Methods from the area of Process Mining traditionally focus on extracting insight in business processes from event logs. In this paper we explore the potential of Process Mining to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from smart home environments. Events in smart home environments are recorded at the level of sensor triggers, which is too low to mine habit-related behavioral patterns. Process discovery algorithms produce then overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts. We show that abstracting the events to a higher-level interpretation can enable discovery of more precise and more comprehensible models. We present a framework to automatically abstract sensor-level events to their interpretation at the human activity level. Our framework is based on the XES IEEE standard for event logs. We use supervised learning techniques to train it on training data for which both the sensor and human activity events are known. We demonstrate our abstraction framework on three real-life smart home event logs and show that the process models that can be discovered after abstraction improve on precision as well as on F-score.
Activity recognition methods generally operate on discrete time windows over the time series of continuous-valued sensor values and aim to map each time window onto the correct type of human activity, e.g. or . Activity recognition methods can be classified into probabilistic approaches @cite_4 @cite_24 @cite_28 @cite_5 and ontological reasoning approaches @cite_23 @cite_21 . The advantage of probabilistic approaches over ontological reasoning approaches is their ability to handle noisy, uncertain and incomplete sensor data @cite_23 .
@cite_4 @cite_28 @cite_21 @cite_24 @cite_23 @cite_5
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Usually, approaches driven by data proposed in literature for sensor-based activity recognition use the begin label and the end label of each activity in the dataset, fixing a temporal window with sensor data events to identify the activity carried out in this window. This type of approach cannot be carried out in real time because it is not possible to predict the start time of an activity, i.e., the class of the future activity that an inhabitant will perform, neither when he she will begin to carry out this activity. However, an activity can be marked as finished in real time only with the previous observations. Therefore, there is a need of online activity recognition approaches that classify activities using only the end label of the activity. In this paper, we propose and evaluate a new approach for online activity recognition with three temporal sub-windows that uses only the end label of the activity. The advantage of our approach is that the temporal sub-windows keep a partial order in the sensor data stream from the end time of the activity in a short-term, medium-term, long-term. The experiments conducted to evaluate our approach suggest the importance of the use of temporal sub-windows versus a single temporal window in terms of accuracy, using only the end time of the activity. The use of temporal sub-windows has improved the accuracy in the 98.95 of experiments carried out. Daily living activity recognition can be exploited to benefit mobile and ubiquitous computing applications. Techniques so far are mature to recognize simple actions. Due to the characteristics of diversity and uncertainty in daily living applications, most existing complex activity recognition approaches have notable limitations. First, graphical model-based approaches still lack sufficient expressive power to model rich temporal relations among activities. Second, it would be rather difficult for graphical model-based approaches to build a unified model for achieving multiple types of tasks. Third, current semantic-based approaches often fail to capture uncertainties. Fourth, formulae in these semantic-based approaches are often manually encoded. Meanwhile, it is impractical to handcraft each formula accurately in daily living scenarios where temporal relations among activities are intricate. To address these issues, we present a probabilistic semantic-based framework that combines Markov logic network with 15 temporal and hierarchical relations to explicitly perform diverse inference tasks of daily living in a unified manner. Advanced pattern mining techniques are introduced to automatically learn the propositional logic rules of intricate relations as well as their weights. Experimental results show that by logical reasoning with the mined temporal dependencies under uncertainty, the proposed model leads to an improved performance, particularly when recognizing complex activities involving the incomplete or incorrect observations of atomic actions. HighlightsA probabilistic semantic-based framework is presented to explicitly perform diverse inference tasks.The framework combines Markov logic networks with 15 relations between activities.Advanced pattern mining techniques are introduced to learn intricate temporal rules.Performances can be improved by logical reasoning with the mined rules under uncertainty.Our approach is robust to the incomplete or incorrect observations of intervals. A majority of the approaches to activity recognition in sensor environments are either based on manually constructed rules for recognizing activities or lack the ability to incorporate complex temporal dependencies. Furthermore, in many cases, the rather unrealistic assumption is made that the subject carries out only one activity at a time. In this paper, we describe the use of Markov logic as a declarative framework for recognizing interleaved and concurrent activities incorporating both input from pervasive light-weight sensor technology and common-sense background knowledge. In particular, we assess its ability to learn statistical-temporal models from training data and to combine these models with background knowledge to improve the overall recognition accuracy. To this end, we propose two Markov logic formulations for inferring the foreground activity as well as each activities' start and end times. We evaluate the approach on an established dataset. where it outperforms state-of-the-art algorithms for activity recognition. Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83 in all experiments for real time activity recognition, which proves the approach and the underlying model.