Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex black boxes, meaning their internal logic and inner workings are hidden to the user and even experts cannot fully understand the rationale behind their predictions. Moreover, new regulations and highly regulated domains have made the audit and verifiability of decisions mandatory, increasing the demand for the ability to question, understand, and trust machine learning systems, for which interpretability is indispensable. The research community has recognized this interpretability problem and focused on developing both interpretable models and explanation methods over the past few years. However, the emergence of these methods shows there is no consensus on how to assess the explanation quality. Which are the most suitable metrics to assess the quality of an explanation? The aim of this article is to provide a review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics. Furthermore, a complete literature review is presented in order to identify future directions of work on this field.

Preventing falls is extremely important today as people live long sedentary lives. Fall prevention platforms can help, by stimulating seniors to perform exercises that improve balance and muscular strength. However, existing platforms for fall prevention mostly target individual users exercising at home. This paper describes the design and evaluation of a multi-player fall prevention game platform, FallSensing Games, to be used in senior care centers. The game design was inspired by the Otago Exercise Programme and the evaluation focused on biomechanical parameters, game experience, and technology acceptance. Results showed that the game was easy to follow, that seniors performed exercises correctly, and that the game integrated well with the activities of the senior care centers. Lessons learned from this project may inspire the development of similar platforms, and, in this way, support group exercise practices at senior care centers.

We present a complete and modular framework that extract trajectories in a real and complex retail scenario, under unconstrained video conditions. Two motion tracking algorithms that combine features from crowd motion detection and multiple tracking are presented to build motion patterns and understand customer's behavior. Their evaluation across several datasets show promising results.

The increasing demand for human activity analysis on surveillance scenarios has been provoking the emerging of new features and concepts that could help to identify the activities of interest. In this paper, we present a context-based descriptor to identify individual profiles. It accounts with a multi-scale histogram representation of position-based and attention-based features that follow a key-point trajectory sampling. The notion of profile is expressed by a new semantic concept introduced as an adjective for action recognition. We also identify a very rich dataset, in terms of intensity and variability of human activity, and extended it by manual annotation to validate the introduced concept of profile and test the descriptor's discriminative power. High rates of recognition were achieved.

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Anthropometry has been widely used in different fields, providing relevant information for medicine, ergonomics and biometric applications. However, the existent solutions present marked disadvantages, reducing the employment of this type of evaluation. Studies have been conducted in order to easily determine anthropometric measures considering data provided by low-cost sensors, such as the Microsoft Kinect. In this work, a methodology is proposed and implemented for estimating anthropometric measures considering the information acquired with this sensor. The measures obtained with this method were compared with the ones from a validation system, Qualisys. Comparing the relative errors determined with state-of-art references, for some of the estimated measures, lower errors were verified and a more complete characterization of the whole body structure was achieved.

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