remain high despite poor pooled ranking. On INCART, S-class ranking was stronger, with all six patient-cluster AUROC intervals above 0.5, but the representative mixed-noise depthwise-separable network (DS-CNN) achieved 0.797 recall with 0.074 precision. Including record 201, which shares a subject with test record 202, increased DS2 S-recall point estimates by 2.49-14.68 percentage points. In the primary architecture panel, FP16 and INT8 met the engineering tolerances in all 120 comparisons. DS-CNN required 9.49 ms per classifier invocation on the ESP32 using preprocessed, annotation-centered inputs. Acquisition, QRS detection, preprocessing, current, and energy were not measured. Patient-dependent S-score behavior and external false-positive predictions limited this panel more than numerical precision or microcontroller resources.