MICAR: multi-inhabitant context-aware activity recognition in home environments.

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.

Spontaneous abdominal bleeding associated with SARS-CoV-2 infection: causality or coincidence?

Authors present 6 cases of abdominal bleeding associated with COVID-19, representing 1.35% of all hospitalized COVID-19 patients and hypothesize that there could be, although not very frequently, a relationship between SARS-CoV2 and bleeding. They excluded a side effect of the low molecular weight heparin therapy that all patients underwent during the course of the disease or other possible causes. Alterations of the coagulation state or a weakness of the vascular wall due toa presumed endotheliitis SARS-CoV-2 infection induced, are hypothesized by the authors. Investigation and follow-up for possible hemorrhagic problems in patients with COVID-19 is recommended. In particular, clinicians should be vigilant about retroperitoneal hemorrhage in COVID-19 patients. In addition to the fact that these patients are being treated with anticoagulants, anemia and abdominal pain are the signs that should lead us to suspect this type of haemorrhage. More studies are needed to understand if COVID-19 can be directly associated with bleeding. (www.actabiomedica.it)

SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment.

OBJECTIVE: In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective. METHODS: A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level. RESULTS: We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates.