Automatic systems to monitor people and subsequently improve people's lives have been emerging in the last few years, and currently, they are capable of identifying many activities of daily living (ADLs). An important field of research in this context is the monitoring of health risks and the identification of falls. It is estimated that every year, one in three persons older than 65 years will fall, and fall events are associated with high mortality rates among the elderly. We propose an anomaly identification framework to detect falls, which incorporates a spatial-temporal convolutional graph network (ST-GCN) as a feature extractor and uses an encoder process to reconstruct ADLs and identify falls as anomalies. As the publicly available fall datasets are few and generally unbalanced, training a reliable model using approaches that need explicit labeling is challenging. Thus, a focus on learning without external supervision is desirable. Treating a fall as an exception of ADLs allows us to recognize falls as anomalies without explicit labels. Given its modular architecture, our framework can robustly represent visual information and use the encoder's reconstruction error to identify falls as anomalies. We assess our framework's ability to recognize falls by training it with only ADLs. We perform three types of experiments: single dataset training and evaluation that consists of separate 90% of the data to train the model 5% to adjust the model, and the rest to the test. A joint dataset experiment, where we combine two datasets to increase the number of samples our model is trained on, and a cross-dataset evaluation, where we train on one dataset and evaluate using another one. Besides presenting state-of-the-art results on our experiments, particularly on the cross-dataset one, the model also presents a low number of false events, which makes it an ideal candidate for real-world application.

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Autonomous control for unmanned aerial vehicles (UAVs) has rapidly raised research interest in the last decade. Widely used in civilian, military, and private areas, UAVs have been implemented in surveillance, search and rescue, and delivery tasks, solving problems where a significant space must be covered and traveled. However, using UAVs for navigation problems with full autonomy in control, planning, localization, and mapping tasks remains an open challenge. This paper presents a systematic literature review on object-goal navigation using autonomous UAVs. Object-goal navigation inherits the same navigation challenges, such as control and navigation, in addition to estimating the target's location. The object-search problem requires to understand what object must be found and where it can be located.In this regard, survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. This review reveals essential issues related to autonomous navigation task dependencies and gaps in UAV development and framework standardization. Open challenges for autonomous UAV control under object-goal navigation must address the research on finding methods for problems, such as autonomy level and comparison metrics, considering safety, ethics, and legal implications.