Today, the Internet of Things, Big Data and Machine Learning are a regular part of people's daily lives, especially for the elderly or the so-called Silver Economy. There are more and more applications in which collecting and storing information about users (and their context) to produce classifications and predictions allows the optimisation of the services offered, improving the life quality of the citizens. In this sense, leisure cruises represent environments where it is necessary to provide quality services for a reasonable price. This requires understandings the behaviour of users both locally (i.e. each cruise) and globally (i.e. all cruises). In this respect, IoT, Big Data and Machine Learning are presented as ideal technologies for prediction-making and the improvement of the quality of services. However, on ships at sea the transfer rate of the Internet connection is limited. In this sense, Edge Computing allows taking part in the application computing to the Edge of the network, closer to the IoT layer. Data can be filtered and pre-processed in the Edge before it is transmitted to the Cloud, reducing its volume and the associated costs of its computing and storage in the Cloud. Machine Learning algorithms can also be applied to recognise patterns and anomalies on the Edge itself, even if the connectivity with the Cloud is lost. In this paper, we propose the design of a platform which aims to improve the quality of life of the elderly on leisure cruises.