Embodiments disclosed herein are directed to a self-learning bladder volume monitoring system. The system can include a bladder volume (BV) system configured to measure an electrical impedance of a bladder region of a patient and determine a volume of fluid disposed therein using an impedance to bladder volume model (By model). Further, the system can measure a total body water (TBW) for the patient and modify the BV model to account for variations in TBW within the tissues surrounding the bladder providing a more accurate bladder volume measurement. The system can include a training unit which can include one of a user input interface, an automatic urine output monitoring system, an ultrasound system, and an intrabladder pressure system configured to verify a volume of fluid within or voided from the bladder and train the BV model.

Apparatus and methods are described, including one or more sensors configured to sense a flow of urine from a subject's body, without contacting the subject's body, during a urination of the subject as the subject pauses the urination. A processor computes one or more parameters of the pause based on the sensing, and outputs a score based on the parameters. Other applications are also described.