Abstract: A system for predicting and detecting urination events of users is disclosed. The system can include any number of wearable devices, mobile devices, hubs, computing devices, and servers to collect, share, process, and interpret data, as well as to provide stimuli to users and caregivers. Biometric and/or environmental data associated with a user can be collected and applied to a urination model to determine a predicted urination time. The user or a caregiver can be provided with direct or environmental stimuli conveying information about predicted urination times. Ongoing biometric and/or environmental data collection can be used to identify, and provide stimuli warning of, imminent urination events. Voluntary and involuntary feedback of actual urination events, as well as continued biometric and/or environmental data collection, can be used to train individual and collective urination models.

Abstract: A bedwetting monitoring method implemented by a computer, bladder monitoring device and a patient alert device and includes determining a urination volume of a bladder of a patient at which a patient will urinate, determining a trigger volume of the bladder of the patient representing a volume that is less than the urination volume, monitoring a volume of the bladder of the patient with a bladder monitoring device, and alerting the patient with the patient alert device at the trigger volume to wake the patient prior to urination.

Abstract: A system for predicting and detecting urination events of users is disclosed. The system can include any number of wearable devices, mobile devices, hubs, computing devices, and servers to collect, share, process, and interpret data, as well as to provide stimuli to users and caregivers. Biometric and/or environmental data associated with a user can be collected and applied to a urination model to determine a predicted urination time. The user or a caregiver can be provided with direct or environmental stimuli conveying information about predicted urination times. Ongoing biometric and/or environmental data collection can be used to identify, and provide stimuli warning of, imminent urination events. Voluntary and involuntary feedback of actual urination events, as well as continued biometric and/or environmental data collection, can be used to train individual and collective urination models.

Abstract: A system for predicting and detecting urination events of users is disclosed. The system can include any number of wearable devices, mobile devices, hubs, computing devices, and servers to collect, share, process, and interpret data, as well as to provide stimuli to users and caregivers. Biometric and/or environmental data associated with a user can be collected and applied to a urination model to determine a predicted urination time. The user or a caregiver can be provided with direct or environmental stimuli conveying information about predicted urination times. Ongoing biometric and/or environmental data collection can be used to identify, and provide stimuli warning of, imminent urination events. Voluntary and involuntary feedback of actual urination events, as well as continued biometric and/or environmental data collection, can be used to train individual and collective urination models.

Abstract: A system for predicting and detecting urination events of users is disclosed. The system can include any number of wearable devices, mobile devices, hubs, computing devices, and servers to collect, share, process, and interpret data, as well as to provide stimuli to users and caregivers. Biometric and/or environmental data associated with a user can be collected and applied to a urination model to determine a predicted urination time. The user or a caregiver can be provided with direct or environmental stimuli conveying information about predicted urination times. Ongoing biometric and/or environmental data collection can be used to identify, and provide stimuli warning of, imminent urination events. Voluntary and involuntary feedback of actual urination events, as well as continued biometric and/or environmental data collection, can be used to train individual and collective urination models.

Abstract: A bedwetting monitoring method implemented by a computer, bladder monitoring device and a patient alert device and includes determining a urination volume of a bladder of a patient at which a patient will urinate, determining a trigger volume of the bladder of the patient representing a volume that is less than the urination volume, monitoring a volume of the bladder of the patient with a bladder monitoring device, and alerting the patient with the patient alert device at the trigger volume to wake the patient prior to urination.