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This paper advances contextual image understanding within perspective-aware Ai (PAi), an emerging paradigm in human–computer interaction that enables users to perceive and interact through each other’s perspectives. While PAi relies on multimodal data—such as text, audio, and images—challenges in data collection, alignment, and privacy have led us to focus on enabling the contextual understanding of images. To achieve this, we developed perspective-aware scene graph generation with LLM post-processing (PASGG-LM). This framework extends traditional scene graph generation (SGG) by incorporating large language models (LLMs) to enhance contextual understanding. PASGG-LM integrates classical scene graph outputs with LLM post-processing to infer richer contextual information, such as emotions, activities, and social contexts. To test PASGG-LM, we introduce the context-aware scene graph generation task, where the goal is to generate a context-aware situation graph describing the input image. We evaluated PASGG-LM pipelines using state-of-the-art SGG models, including Motifs, Motifs-TDE, and RelTR, and showed that fine-tuning LLMs, particularly GPT-4o-mini and Llama-3.1-8B, improves performance in terms of R@K, mR@K, and mAP. Our method is capable of generating scene graphs that capture complex contextual aspects, advancing human–machine interaction by enhancing the representation of diverse perspectives. Future directions include refining contextual scene graph models and expanding multi-modal data integration for PAi applications in domains such as healthcare, education, and social robotics.

Explainable artificial intelligence (XAI) has shed light on enormous applications by clarifying why neural models make specific decisions. However, it remains challenging to measure how sensitive XAI solutions are to the explanations of neural models. Although different evaluation metrics have been proposed to measure sensitivity, the main focus has been on the visual and textual data. There is insufficient attention devoted to the sensitivity metrics tailored for time series data. In this paper, we formulate several metrics, including max short-term sensitivity (MSS), max long-term sensitivity (MLS), average short-term sensitivity (ASS) and average long-term sensitivity (ALS), that target the sensitivity of XAI models with respect to the generated and real time series. Our hypothesis is that for close series with the same labels, we obtain similar explanations. We evaluate three XAI models, LIME, integrated gradient (IG), and SmoothGrad (SG), on CN-Waterfall, a deep convolutional network. This network is a highly accurate time series classifier in affect computing. Our experiments rely on data-, metric- and XAI hyperparameter- related settings on the WESAD and MAHNOB-HCI datasets. The results reveal that (i) IG and LIME provide a lower sensitivity scale than SG in all the metrics and settings, potentially due to the lower scale of important scores generated by IG and LIME, (ii) the XAI models show higher sensitivities for a smaller window of data, (iii) the sensitivities of XAI models fluctuate when the network parameters and data properties change, and (iv) the XAI models provide unstable sensitivities under different settings of hyperparameters.

Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users’ daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients’ habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients’ habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users’ behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.

Time series data acquired from sensors deployed in smart homes present valuable information for intelligent systems to learn activity patterns of occupants. With the increasing need to enable people to age in place independently, the availability of such data is key to the development of home monitoring solutions. In this article we describe an unlabelled dataset of measurements collected from multiple environmental sensors placed in a smart home to capture human activities of daily living. Various sensors were used including passive infrared, force sensing resistors, reed switches, mini photocell light sensors, temperature and humidity, and smart plugs. The sensors record data from the user’s interactions with the environment, such as indoor movements, pressure applied on the bed, or current consumption when using electrical appliances. Millions of raw sensor data samples were collected continuously at a frequency of 1 Hz over a period of six months between 26 February 2020 and 26 August 2020. The dataset can be useful in the analysis of different methods, including data-driven algorithms for activity or habit recognition. In particular, the research community might be interested in investigating the performance of algorithms when applied on unlabelled datasets and not necessarily on annotated datasets. Furthermore, by applying artificial intelligence (AI) algorithms on such data collected over long periods, it is possible to extract patterns that reveal the user’s habits as well as detect changes in the habits. This can benefit in detecting deviations in order to provide timely interventions for patients, e.g., people with dementia.

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.