Animal welfare is a multidimensional phenomenon and currently its on-farm assessment requires complex, multidimensional frameworks involving farm audits which are time-consuming, infrequent and expensive. The core principle of precision agriculture is to use sensor technologies to improve the efficiency of resource use by targeting resources to where they give a benefit. Precision livestock farming (PLF) enables farm animal management to move away from the group level to monitoring and managing individual animals. A range of precision livestock monitoring and control technologies have been developed, primarily to improve livestock production efficiency. Examples include using camera systems monitoring the movement of housed broiler chickens to detect problems with feeding systems or disease and leg-mounted accelerometers enabling the detection of the early stages of lameness in dairy cows. These systems are already improving farm animal welfare by, for example, improving the detection of health issues enabling more rapid treatment, or the detection of problems with feeding systems helping to reduce the risk of hunger. Environmental monitoring and control in buildings can improve animal comfort, and automatic milking systems facilitate animal choice and improve human-animal interactions. Although these precision livestock technologies monitor some parameters relevant to farm animal welfare (e.g. feeding, health), none of the systems yet provide the broad, multidimensional integration that is required to give a complete assessment of an animal’s welfare. However, data from PLF sensors could potentially be integrated into automated animal welfare assessment systems, although further research is needed to define and validate this approach.
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This chapter describes the growing influence of point-of-care diagnostics (POCD) on the daily lives of citizens, their immediate families, and healthcare providers. With a view to the future, the most important contemporary developments in this field are discussed, such as noninvasive sensor technology in the diagnostic process, practical examples of point-of-care diagnostics (POCD), including the quantify-self movement and infrared technology. Cost-effectiveness, adoption of POCD, and the contribution of POCD innovations to self-management and health literacy are also discussed. Developments in which deep learning and artificial intelligence are used to make the diagnostic results more reliable are also conferred, such as the development of point-of-care Internet diagnostics. The discussion of professional advice dilemma’s in POCD, the patient’s appreciation of POCD, and ethical and philosophical considerations conclude this chapter.
Sensors in offices mainly measure environmental data, missing qualitative insights into office workers’ perceptions. This opens the opportunity for active individual participation in data collection. To promote reflection on office well-being while overcoming experience sampling challenges in terms of privacy, notification, and display overload, and in-the-moment data collection, we developed Click-IO. Click-IO is a tangible, privacy-sensitive, mobile experience sampling tool that collects contextual information. We evaluated Click-IO for 20-days. The system enabled real-time reflections for office workers, promoting self-awareness of their environment and well-being. Its non-digital design ensured privacy-sensitive feedback collection, while its mobility facilitated in-the-moment feedback. Based on our findings, we identify design recommendations for the evelopment of mobile experience sampling tools. Moreover, the integration of contextual data with environmental sensor data presented a more comprehensive understanding of individuals’ experiences. This research contributes to the development of experience sampling tools and sensor integration for understanding office well-being.
