In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca²⁺), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca²⁺(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients’ urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes.

Hall effect sensors have been utilized in several applications that range from industrial and biomedical. It has been deployed alongside magnetic nano-beads into an assay as part of a protein detection platform. However, the hall voltage can be affected by several interfering noise signals in the form of supply hum, dc offsets, temperature variations, circuit induced noises, etc. A possible method that can reduce the effect of these signals is by utilizing an AC excitation line instead of DC. This was demonstrated on this paper as well as the design of a suitable front-end detecting system with a digital output. Such front-end consisted of a chopper stabilized instrumentation amplifier and a bandpass filter based on PMOS pseudoresistors, while the digital back-end consisted of a 12-bit Successive Approximation Register A/D converter. The system was designed using TSMC 0.18 um 1P6M technology.

In this paper, we propose a dual key-parameter readout system for the application of urolithiasis recurrence prevention. Two major urine quality indicators pH and Ca2+ are sensed to assist a patient in assessing the condition of his urinary system. Ion Sensitive Field Effect Transistors (ISFET) and Constant Voltage Constant Current (CVCS) readout circuit are used for pH and Ca2+ monitoring. The prototype with these sensors and readout circuit was integrated and tested in the laboratory. In addition, it was designed and intended to be fabricated in UMC 0.18um CMOS process. The experimental results of the prototype and SPICE simulation readout circuit present a promising performance referred to commercial meters for individual parameter sensing.