Journal of Ambient Intelligence and Smart Environments - Volume 11, issue 6

Authors: Aghajan, Hamid | Härmä, Aki | Wang, Kevin I-Kai | Augusto, Juan Carlos

Article Type: Editorial

DOI: 10.3233/AIS-190542

Citation: Journal of Ambient Intelligence and Smart Environments, vol. 11, no. 6, pp. 473-474, 2019

Authors: Sprute, Dennis | Tönnies, Klaus | König, Matthias

Article Type: Research Article

Abstract: Virtual borders are employed to allow humans the interactive and flexible restriction of their mobile robots’ workspaces in human-centered environments, e.g. to exclude privacy zones from the workspace or to indicate certain areas for working. They have been successfully specified in interaction processes using methods from human-robot interaction. However, these methods often lack an expressive feedback system, are restricted to robot’s on-board interaction capabilities and require a direct line of sight between human and robot. This negatively affects the user experience and interaction time. Therefore, we investigate the effect of a smart environment on the teaching of virtual borders with …the objective to enhance the perceptual and interaction capabilities of a robot. For this purpose, we propose a novel interaction method based on a laser pointer, that leverages a smart home environment in the interaction process. This interaction method comprises an architecture for a smart home environment designed to support the interaction process, the cooperation of human, robot and smart environment in the interaction process, a cooperative perception including stationary and mobile cameras to perceive laser spots and an algorithm to extract virtual borders from multiple camera observations. The results of an experimental evaluation support our hypotheses that our novel interaction method features a significantly shorter interaction time and a better user experience compared to an approach without support of a smart environment. Moreover, the interaction method does not negatively affect other user requirements concerning completeness and accuracy. Show more

Keywords: Robot workspace restriction, virtual borders, smart home environment, network robot system

DOI: 10.3233/AIS-190539

Citation: Journal of Ambient Intelligence and Smart Environments, vol. 11, no. 6, pp. 475-494, 2019

Authors: Rosales Sanabria, Andrea | Kelsey, Thomas W. | Dobson, Simon | Ye, Juan

Article Type: Research Article

Abstract: Daily human activity recognition using sensor data can be a fundamental task for many real-world applications, such as home monitoring and assisted living. One of the challenges in human activity recognition is to distinguish activities that have infrequent occurrence and less distinctive patterns. We propose a hierarchical classifier to perform two-phase learning. In the first phase the classifier learns general features to recognise majority classes, and the second phase is to collect minority and subtle classes to identify fine difference between them. We compare our proposal with a collection of state-of-the-art classification techniques on four real-world third-party datasets that involve …different types of object sensors and are collected in different environments and on different subjects and six imbalanced datasets from the UCI-Irvine Machine Learning repository. Our results demonstrate that our hierarchical classifier approach performs better than state-of-the-art techniques including both structure- and feature-based learning techniques. The key novelty of our approach is that we reduce the bias of the ensemble classifier by training it on a subspace of data, which allows identification of activities with subtle differences, and thus provides well-discriminating features. Show more

Keywords: Activity recognition, representation learning, sampling techniques, ensemble learning, smart home

DOI: 10.3233/AIS-190541

Citation: Journal of Ambient Intelligence and Smart Environments, vol. 11, no. 6, pp. 495-513, 2019

Authors: Ujager, Farhan Sabir | Mahmood, Azhar | Khatoon, Shaheen

Article Type: Research Article

Abstract: With the advent of smart computing, Internet of Things (IoT) and sensor technology, it is now possible to determine the wellness of the elderly living alone in a home equipped with miniaturized sensors. The technology has the potential to seamlessly monitor daily activities performed by humans living under free conditions. Such automatic activity monitoring systems depend on classification techniques, which can effectively interpret activities as normal or abnormal. Although a significant progress has been made in this area, the application of advanced classification techniques are still required to capture various aspects of activities to predict the wellness with high accuracy. …In this paper, we propose a wellness classification model called Temporal Weighted Associative Classification (TWAC) by integrating the spatio-temporal contextual information for predicting wellness of the elderly by monitoring their daily usage patterns of household appliances equipped with sensors. The two key components of the model are Data Preparation Module (DPM) and Dynamic Classification Module (DCM). The DPM collects data from sensors and represents it into a format suitable to apply classification by capturing spatio-temporal information of each activity such as time, location, duration and sub-activities, if any. DCM dynamically generates classification rules by identifying correlations among frequent and less frequent activities recorded over a predefined time window. A classification model is then based on these rules to predict normal or abnormal activities. Moreover, the classification model learns from new data and dynamically updates the rules to accommodate the change in daily activities’ pattern, hence, over time the prediction become more accurate. TWAC is tested for its accuracy by comparing it with well-known classifiers such as C4.5, HMM, NB, SVM, CPAR and CBAR. Improved analysis results have been observed as documented in the experimental analysis. Based on a high accuracy, the proposed model will be suitable to develop systems used to forecast the behavior and wellness of the elderly living alone in smart homes. Show more

Keywords: Wellness detection, elderly people, WSN smart homes, activity recognition, temporal correlation

DOI: 10.3233/AIS-190538

Citation: Journal of Ambient Intelligence and Smart Environments, vol. 11, no. 6, pp. 515-526, 2019

Authors: Alkobaisi, Shayma | Bae, Wan D. | Horak, Matthew | Narayanappa, Sada | Lee, Jongwon | AbuKhousa, Eman | Park, Choon-Sik | Bae, Da Jung

Article Type: Research Article

Abstract: The emerging predictive health analytics provides great promise in reducing costs and improving health outcomes. However, most predictive models do not capture environmental exposures that impact health risk patterns in several chronic diseases such as asthma. This gap prompted the development of the exposome paradigm to improve health intervention and prevention by providing meaningful and understandable feedback on individuals’ collected data and minimizing their exposures to health risks. The exposome paradigm focuses on the simultaneous monitoring of mobility behaviors and measurement of environmental conditions to capture their impact on human health. In this paper, we introduce the concept of exposome …analytics that compliments predictive analytics to develop an effective health monitoring and management system. We present the current analytical developments including our ongoing project to manage risks of asthma exacerbations as a case study. Our proposed approach uses a novel exposome assessment paradigm that utilizes the spatio-temporal properties of the data in the model training process and hence results in improving the accuracy of asthma prediction. The quality of the proposed approach is extensively evaluated using real patients and environmental datasets. Show more

Keywords: Exposome, predictive health analytics, individual-level health analytics, asthma risk management, classification, logistic regression, quantile regression

DOI: 10.3233/AIS-190540

Citation: Journal of Ambient Intelligence and Smart Environments, vol. 11, no. 6, pp. 527-552, 2019