An environment control system that controls an environment of a subject is provided. The environment control system includes an actuator configured to control an environment of a subject, and a controller configured to control an operation of the actuator. The environment control system includes an inference unit that includes a first learned model and a second learned model. The first learned model has been trained by associating environmental information indicating an environment of a subject with data correlating with one of sleep, excretion, movement, skin, and stress conditions of the subject. The second learned model has been trained by associating the data correlating with one of the sleep, excretion, movement, skin, and stress conditions of the subject with data correlating with a magnitude of one or more risks that may occur with respect to the subject in a future period of time. The environment control system includes an operating condition determining unit configured to, in a case in which data correlating with the magnitude of the one or more risks that may occur with respect to a subject in a future period of time is inferred based on the first and second learned model, evaluate the inferred data to determine an operating condition of the actuator.

A method of diagnosing the risk that a subject has or has an increased risk of developing overactive bladder syndrome is described. The method includes detecting the levels of choline and/or acetylcholine in a biological sample from the subject, comparing the detected levels to reference values, and characterizing the subject as having an increased risk of having or developing overactive bladder syndrome if the choline and/or acetylcholine values are higher than the reference values. Kits for determining choline and/or acetylcholine levels in a subject, and methods of treating a subject for overactive bladder syndrome are also described.

It is a principal goal of the present invention is to provide a uroflowmetry device for calculating uroflowmetry data (flow rate and other data) associated with urination sessions. The invention is an in-toilet uroflowmetry device, which unlike existing stand-alone and in-toilet devices is not touched by the urine stream, and un-like with existing in-toilet devices, the toilet is useable for all normal functions by men and women.

The invention also provides new data not provided by existing uroflowmetry devices.

The present invention is a device comprised of an electronic open loop belt with video cameras, a single-board computer (SBC), LEDs and various sensors to start the video cameras and control the LEDs. The video data is transferred wirelessly to a website where image processing is performed on the video data, followed by computations of flow rate and additional uroflowmetry data.

An incontinence detection alert system may include a bed configured to receive an occupant and at least one monitor configured to acquire data related to at least one of a status of the bed or a status of the occupant. An incontinence detection system may have circuitry to detect an incontinence event of the occupant. A controller may be configured to receive the data from the at least one monitor and to further receive data related to a time of the incontinence event. A remote device may be configured to receive an alert from the controller before a predicted time of a future incontinence event.

Parametrical analysis of uroflowmetry test results identifies urological disorders so as to distinguish men who have a low urinary tract disorder/benign prostatic hyperplasia from those who have an overactive bladder. Primary urine flow dynamic parameters and secondary urine flow dynamic parameters are calculated. Patient's urological disorders can be assessed by comparing the primary and secondary urine flow dynamic parameters with a library or database of comparable data derived from healthy or normal individuals as well as comparable data derived from individuals afflicted with specific urological disorders. A predictive model of lower urinary tract function disorders can be developed from existing reference primary and secondary urine flow dynamic parameters. The model allows for complex analysis and objective disease prediction.

An oxygen measurement device includes a catheter including a flexible hollow shaft, the flexible shaft having an open port configured to allow urine from a bladder to flow into the open port, and a urinary passage in communication with the open port configured to discharge the urine; and an oxygen sensor including an oxygen sensor main body capable of detecting oxygen in the urine, the oxygen sensor being disposed in the catheter and configured such that the oxygen sensor main body is in contact with the urine flowing in the urinary passage.

An apparatus for measuring the flow rate of urine, including an encasement, configured to encase components of the apparatus; a receptacle bowl attached to the encasement, configured to be placed over a toilet bowl or seat, and direct fluid through a single point of exit to a fluid flow guide; the fluid flow guide, configured to transfer fluid from the receptacle bowl to an impeller; the impeller, configured to rotate along a rotation axis, wherein the impeller includes a plurality of blades, configured to receive the urine from the flow guide, and thereby rotate the impeller at a speed correlating with the flow rate of the urine; and an angular velocity sensor, configured to produce electric signals that correlate with the angular velocity or angular position of the impeller.

Systems and methods are disclosed for diagnosis and treatment of urinary tract symptoms into machine learning based clusters. In some examples, a diagnostic questionnaire is processed by a machine learning model to evaluate a patient's urinary tract health condition and determine a diagnosis based on one or more indications of urinary tract health of the patient. In one example, the machine learning model is trained using datasets labelled according to one or more diagnostic clusters generated by an unsupervised learning model, such as a clustering model. In some examples, a measure of severity of the diagnosis is output by the machine learning model or a second machine learning model.

The present disclosure relates to an intelligent internet of things (IoT) monitoring system, and in particular to techniques (e.g., systems, methods, computer program products storing code or instructions executable by one or more processors) for the implementation of an IoT solution to manage urinary incontinence. Some aspects are directed to the concept of a management platform that allows for end users such as health care providers, caretakers, or medical personnel to manage and monitor one or more subjects through one or more client devices using a network of sensors and IoT devices. Other aspects are directed the concept of a data analysis system configured to train and deploy one or more prediction models for analysis and tracking metrics of health or wellbeing for the one or more subjects.

Over 2 million cystoscopies are performed annually in the United States and Europe for detection and surveillance of bladder cancer. Adequate identification of suspicious lesions is critical to minimizing recurrence and progression rates, however standard cystoscopy misses up to 20% of bladder cancer. Access to adjunct imaging technology may be limited by cost and availability of experienced personnel. Machine learning holds the potential to enhance medical decision-making in cancer detection and imaging. Various embodiments described herein are directed to methods for identifying cancers, tumors, and/or other abnormalities present in a person's bladder. Additional embodiments are directed to machine learning systems to identify cancers, tumors, and/or other abnormalities present in a person's bladder, while additional embodiments will also identify benign or native structures or features in a person's bladder. Further embodiments incorporate such systems into cystoscopy equipment to allow for real time and/or immediate detection of cancers, tumors, and/or other abnormalities present in a person's bladder during a cystoscopy procedure.