Fred Bergman Healthcare Pty Ltd

Hadi Mashin-Chi

Mehdi Azimi

Peter Curran

Peter Hubertus Aigner

Juuso Olkkonen

David Albert Barda

Anna-Marja Aura

Maria Smolander

Anu Vaari

Jian Huang

Antti Nyyssola

Anushiya Arunachalam Kannan

Lisa Hakola

Mohammed Asef Ali

Peter Aigner

Antti NYYSSÖLÄ

The invention provides the present invention provides a faecal detection sensor for an absorbent article. The sensor includes a faeces-sensitive material that reacts to the presence of a constituent of faecal matter, wherein the sensor exhibits an electrical property that changes following the reaction of the faeces-sensitive material. In embodiments of the invention the faeces sensitive material is a material that reacts due to the presence of a sulfur-containing compound in faecal matter, such as metallic faeces-sensitive materials, or is a material that reacts with a faecal enzyme and/or other constituents of faecal matter, such as organic faeces-sensitive materials.

Faecal detection sensor

A method and an incontinence monitoring system for estimating a volume or a volume range of a bodily output in a combination absorbent article and wetness sensor. The system comprises a combination of an absorbent article and a wetness sensor, and a processor adapted to receive an input representative of an electrical variable of the wetness sensor indicative of the occurrence of a bodily output in the absorbent article and to process the input including: characterising the input to generate a vector that is representative of the bodily output, applying one or more functions to the vector to generate one or more possible volumes of the bodily output, generating a distribution of the possible volumes, and determining a volume or a volume range for the bodily output based on the distribution of the possible volumes.

Method and system for volume estimation of bodily outputs in absorbent articles

The present invention relates to systems, methods and devices for determining one or more pre-existing characteristics, pre-existing states or both of a combination absorbent article and electrical circuit after connecting an electronic device to the combination absorbent article and electrical circuit. The method and system is particularly, although not exclusively, suitable for determining pre-existing characteristics such as absorbent article size, type, capacity and a pre-existing state such as whether or not the electrical circuit of the combination absorbent article and electrical circuit has been previously connected to the electronic device or another like electronic device. These pre-existing characteristics, pre-existing states or both are derived from measured electrical variables of the electrical circuit which may include one or more components, such as a resistor or a capacitor or a variable such as charge on a capacitor after the connection of an electronic device, which may include a transmitter or a transceiver. The method includes: measuring an electrical variable after connection of an electronic device to the combination article and electrical circuit to generate data indicative of the electrical variable at sequential time values; and processing the data with a processor including applying an If-Then rule based function comprising two or more conditions to determine a pre-existing characteristic, a pre-existing state or both of the combination absorbent article and electrical circuit.

System and method for determining a pre-existing characteristic and pre-existing state of an article

A method for analyzing incoming data, comprising the steps of processing the incoming data in segments to output a sequence of segment types by extracting one or more properties of an incoming data segment and forming an Unknown Property Vector for each segment of data in the incoming data, and processing the sequence of segment types to identify events in the incoming data. The sequence of segment types is determined, for each segment, by analyzing the Unknown Property Vector by reference to one or more collections of vectors obtained from a set of Reference Property Vectors. This may each of the one or more collections of vectors being selected from the set of Reference Property Vectors randomly or based on relevance or clustering.

Detecting fecal and urine events by reference to collections of data

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North Bondi, Australia

Hadi Mashin-Chi