Smart sensing systems that integrate the latest signal processing techniques with IT tools to automatically interpret time series data and output information in a form suited to clinical use need to support the busy practice of medicine. Increased throughput of patients will widen access to the processes of screening, diagnostics and therapy. In all cases, a smart sensing system needs to discriminate the possible series of conditions related to the application range. Sensors should be mechanically simple and robust to suit the arduous medical working environment while also being cost-effective through enabling staff to work efficiently.
At one level smart sensing instruments are needed to provide information output to assist in diagnosis and monitoring in critical care rather than outputting data which requires time consuming processing by clinical staff. A system may track the state of a patient and alert changes in state, or critical stages, and may form part of a bed chair or instrument used to help the patient or clinician.
At the opposite level of operation in healthcare, smart sensing systems can empower patients to monitor themselves. In these applications the devices would need to be intuitive to use and indeed the patient may not be informed on its operation as it may be integrated into a device used in daily living, such as a walking aid or mat.
Already monitoring devices to measure temperature or CDV metrics are commercially available. However, these produce values that on their own do not provide information on well being, progression on a pathology, or discriminate pathologies. Smart sensing systems could also provide elementary advice or improvements in technique or ritual in daily living. Information is not only an important form of output to be directly useful in clinical work or to advise patients remotely, it has the potential to provide for efficient storage of types of event or condition and can be transmitted more efficiently than data. This is compatible with devices for remote use and links to health information systems on the internet.
The research presented here describes two examples: