This paper presents an initial investigation of the pulsed transmission strategy in optical wireless power transfer (OWPT) for medical implant devices (MIDs) case, aiming for deeper penetration across biological tissue. The main idea is to transmit high-power pulses for very short time periods, ensuring safe exposure through rapid switching periods while still attaining a reasonable average of harvested energy. This study considers an optical phantom to mimic human soft tissue. An 810 nm 375 mW NIR LED and a commercial indoor PV cell were used as transmitters and receivers in the OWPT testbeds, respectively. We compared the required time to charge the supercapacitor fully (storing approximately 5 J) and the energy harvested rate between the pulsed and continuous transmission methods over different levels of LED driving currents. Approximately 1.95 mJ/s in similar to 18 minutes of energy harvesting rate using 375 mW of transmitted power can be attained using pulsed mode (at 100 kHz). In contrast, similar to 4.41 mJ/s is attained in similar to 42 minutes using the same transmitted power at continuous mode. Results reveal that transmitting high illumination power across biological tissue in pulsed mode using readily available testbeds is feasible, albeit with a trade-off in terms of the supercapacitor's charging duration and the energy harvesting rate.
This paper focuses on an apnea/hypopnea as one of the major symptoms of Sleep Apnea Syndrome (SAS) and proposes its detection method based on knowledge distillation by reducing the gap between the contact and non-contact sensors. For this issue, the proposed method leverages the teacher model of the neural network trained by data of the high-quality contact sensors (e.g., the nasal pressure sensor or thoracic/abdominal belt sensor) when training the student model of the neural network with data of the low-quality non-contact sensors (e.g. the mattress sensor). To improve an effect of the above knowledge distillation, the following two data preprocessing methods are proposed: (i) the respiration amplitude normalization to suppress different amplitude caused by the different sleep positions; and (ii) the phase shift adjustment of the different respiration amplitude measured by the different sensors. To investigate the effectiveness of the proposed data preprocessing, this paper conducted the human subject experiment of the 35 subjects and revealed the following implications: (i) while the knowledge distillation without reducing the gap of the different sensors deteriorated the estimation, the knowledge distillation with the respiration amplitude normalization significantly improved the accuracy, precision, recall, and F1; (ii) an integration of the respiration amplitude normalization and its phase shift adjustment further and significantly improved the recall, which contributes not to overlooking the apnea/hypopnea.
This application-oriented study explores the potential of wireless power transfer (WPT) across biological tissues using commercial single-beam broadband near-infrared (NIR) LEDs, which is demonstrated on a testbed. We employ commercial indoor monocrystalline silicon photovoltaic (PV) cells as receivers in the testbed. Typically, PV cells are optimized for broad-spectrum light, such as sunlight or artificial light sources, which can cause inefficiencies when exposed to narrow-spectrum light like NIR due to spectral mismatches. To this end, a wide-spectrum (broadband) NIR LED is envisioned to enhance power conversion efficiency (PCE). To simulate biological tissues, tissue-mimicking optical phantoms were used. This preliminary study focuses on evaluating the open-circuit voltage (V-oc) of a commercial PV cell across various optical phantom thicknesses under different illumination levels of NIR light. The NIR LED used in this study emits in the wavelength range of 770-940 nm. The results show that PV cells can produce voltage from broadband NIR light penetrating optical phantoms up to 50 mm thick. This research emphasizes the potential of WPT using broadband NIR light for powering implantable electronic devices (IEDs).
Delirium is a common and serious condition among hospitalized patients, particularly in older adults, with significant clinical, social, and economic implications. Preventive measures targeting modifiable risk factors, such as cognitive stimulation and reorientation, have shown promise in reducing delirium incidence. This study explores the integration of manualized cognitive interventions delivered by volunteer medical students with continuous monitoring of motor activity using wearable sensors. The goal is to detect early signs of delirium and enhance prevention efforts. The project outlines a structured protocol focusing on hospitalized patients at risk of delirium in critical and non-critical care settings. Volunteer medical students serve as a bridge between healthcare professionals and patients, delivering daily cognitive interventions under supervision. Simultaneously, wearable sensors monitor patients’ motor activity, providing real-time data to identify deviations indicative of potential delirium onset.
Today, the early detection and prognostic assessment of primary melanoma of the skin are still significant challenges, as the disease remains a leading cause of mortality worldwide. Cutaneous melanoma prognosis is strictly related to accurate pathological Staging which includes primarily the measurement of Breslow tumor thickness, recorded to the nearest 0.1 mm, and tumor ulceration. Automatic classification of melanoma based on its Breslow thickness could improve patients stratification into clinically relevant groups. In this work, we present a custom Artificial Neural Network that classifies the severity stages by extracting asymmetry, border, diameter, color and textural features from melanoma dermoscopic images. Our model showed very high performance on both internal testing (91.41%) and external testing on a disjoint real-world image dataset (82.45%). Although future work should involve expanding the training and testing datasets, our model exhibited strong predictive and generalization capabilities.
