Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.

Big data analytics and smart cities: applications, challenges, and opportunities.

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.

Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives.

The question regarding the actual usage of built environments is of immense importance in behavioral research. Yet traditional methods of collecting and analyzing data on movements and activities often lack needed accuracy and granularity. Thus, this article reviewed and summarized the applicability of emergent GPS trajectory mining approaches in the field of architecture from geographic, semantic, and quantitative perspectives, respectively. Accordingly, three experiments based on a case study using real GPS trajectory data from visitors to the Palace Museum in China were conducted to examine the usefulness and weakness of the aforementioned approaches. The findings revealed that although all three dimensions of the trajectory mining approaches had the potential to provide useful information for architectural and urban design, the higher the dimensionality in utilizing the data, the more effective the approach was in discovering generalizable knowledge of human behavioral pattern. Furthermore, the results suggested that to gain insights into the typological characteristics of human behaviors related to the built environments, the contribution of trajectory data alone was limited, hence, conventional field surveys and questionnaires which contain information on individual characteristics and spatial features should be used in conjunction. Future research and practical implications were outlined.