Negotiating the capacities and limitations of sensor-mediated care in the home
基于对研究人员、医护人员、服务用户及照护者的访谈，探讨了机器学习支持的居家传感器系统如何通过多种劳动形式，从缺失数据中提取有意义、合乎伦理且可持续的信息，以改善痴呆等长期疾病患者的护理沟通。
Abstract In-home sensor systems supported by machine learning are increasingly used to enhance communication between those living with long-term conditions such as dementia and healthcare professionals and carers who support them. Perspectives from the sociology of infrastructures are used to explore the development and deployment of such a system of smart care, drawing on interviews with researchers and developers, healthcare professionals and service users, and carers. The analysis finds that labor of various forms is required to manage the production of useful sensor data, including parsing the reasons for missing data and organizing appropriate actions in response. The analysis highlights active processes of deriving meaning from that data in ways that participants find useful, ethical, and sustainable. The conclusion emphasizes the usefulness of an infrastructural approach in order to recognize the heterogeneous forms of labor involved in developing ethically sensitive, person-centered forms of remote-monitoring-enabled care.