MABLE Carer is a mobile App providing ambient contextual learning to carers. The first application will be focused upon Dementia Carers. MABLE Carer monitors live conversations between the carer and person under care (through speech recongition and eventually audio analysis), processes that through a custom-developed Natural Language Classifier and uses the resultant classification to provide immediate in-depth and contextually relevant learnings. Through this mechanism Carers can naturally learn to adapt to the challenges of Dementia care in-situ with less risk of escalation.
The Natural Language Classifier is built using data gathered in regular communications occurring through the core MABLE system – combining speech recognition with carer end-of-call surveys as seed supervised classifications.
In this way, MABLE Carer is but one example of a new generation of Ambient Contextual Learning (ACL) systems that are possible by combining well-constructed domain knowledge with in-situ awareness. Learning can be focused, relevant, lifelong, and practical.
Carers are one of the toughest audiences for this type of technology intervention but also one with the most potential benefit. This group in general, and those carers for Dementia face a steep learning curve of situations that they have never experienced nor can always easily empathize with. People under care with Dementia increasingly cannot moderate their behaviour and many types of carer interactions can easily make the situation worse, leading to increased anxiety, stress, and in some cases anger and violence. As a consequence the carer must learn to recognize situations and appropriate responses.
A typical Dementia Carer learning experience involves one of two imperfect choices; try to absorb all possible situations and responses AHEAD of time, or react in the moment as best as possible and followup AFTER THE FACT to be better prepared for the next case of the situation. Both existing approaches are highly error-prone and often lead to stress, anxiety or even anger which become escalating problems.
This is compounded by care being an additive to the carer life, a second job for most. Adding a steep learning time commitment on top of an already-difficult time demand reduced the likihood of using learning resources even where they do exist.
MABLE Carer digests learning to immediately-relevant chunks delivered just-in-time. To support this limited fast-response delivery, MABLE Carer will track learning chunks through the day and provide a focused contextually-relevant deeper learning resource if and when the carer has additional time to digest more (in transit, at end-of-day, etc.)