In the last few years there has been a growing interest in Human Activity Recognition (HAR) topic. Sensor-based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming-based applications such as online HAR, has emerged over the past decades. However, proposing sufficiently robust online activity recognition approach in smart environment setting is still considered as a remarkable challenge. This paper presents a novel online application of Hierarchical Hidden Markov Model in order to detect the current activity on the live streaming of sensor events. Our method consists of two phases. In the first phase, data stream is segmented based on the beginning and ending of the activity patterns. Also, on-going activity is reported with every receiving observation. This phase is implemented using Hierarchical Hidden Markov models. The second phase is devoted to the correction of the provided label for the segmented data stream based on statistical features. The proposed model can also discover the activities that happen during another activity - so-called interrupted activities. After detecting the activity pane, the predicted label will be corrected utilizing statistical features such as time of day at which the activity happened and the duration of the activity. We validated our proposed method by testing it against two different smart home datasets and demonstrated its effectiveness, which is competing with the state-of-the-art methods.
Authors in @cite_7 @cite_0 present a novel probabilistic method to determine the window size. A different window size is initialized regarding each class of activity based on prior estimation and it is getting updated by the upcoming sensor events.
@cite_0 @cite_7
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In active and assisted living environments, a major service that can be provided is the automated assessment of elderly people’s well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size of the required window for online sensor data streams to recognize a specific activity, especially when new sensor events are recorded. This paper proposes a windowing algorithm, which presents promising results to recognize complex human activities for multi-resident homes. The approach is based on the analysis of the sensor data to identify the best fitting sensors that should be considered in a specified window. Moreover, the second part of this paper proposes a set of different statistical spatio-temporal features to recognize human activities. In order to check the overall performance, this approach is tested using the CASAS data set and artificially generated laboratory data using our HBMS simulator. The results show high performance based on different evaluation metrics compared to other approaches. We believe that the proposed windowing approach provides a good approximation of the required window size in order to robustly detect human activities in comparison to other windowing approaches. Determining the appropriate data window size for online sensor data streams to recognize a specific activity is still a challenging task. In particular, when new sensor events are recorded. This paper proposes a windowing algorithm which presents promising results to recognize complex activities, e.g., in a smart home environment. The underlying basic idea is to analyze the sensor data in order to identify the set of “best fitting sensors”: it contains those sensors that most contribute to the recognition task, and therefore should be considered in a window. To validate our approach, we applied it on the CASAS data set which is an international data set for activity recognition. Based on the promising results, we believe that this algorithm can assist to detect human activities. Thus, our approach might be used in Active and Assisted Living Environments (AAL), where activity recognition is required to distinguish the types of help, a person needs to master his her daily life activities.