This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like in modern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user’s physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets.

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

Background: Older adults often want to stay in a familiar place, such as their home, as they get older. This so-called aging in place, which may involve support from relatives or care professionals, can promote older people’s independence and well-being. The combination of aging and disease, however, can lead to complex medication regimes and difficulties for care providers in correctly assessing the older person's health. In addition, the organization of health care is fragmented, which makes it difficult for health professionals to encourage older people to participate in their own care. It is also a challenge to perform adequate health assessments and to engage in appropriate communication between health care professionals. Objective: The purpose of this paper is to describe the design for an integrated home-based system that can acquire and compile health-related evidence for guidance and information-sharing among care providers and care receivers in order to support and promote medication self-management among older people. Methods: The authors used a participatory design approach for this mixed-methods project, which was divided into four phases. Phase I, Conceptualization, consists of the conceptualization of a system to support medication self-management, objective health assessments, and communication between health care professionals. Phase II, Development of a System, consists of building and bringing together the conceptualized systems from Phase I. Phase III, Pilot Study, and Phase IV, Full-Scale Intervention, are described briefly. Results: Participants in Phase I were people who were involved in some way in the care of older adults and included older adults themselves, relatives of older adults, care professionals, and industrial partners. With input from Phase I participants, we identified two relevant concepts for promoting medication self-management, both of which related to systems that participants believed could provide guidance for the older adults themselves, relatives of older adults, and care professionals. The systems will also encourage information-sharing between care providers and care receivers. The first is the concept of the Intelligent Age-Friendly Home (IAFH), defined as an integrated residential system that evolves to sense, reason, and act in response to individuals’ needs, preferences, and behaviors as these change over time. The second concept is the Medication safety, Objective assessments of health-related behaviors, and Personalized medication reminders (MedOP) system, a system that would be supported by the IAFH, and which consists of three related components: one that assesses health behaviors, another that communicates health data, and a third that promotes medication self-management. Conclusions: The participants in this project were older adults, relatives of older adults, care professionals, and our industrial partners. With input from the participants, we identified two main concepts that could comprise a system for health assessment, communication, and medication self-management: the IAFH and the MedOP system. These concepts will be tested in this study to determine whether they can facilitate and promote medication self-management among older people. International Registered Report Identifier (IRRID): DERR1-10.2196/12447