Toward a Holistic Computational Representation for Sleep Quality and its Support for Explainability.

Sleep quality is recognized as one of the main factors that affect human health. Thus, several studies have been encouraged to analyze features, such as stress level and female menopause, which are directly related to sleep quality. While these works rely mostly on reductionism as the philosophical framework, we approach this problem from a holist perspective, using a model with 10 features that could provide more reliable explanations for inductive conclusions. We demonstrate the principles of this hypothesis by analyzing the data regarding the day before a sleep episode of 1736 volunteers. This analysis shows, for example, the performance of each feature when they are jointly used along prediction tasks. Moreover, we evaluate the readability and accuracy of explanations, given as description logic sentences and based on a knowledge representation that considers the 10 features as elements that compose a sleep quality ontological definition.

Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data.

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals' daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.

A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition.

Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities "in the wild" demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user's movements in time series. However, frequency series contain more information about sensors' signals. In this paper, we investigate the use of feature engineering to improve the performance of a Deep Learning model. Thus, we propose using Fast Fourier Transform algorithms to extract features from frequency series instead of time series. We evaluated our approach on the ExtraSensory and WISDM datasets. The results show that using Fast Fourier Transform algorithms to extract features performed better than using statistics measures to extract features from temporal series. Additionally, we examined the impact of individual sensors on identifying specific labels and proved that incorporating more sensors enhances the model's effectiveness. On the ExtraSensory dataset, the use of frequency features outperformed that of time-domain features by 8.9 p.p., 0.2 p.p., 39.5 p.p., and 0.4 p.p. in Standing, Sitting, Lying Down, and Walking activities, respectively, and on the WISDM dataset, the model performance improved by 1.7 p.p., just by using feature engineering.

Behaviour recommendations with a deep learning model and genetic algorithm for health debt characterisation.

Human behaviour is a dense longitudinal multi-featured measure that directly impacts the health of individuals in the short and long terms. Therefore, issues usually emerge from the insistence on performing risky behaviours, such as smoking or eating fast foods, which continuously increase the gap between current and beneficial health states. This paper introduces the term "health debt" as an economic metaphor to represent the quantification of this gap in domains such as sleep, contributing to physical and mental health states. Then, we present a theoretical framework that relies on behaviour change recommendations to quantify this debt. The practical instantiation of this framework relies on passively assessed sleep related data via personal wearable devices, and uses of an attention-based predictive model as the fitness function of a genetic algorithm that acts as a recommender. We evaluate this proposal by means of a case example aimed at improving the sleep duration of individuals. Results show, for example, that the use of individual rather than generic datasets produces more accurate models. At the same time, the use of constraints on the variability of behaviours features generates more feasible recommendations. These foundations open new research opportunities to support the adoption of preventive medicine based on longitudinal wearable passive data analysis.