This organization is seeking wearable and/or compact sensor technologies that can be used to improve the well-being of people living with stomas and/or urinary retention.
Having an ostomy and/or urinary retention can have significant impact on quality of life. Risk of infection and/or compromised skin is high and other factors such as uptake of nutrients may be compromised. Discreet sensors capable of monitoring and alerting to these conditions would provide significant benefit for people living with these conditions. Additionally, urine and feces can be used to diagnose many different conditions either related to a general health condition or conditions specific for people living with a stoma and catheter users. Sensors that can monitor these indicators within feces or urine on a regular basis in a homecare setting would also be beneficial.
All sensors should be:
- Clinically validated and relevant to benefiting people with either an ostomy or urinary retention
- Accurate over the range of a given metric required to make a clinically relevant decision.
- Be compact, discreet, and easy to use – the sensor can either be a wearable, catheter, or compact tabletop device for the home setting
- Must not permanently reside within the body
Examples of Ostomy related sensors could be sensors able to measure and alert to one of the following factors:
- Leaks around the peristomal skin
- Uptake of nutrients
- Remission of the underlying condition for the stoma (e.g., colon cancer, irritable bowel disorder)
Examples of Urinary Retention related sensors could be sensors able to measure and alert to one of the following factors:
- Bladder pressure
- Urine volume within the bladder
- Kidney malfunction
Possible Solution Areas:
- Miniaturized ultrasound
- Biomarker/chemical sensors
- ISE and amperometric sensors
- Capacitive sensors
- Pressure sensors
- Spectroscopy (Different types)
- Colorimetric dyes
- Mechanical sensors e.g., resonance or stretch. Could be functionalized to capture analyte of interest.
- Combination of sensor results e.g., added information from machine learning on multiple sensor outputs.
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