Human Activity Recognition (HAR) in smart environments is a well-explored research domain, given its diverse applications which include healthcare, surveillance, building management, and many more. While the majority of HAR research focuses on recognizing the activities of a single subject, in real-world scenarios smart environments are often populated by multiple subjects that may be engaged in both independent and joint activities. This gives rise to the challenge of Multi-Subject HAR, which is an open and complex problem. This survey paper aims to offer researchers and practitioners a comprehensive analysis of Multi-Subject HAR, encompassing its potential applications, sensing solutions, methods, datasets, evaluation metrics, and ongoing challenges. In addition to presenting the latest research works in this area and identifying open issues, our major contributions consist of a comprehensive problem formalization and a thorough discussion of the evaluation metrics to assess different dimensions of multi-subject HAR systems. • We provide a comprehensive formalization for multi-subject HAR in smart environments. • We review the latest research works and datasets in this area. • We explore evaluation metrics and dataset-splitting strategies.

Context-aware Human Activity Recognition (HAR) is a hot research area in mobile computing, and the most effective solutions in the literature are based on supervised deep learning models. However, the actual deployment of these systems is limited by the scarcity of labeled data that is required for training. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate this issue, by infusing common-sense knowledge about human activities and the contexts in which they can be performed into HAR deep learning classifiers. Existing NeSy methods for context-aware HAR rely on knowledge encoded in logic-based models (e.g., ontologies) whose design, implementation, and maintenance to capture new activities and contexts require significant human engineering efforts, technical knowledge, and domain expertise. Recent works show that pre-trained Large Language Models (LLMs) effectively encode common-sense knowledge about human activities. In this work, we propose ContextGPT: a novel prompt engineering approach to retrieve from LLMs common-sense knowledge about the relationship between human activities and the context in which they are performed. Unlike ontologies, ContextGPT requires limited human effort and expertise, while sharing similar privacy concerns if the reasoning is performed in the cloud. An extensive evaluation using two public datasets shows how a NeSy model obtained by infusing common-sense knowledge from ContextGPT is effective in data scarcity scenarios, leading to similar (and sometimes better) recognition rates than logic-based approaches with a fraction of the effort.

Human Activity Recognition (HAR) with mobile and wearable devices has been deeply studied in the last decades. Research groups working on this topic evaluated their proposed methods mostly on public datasets. However, most of the existing datasets only include inertial sensor data, while it is well-known that additional context data (e.g., semantic location) has the potential to significantly improve the recognition rate. Only a few datasets for context-aware HAR are publicly available, and their annotations were mostly self-reported in-the-wild by the subjects involved in data acquisition. This method harms the quality of annotations, thus discouraging the application of supervised models. In this paper, we propose DOMINO, a new public dataset for context-aware HAR. DOMINO includes 25 users (wearing a smartphone and a smartwatch) performing 14 activities. During data acquisition, the mobile devices recorded both inertial and high-level context data while our team monitored the quality of the self-reported annotations. Our experiments on DOMINO show the positive impact of considering high-level context information for Human Activity Recognition.

Abstract The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments enables several important applications, including the continuous monitoring of fragile subjects in their homes for healthcare systems. The majority of the approaches in the literature assume that only one resident is living in the home. Multi-inhabitant ADLs recognition is significantly more challenging, and only a limited effort has been devoted to address this setting by the research community. One of the major open problems is called data association, which is correctly associating each environmental sensor event (e.g., the opening of a fridge door) with the inhabitant that actually triggered it. Moreover, existing multi-inhabitant approaches rely on supervised learning, assuming a high availability of labeled data. However, collecting a comprehensive training set of ADLs (especially in multiple-residents settings) is prohibitive. In this work, we propose MICAR: a novel multi-inhabitant ADLs recognition approach that combines semi-supervised learning and knowledge-based reasoning. Data association is performed by semantic reasoning, combining high-level context information (e.g., residents’ postures and semantic locations) with triggered sensor events. The personalized stream of sensor events is processed by an incremental classifier, that is initialized with a limited amount of labeled ADLs. A novel cache-based active learning strategy is adopted to continuously improve the classifier. Our results on a dataset where up to 4 subjects perform ADLs at the same time show that MICAR reliably recognizes individual and joint activities while triggering a significantly low number of active learning queries.

