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.
Researchers in @cite_4 presented a multi-stage classification method. The first stage is to cluster the activities using a Hidden Markov Model based on location data and then in the next stage, another HMM classifies the exact activity using a sequence of sensor data. The major weakness of this method is that it makes no attempt to specify the boundaries of activities which negatively affects the performance.
@cite_4
1972250859 1981515531 2019586054 2105414191
In unsupervised classification, Hidden Markov Models (HMM) are used to account for a neighborhood structure between observations. The emission distributions are often supposed to belong to some parametric family. In this paper, a semiparametric model where the emission distributions are a mixture of parametric distributions is proposed to get a higher flexibility. We show that the standard EM algorithm can be adapted to infer the model parameters. For the initialization step, starting from a large number of components, a hierarchical method to combine them into the hidden states is proposed. Three likelihood-based criteria to select the components to be combined are discussed. To estimate the number of hidden states, BIC-like criteria are derived. A simulation study is carried out both to determine the best combination between the combining criteria and the model selection criteria and to evaluate the accuracy of classification. The proposed method is also illustrated using a biological dataset from the model plant Arabidopsis thaliana. A R package HMMmix is freely available on the CRAN. This paper presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. We develop a novel framework that contains simple pre- and post-classification strategies to improve the overall performance. We achieve this through class-imbalance correction on the learning data using structure preserving oversampling (SPO), leveraging the sequential nature of sensory data using smoothing of the predicted label sequence and classifier fusion, respectively. Through evaluation on recent publicly available activity datasets comprising of a large amount of multi-dimensional sensory data, we demonstrate that our proposed strategies are effective in improving classification performance over common techniques such as One Nearest Neighbor (1NN) and Support Vector Machines (SVM). Our framework also shows better performance over sequential probabilistic models, such as Conditional Random Field (CRF) and Hidden Markov Model (HMM) and when these models are used as meta-learners. Hidden Markov models (HMMs) provide joint segmentation and classification of sequential data by efficient inference algorithms and have therefore been employed in fields as diverse as speech recognition, document processing, and genomics. However, conventional HMMs do not suit action segmentation in video due to the nature of the measurements which are often irregular in space and time, high dimensional and affected by outliers. For this reason, in this paper we present a joint action segmentation and classification approach based on an extended model: the hidden Markov model for multiple, irregular observations (HMM-MIO). Experiments performed over a concatenated version of the popular KTH action dataset and the challenging CMU multi-modal activity dataset (CMU-MMAC) report accuracies comparable to or higher than those of a bag-of-features approach, showing the usefulness of improved sequential models for joint action segmentation and classification tasks. We present AALO: a novel Activity recognition system for single person smart homes using Active Learning in the presence of Overlapped activities. AALO applies data mining techniques to cluster in-home sensor firings so that each cluster represents instances of the same activity. Users only need to label each cluster as an activity as opposed to labeling all instances of all activities. Once the clusters are associated to their corresponding activities, our system can recognize future activities. To improve the activity recognition accuracy, our system preprocesses raw sensor data by identifying overlapping activities. The evaluation of activity recognition performance on a 26-day dataset shows that compared to Naive Bayesian (NB), Hidden Markov Model (HMM), and Hidden Semi Markov Model (HSMM) based activity recognition systems, our average time slice error (24.15 ) is much lower than NB (53.04 ), and similar to HMM (29.97 ) and HSMM (26.29 ). Thus, our active learning based approach performs as good as the state of the art supervised techniques (HMM and HSMM).