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[IEEE 2019 14th International Conference on Computer Engineering and Systems (ICCES) - Cairo, Egypt (2019.12.17-2019.12.17)] 2019 14th International Conference on Computer Engineering and Systems (ICCES) - Analog Control Algorithm-Based a Photovoltaic Energy Harvesting System for Low-Power Medical Applications
摘要： One of the attractive solutions used for supplying low-power medical applications is the photovoltaic (PV) energy harvesting system. In this paper, the proposed PV energy harvesting system is composed of a photovoltaic panel, a DC-DC boost converter, a fixed resistive load and an analog control algorithm. This algorithm is designed based on the output load current. The algorithm is implemented using the multisim program. This algorithm is simple, efficient, low cost and low power consumption because it measures only the output current parameter and does not need multipliers. The power consumption of the proposed load is approximately 39.24 mW. Therefore, the expected working duration of the load is 20.9 hours under continuously operation of the light for 4 hours. Finally, the simulation results illustrate the transient characteristics of the proposed PV system.
关键词： Medical wearable sensor nodes,Energy harvesting,Multisim,Photovoltaic,Maximum power point (MPP)
更新于2025-09-23 15:19:57
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[ACM Press the 30th Australian Conference - Melbourne, Australia (2018.12.04-2018.12.07)] Proceedings of the 30th Australian Conference on Computer-Human Interaction - OzCHI '18 - Center of pressure estimation and gait pattern recognition using shoes with photo-reflective sensors
摘要： Gait analysis is an important issue in various fields. In this paper, we developed a shoe-type device to measure the foot pressure when walking. Our device measures the deformation of the sole when pressure is applied and is detected by sensors embedded in the sole. As pressure is not applied directly onto the sensors, the system has better durability and a wider dynamic range. We then proposed a method to estimate the center of pressure (CoP), obtaining an average coefficient of determination of 0.69. Our device also identifies gait patterns by obtaining the discrimination rate of 9 types of walking methods, averaging to an accuracy of 88%.
关键词： Shoe device,Wearable sensor,Gait analysis
更新于2025-09-19 17:15:36
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Wearable-band Type Visible-Near Infrared Optical Biosensor for Non-invasive Blood Glucose Monitoring
摘要： Diabetes is a worldwide-serious problem that can only be delayed or prevented by a regular monitoring of blood glucose (BG) concentration level. Continuous monitoring systems allow subjects to prepare the diabetes management strategy and prevent the long-term complications diseases. Until now, most studies utilize various biofluids such as sweat, tears and saliva that have serious unresolved setback such as expensive material, sensor stability, sensor calibration and long-settling time. Therefore, we developed a novel BG sensor which is cost efficient and highly wearable with a small data acquisition time window that allow a non-invasive, long-term continuous blood glucose monitoring (CGM) system. The novel biosensor exploits a unique information of the pulsatile to continuous components of the arterial blood volume pulsation during the change of blood glucose (BG) concentration at the wrist tissue. The reflected optical signal was measured in the combine visible-near infrared (Vis-NIR) spectroscopy. An in-vivo experiment which enclosed 12 volunteers in a two-hour modified carbohydrate-rich meals reached the average correlation coefficient (????) between the estimated and reference BG concentration of 0.86, with the standard prediction error (SPE) of 6.16 mg/dl. Moreover, the full-day experiment was also conducted to test the reliability of the proposed sensor. Results showed that the created model in the previous day, may estimate a full-day BG concentration which was done in next day with an adequate performance.
关键词： Wearable Sensor,Optical Biosensor,Noninvasive Measurement,Visible-Near Infrared Spectroscopy,Diabetes,Continuous Blood Glucose Monitoring
更新于2025-09-19 17:15:36