Urban farming has gained popularity in recent years, as more people have become interested in locally grown food and reducing their carbon footprint. Smart hydroponic systems can be an important tool for urban farming as they allow for precise control over plant growth and require less space and resources than traditional farming methods. Urban areas often lack access to land suitable for farming, making hydroponic systems a viable option for growing crops in limited space. Readily available hydroponic systems in the market are costly and not cost effective, thus hydroponic systems are usually only installed in larger scale farming. The challenge here is to connect multiple low-cost sensors to microcontrollers and to any store-bought hydroponic set. This paper describes the development of smart Internet of Things (IoT) hydroponic system integrated with an Android mobile application for small scale urban farming. The new set up of IoT hydroponic set, coined as SMART GROW, is used to monitor and control various aspects of the system based on the basic parameters important in growing a healthy plant. The challenges faced during this build were irregular reading of the analog sensor when connected to a single board microcontroller (ESP32). This issue was resolved. SMART GROW currently is capable of monitoring basic parameters such as pH, EC and water level and can cater to additional sensors for monitoring other parameters if required. SMART GROW can easily be replicated and built at home and customized to the needs of the plant's requirement. SMART GROW is versatile as it can be used to grow a wide variety of plants, including herbs, vegetables, and fruits, and offers several benefits over traditional soil-based growing methods such as automated regulation of the water level.
Wearable technology, such as electronic components integrated into clothing or worn as accessories, is becoming increasingly prevalent in fields like healthcare and biomedical monitoring. These devices allow for continuous monitoring of important biomarkers for medical diagnosis, monitoring of physiological health, and evaluation. However, an open-source wearable potentiostat is a relatively new technology that still faces several design limitations such as short battery lifetime, bulky size, heavy weight, and the requirement for a wire for data transmission, which affects comfortability during long periods of measurement. In this work, an open-source wearable potentiostat device named We-VoltamoStat is developed to allow interested parties to use and modify the device for creating new products, research, and teaching purposes. The proposed device includes improved and added features, such as wireless real-time signal monitoring and data collection. It also has an ultra-low power consumption battery estimated to deliver 15 mA during operating mode for 33 h and 20 min and 5 mA during standby mode for 100 h without recharging. Its convenience for wearable applications, tough design, and compact size of 67x54x38 mm make it suitable for wearable applications. Cost-effectiveness is another advantage, with a price less than 120 USD. Validation performance tests indicate that the device has good accuracy, with an R2 value of 0.99 for linear regression of test accuracy on milli-, micro-, and nano-Ampere detection. In the future, it is recommended to improve the design and add more features to the device, including new applications for wearable potentiostats.
Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.