In the general machine learning domain, solutions based on the integration of deep learning models with knowledge-based approaches are emerging. Indeed, such hybrid systems have the advantage of improving the recognition rate and the model’s interpretability. At the same time, they require a significantly reduced amount of labeled data to reliably train the model. However, these techniques have been poorly explored in the sensor-based Human Activity Recognition (HAR) domain. The common-sense knowledge about activity execution can potentially improve purely data-driven approaches. While a few knowledge infusion approaches have been proposed for HAR, they rely on rigid logic formalisms that do not take into account uncertainty. In this paper, we propose P-NIMBUS, a novel knowledge infusion approach for sensor-based HAR that relies on probabilistic reasoning. A probabilistic ontology is in charge of computing symbolic features that are combined with the features automatically extracted by a CNN model from raw sensor data and high-level context data. In particular, the symbolic features encode probabilistic common-sense knowledge about the activities consistent with the user’s surrounding context. These features are infused within the model before the classification layer. We experimentally evaluated P-NIMBUS on a HAR dataset of mobile devices sensor data that includes 14 different activities performed by 25 users. Our results show that P-NIMBUS outperforms state-of-the-art neuro-symbolic approaches, with the advantage of requiring a limited amount of training data to reach satisfying recognition rates (i.e., more than 80% of F1-score with only 20% of labeled data).

The recognition of human activities in sensorized smart-home environments enables a wide variety of healthcare applications, including the detection of early symptoms of cognitive decline. The most effective Human Activity Recognition (HAR) methods are based on supervised Deep Learning classifiers. Those models are usually considered as black boxes, and the rationale behind their decisions is difficult to understand for human beings. The recent advances in eXplainable Artificial Intelligence (XAI) offer promising tools to make HAR models more transparent. The state-of-the-art explainable HAR methods provide explanations for the output of classifiers that periodically predict the performed activity on short time windows (usually in the range of 15-60 seconds). However, non-technical users may be more interested in investigating explanations associated with complete activity instances (e.g., an instance of the cooking activity may last 30 minutes). Unfortunately, temporally extending the time window harms the recognition rate of HAR classifiers. In this paper, we propose DeXAR++: a novel method that generates explanations for human activity instances based on deep learning classifiers. The sensor data time windows used for classification are encoded as images. DeXAR++ aggregates the explanations generated by a computer-vision XAI approach on each time window to obtain a single explanation for approximated activity instances. Moreover, DeXAR++ includes a novel visualization approach particularly suitable for non-expert users. We evaluate DeXAR++ with both automatic and user-based evaluation methodologies on a public dataset of activities performed in smart-home environments, showing that our results outperform the ones obtained by state-of-the-art methods.

Neuro-symbolic AI methods aim at integrating the capabilities of data-driven deep learning solutions with the ones of more traditional symbolic approaches. These techniques have been poorly explored in the sensor-based Human Activity Recognition (HAR) research field, even if they could lead to multiple benefits such as improving model interpretability and reducing the amount of labeled data that is necessary to reliably train the model. In this paper, we propose DUSTIN, a novel knowledge infusion approach for sensor-based HAR. DUSTIN concatenates the features automatically extracted by a CNN model from raw sensor data and high-level context data with the ones inferred by a knowledge-based reasoner. In particular, the symbolic features encode common-sense knowledge about the activities which are consistent with the context of the user, and they are infused within the model before the classification layer. We experimentally evaluated DUSTIN on a HAR dataset of mobile devices sensor data that includes 14 different activities performed by 26 users. Our results show that DUSTIN outperforms state-of-the-art neuro-symbolic approaches, with the advantage of requiring a limited amount of training data and training epochs to reach satisfying recognition rates.

The sensor-based detection of Activities of Daily Living (ADLs) in smart home environments can be exploited to provide healthcare applications, like remotely monitoring fragile subjects living in their habitations. However, ADLs recognition methods have been mainly investigated with a focus on singleinhabitant scenarios. The major problem in multi-inhabitant settings is data association: assigning to each resident the environmental sensors' events that he/she triggered. Furthermore, Deep Learning (DL) solutions have been recently explored for ADLs recognition, with promising results. Nevertheless, the main drawbacks of these methods are their need for large amounts of training data, and their lack of interpretability. This paper summarizes some contributions of my Ph.D. research, in which we are designing explainable multi-inhabitant approaches for ADLs recognition. We have already investigated a hybrid knowledge- and data-driven solution that exploits the high-level context of each resident to perform data association. Currently, we are studying semi-supervised techniques to mitigate the data scarcity issue, and explainable Artificial Intelligence (XAI) methods to make DL classifiers for ADLs more transparent.

Recognizing the activities of daily living (ADLs) in multi-inhabitant settings is a challenging task. One of the major challenges is the so-called data association problem: how to assign to each user the environmental sensor events that he/she actually triggered? In this paper, we tackle this problem with a contextaware approach. Each user in the home wears a smartwatch, which is used to gather several high-level context information, like the location in the home (thanks to a micro-localization infrastructure) and the posture (e.g., sitting or standing). Context data is used to associate sensor events to the users which more likely triggered them. We show the impact of context reasoning in our framework on a dataset where up to 4 subjects perform ADLs at the same time (collaboratively or individually). We also report our experience and the lessons learned in deploying a running prototype of our method.