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Short abstract:

This paper offers a case study of a machine-learning enabled system for remote monitoring of people living at home with long term conditions such as dementia, exploring the mundane challenges experienced by smart care users and examining the distribution of labour that addresses these challenges.

Long abstract:

This paper offers a case study of the development of a machine-learning enabled system for remote monitoring of people living at home with long term conditions such as dementia. The study involved interviews with researchers and developers and healthcare professionals involved in developing and delivering the system and also smart care users and their carers. From each perspective, an array of challenges were identified that impeded smooth progress towards the envisaged goal of an intelligent monitoring system able to deliver clinical benefits in terms of reduced hospitalization and longer safe living in the place of the user’s preference. This paper focuses specifically on the challenges identified by smart care users and their carers, including such practical, mundane issues as batteries that run out, sensors that fall off doors, houses that do not conform to designers’ expectations about house layout, weighing scales that need more balance than a user can deliver, physiological measurement devices that do not work for cold hands or on hot days and lifestyles that confound attempts at meaningful machine learning. The paper explores the distribution of labour involved in addressing these challenges across the users and carers, healthcare professionals and researchers and developers, finding that at least in some cases there are steps that can be taken to avoid practical mundane challenges becoming an additional, invisible burden on carers. The paper thus contributes to a growing field of literature focused on the labour of datafication with a reflection on how that labour is distributed.