Abstract
Ambient assisted living (AAL) environments represent a key concept for dealing with the inevitable problem of population-ageing. Until recently, the use of computational intensive techniques, like Machine Learning (ML) or Computer Vision (CV), were not suitable for IoT end-nodes due to their limited resources. However, recent advances in edge intelligence have somehow changed this landscape for smart environments. This paper presents an AAL scenario where the use of ML is tested in kitchen appliances recognition using CV. The goal is to help users working with those appliances through Augmented Reality (AR) on a mobile device. Seven types of kitchen appliances were selected: blender, coffee machine, fridge, water kettle, microwave, stove, and toaster. A dataset with nearly 4900 images was organized. Three different deep learning (DL) models from the literature were selected, each with a total number of parameters and architecture compatibles with their execution on mobile devices. The results obtained in the training of these models reveal precision in the test set above 95% for the model with better results. The combination of edge AI and ML opens the application of CV in smart homes and AAL without compromising mandatory requirements as system privacy or latency. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Psoriasis is a chronic inflammatory skin disease with a high worldwide incidence that in worst cases reaches 4.6%. This dermatosis can be associated with other comorbidities and has a significant negative impact on labor productivity and the quality of life of affected people. During day-to-day lives, psoriasis patients come across several practical clinical difficulties, e.g. to i) easily register a time evolution of affected skin areas (for later analysis by health carers); ii) daily evaluate the size of each affected skin area, to be able to iii) calculate the amount of medication to be applied on those affected body areas. In such a context, this paper proposes the Follow-App mobile system aiming to support people with psoriasis, by alleviating and managing their daily life with the disease. More precisely, the goals of the system are: to allow individual photographic registration of body parts affected by psoriasis; in addition, cataloging each image according to its body segment location and sampling date; then, on those photos, automatically detect and segment the affected skin surface, to posteriorly be able to calculate the area of the lesions; finally, based on the area and prescribed medicine, dynamically accounting the amount of topical medicine to use. These were the requirements addressed by the proposed system prototype. The evaluation tests on the ability to detect and quantify the area of the skin lesions were performed on a data-set with 22 images. The proposed segmentation algorithm for detecting the area of redness lesions reached an IoU rate over 81%. Therefore, the proposed Follow-App mobile system may become an important asset for people with psoriasis since the extent and redness of affected areas are major evaluation factors for the disease severity. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper presents an efficient, battery-powered, low-cost, and context-aware IoT edge computing solution tailored for monitoring a real enterprise cooking oil collecting infrastructure. The presented IoT solution allows the collecting enterprise to monitor the amount of oil deposited in specific barrels, deployed country-wide around several partner restaurants. The paper focuses on the specification, implementation, deployment and testing of ESP32/ESP8266-based end-node components deployed as an edge computing monitoring infrastructure. The achieved low-cost solution guarantees more than a year of battery life, reliable data communication, and enables automatic over-the-air end-node updates. The open-source software libraries developed for this project are shared with the community and may be applied in scenarios with similar requirements. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
Cloud computing is omnipresent and plays an important role in today’s world of Internet of Things (IoT). Several IoT devices and their applications already run and communicate through the cloud, easing the configuration burden for their users. With the expected exponential growth on the number of connected IoT devices this centralized approach raises latency, privacy and scalability concerns. This paper proposes the use of fog computing to overcome those concerns. It presents an architecture intended to distribute the communication, computation and storage loads to small gateways, close to the edge of the network, in charge of a group of IoT devices. This approach saves battery on end devices, enables local sensor fusion and fast response to urgent situations while improving user privacy. This architecture was implemented and tested on a project to monitor the level of used cooking oil, stored in barrels, in some restaurants where low cost, battery powered end devices are periodically reporting sensor data. Results show a 93% improvement in end device battery life (by reducing their communication time) and a 75% saving on cloud storage (by processing raw data on the fog device). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
This project proposes a machine learning (ML) pipeline for inferring office employee's well-being, from heterogeneous sources of contextual data (cf. physiological, social and workplace environment), which brings several demanding issues. In this paper we focus specifically in raw data collection problems and pre-processing challenges. To start with, context data was collected in real environments, during weeks, in several office organizations and involving employees along theirs daily working routines. Moreover, data collection resort to a wide range of sources (e.g. sensors, questionnaires, apps, etc.) that were subject to potential interferences and noisy conditions. Given the influence of data quality in ML algorithms results and considering the number of instruments used, it was essential to implement a pre-processing stage to automate and improve the quality of collected data. Hence, the usefulness of the proposed DQVA tool, which computes several common statistical measures and provides also graphical and tabular visual insights about the data. For example, it allows to: i) compare data sources from different participants and organizations, on a per sensor/data source basis (through data tables, data distribution histograms, and visualizations); iii) check and pinspot the existence of outliers; iv) visually spot signal gaps; etc. Therefore, we argue that the proposed DQVA tool allows to evaluate, per sensor and per individual, raw data quality, on the integration stage of our classification pipeline. It proved to be an agile, useful and simple to re-use tool for detecting raw data irregularities, thus increasing data quality assurances for the next steps of our classification pipeline.

Abstract
Abstract The world population is facing several difficulties related to an aging society. In particular, the widespread increase of chronic and incapacitating diseases is overwhelming for traditional healthcare services. Ambient assisted living (AAL) systems can greatly improve healthcare scalability and scope while keeping people in the comfort of their home environments. This chapter focuses precisely on presenting the fundamental key aspects (cf. processing and sensing, integration and management, communication and coordination, intelligence and reasoning) to promote safety and support for outpatients living autonomously in AAL settings. Furthermore, for each key issue, a set of practical technological solutions are reported and detailed, showing real applicability of ubicomp technology to the integration and management of AAL systems specially designed for supporting daily living activities of people with progressive loss of capacities. © 2019 John Wiley & Sons, Ltd.