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The aim of this current work is to identify three different gears of cross-country skiing utilizing embedded inertial measurement units and a suitable deep learning model. The cross-country style studied was the skating style during the uphill, which involved three different gears: symmetric gear pushing with poles on both sides (G3) and two asymmetric gears pushing with poles on the right side (G2R) or to the left side (G2L). To monitor the technique, inertial measurement units (IMUs) were affixed to the skis, recording acceleration and Euler angle data during the uphill tests performed by two experienced skiers using the gears under study. The initiation and termination points of the tests were controlled via Bluetooth by a smartphone using a custom application developed with Android Studio. Data were collected on the smartphone and stored on the SD memory cards included in each IMU. Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. The performance of the model in cross-user evaluations demonstrated an overall accuracy of 90%, and it achieved an accuracy of 98% in the cross-scene evaluations for individual users. These results indicate a promising performance of the developed system in distinguishing between different ski gears within skating styles, providing a valuable tool to enhance ski training and analysis.

Active ageing is a multidimensional process for achieving the potential quality of life and meaning in the life cycle. In the context of the Andalusian region in Spain, where the majority of the population is over 60 years old and lives in rural areas, it has become a key challenge. That is why the European projects within the Framework Programme for Research and Innovation, such as Pharaon - Pilots for Active and Healthy Ageing, promote technologies adapted by and for our elders. In the case of the Andalusian pilot, part of this project, we have selected a social network adapted to them, enabling them to communicate with the community at home and share their experiences. In addition, to improve their physical fitness, a device to count active minutes and steps is included, which provides users and caregivers with a visible and objective metric of daily health status. The technology has been evaluated following a well-defined methodology, which is described in this work to promote the deployment of technology in large-scale pilots. A specific architecture (Information System for Active Ageing in Andalusia - ISA3) and the components evaluated within a common ecosystem (Pharaon Project) are presented.

Activity Recognition is an active research topic focused on detecting human actions and behaviours in smart environments. In most cases, the use of data-driven models aim to relate data from sensors to an activity through a model developed by a supervised approach. In this work, we focus on the goal of domain adaptation between smart environments, which has required a novel approach to relate the concepts of domain adaptation using binary sensor and learning from daily imbalanced data. In this work, the sensor activation from a given context is translated to a different one, based on the temporal alignment from human activities. The domain adaptation of binary sensor is accomplished through a three step procedure: i) clustering of sensor activation, ii) activity based alignment of sensor data between the two environments, iii) an ensemble of classifiers used to mine a mapping function, translating sensor data between the two environments. The proposed method was evaluated over a publicly available dataset, and obtained preliminary results which were encouraging with an F1-Score of 87%.

The use of multimodal sensors to describe activities of daily living in a noninvasive way is a promising research field in continuous development. In this work, we propose the use of ambient audio sensors to recognise events which are generated from the activities of daily living carried out by the inhabitants of a home. An edge–fog computing approach is proposed to integrate the recognition of audio events with smart boards where the data are collected. To this end, we compiled a balanced dataset which was collected and labelled in controlled conditions. A spectral representation of sounds was computed using convolutional network inputs to recognise ambient sounds with encouraging results. Next, fuzzy processing of audio event streams was included in the IoT boards by means of temporal restrictions defined by protoforms to filter the raw audio event recognition, which are key in removing false positives in real-time event recognition.

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.

Pressure ulcers affect the quality of life and health level of patients while causing a cost increase for the health system. Despite all of this, there is no monitoring system for this disease, even though it is very common in hospitalized patients. To resolve this issue, in this work, we present a prototype for monitoring and caring of pressure ulcers, through an intelligent system based on wearable inertial devices. The system detects the position of patients with reduced mobility while in bed, walking, or standing; providing, to medical staff and caregivers, useful information by means of mobile devices, in particular: (i) A historical record of body activity and (ii) notifications when a single position persists for a prolonged period of time. The following contributions are presented in this paper. Firstly, a technological survey of healthcare systems applied to pressure ulcers is presented. Secondly, an architecture and a methodology for modelling wearable inertial sensors for classifying body position are proposed. Finally, experimental tests with real cases were conducted to evaluate machine learning approaches and to validate the position estimation of the case study developed with four participants.

The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.

In this paper we present Deep Learning (DL) modelling to forecast the behaviour and energy production of a photovoltaic (PV) system. Using deep learning models rather than following the classical way (analytical models of PV systems) presents an outstanding advantage: context-aware learning for PV systems, which is independent of the deployment and configuration parameters of the PV system, its location and environmental conditions. These deep learning models were developed within the Ópera Digital Platform using the data of the UniVer Project, which is a standard PV system that was in place for the last twenty years in the Campus of the University of Jaén (Spain). From the obtained results, we conclude that the combination of CNN and LSTM is an encouraging model to forecast the behaviour of PV systems, even improving the results from the standard analytical model.