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Background Even if we are not aware of it, machine learning techniques are part of our daily lives. It is of the utmost interest that citizens become familiar with the use of these techniques and discover their potential to solve everyday problems. Objective and Methods In this article, we describe the methodology and results of a highly replicable citizen science project that allows citizens to get closer to the scientific process and understand the potential of machine learning to solve a social problem of interest to them. For this purpose, we have chosen a problem of social relevance in contemporary societies, namely the detection of loneliness in older adults. Citizens are challenged to apply machine learning techniques to identify levels of loneliness from natural language. Results The results of this project suggest that citizens are willing to engage in science when the challenges posed are of social interest to them. A total of 1517 citizens actively engaged in the project. A database containing 1112 texts about loneliness expressions was collected. An accuracy of 83.12% using the logistic regression algorithm and 62.23% accuracy when using the Naïve Bayes algorithm was reached in detecting loneliness from texts. Conclusions Detecting loneliness using machine learning techniques is an attractive and relevant topic that allows citizens to be involved in science and introduces them to machine learning practices. The methodology of this project can be replicated in other places around the world.

The ageing of the population needs the automation of patient monitoring. The objective of this is twofold: to improve care and reduce costs. Frailty, as a state of increased vulnerability resulting from several diseases, can be seen as a pandemic for older people. One of the most common detection tests is gait speed. This article compares the gait speed measured outdoors using smartphones with that measured using manual tests conducted in medical centres. In the experiments, the walking speed was measured over a straight path of 80 m. Additionally, the speed was measured over 2.4 m in the middle of the path, given that this is the minimum distance used in medical frailty tests. To eliminate external factors, the participants were healthy individuals, the weather was good, and the path was flat and free of obstacles. The results obtained are promising. The measurements taken with common smartphones over a straight path of 80 m are within the same order of error as those observed in the manual tests conducted by practitioners.

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.