Diabetic Foot Complications
Current estimates indicate there are approximately 350 million diabetics in the world, with over 15 million developing diabetic foot ulcers (DFU), which is the most common cause of amputations.
The primary treatment of DFU is “Mechanical Off-loading”. However, research indicates that patient adherence to off-loading is limited and treatment failure is the norm. This may likely be due to inadequate utilization and adherence to mechanical off-loading. Moreover, verification of mechanical off-loading is currently limited to clinical settings using in-shoe plantar pressure analysis. However, once patients are away from the clinic, there has been no adequate way to provide ongoing monitoring of plantar pressure.
Sensoria’s textile sensor technology, along with real-time alerting capabilities, can potentially be applied to acute care, primary prevention and secondary prevention scenarios.
Fall Detection and Prevention
Whereas an accelerometer alone may yield false positives when a device is forgotten or dropped by the patient, the combination of accelerometer with plantar pressure readings will reduce false positives, yield more reliable fall detections and reduce the costs of unnecessary care escalation or ambulance dispatch.
In terms of prevention, we endeavor to reduce the burden of falls through targeted early alerts to encourage a change in behavior, such as use of a walker, or care escalation, such as visiting a physical therapist.
The socket fitting is one of the most challenging aspects of the entire prosthesis. The difficulties accompanied with the socket are that it needs to have a perfect fit, with total surface bearing to prevent painful pressure spots. Thanks to our proprietary e-textile, we could improve the socket fitting process by detecting the main pressure points.
Sensoria will enable a range of rehabilitation services to be delivered at home. For example, functional capacity and mobility tests – traditionally restricted to the clinic setting – could be tracked and recorded in the community setting to monitor recovery, track therapy response, and predict hospitalization risk. To begin, Sensoria could provide live guidance to ensure accurate performance of current standard tests. In the future algorithms could be developed to passively track physical function and mobility, thereby replacing clinical proxies with actual “real-life” data. The monitoring functionality could also be paired with therapeutic modules to create a home-based rehabilitation program.