In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets (i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68 ). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.
Human Activity recognition has been a popular research topic in pervasive computing @cite_42 for its competence in learning profound high-level knowledge about human activity from raw sensor inputs. Several survey articles have elaborated the recent advance of activity recognition using conventional machine learning @cite_42 @cite_12 and deep learning @cite_3 approaches.
@cite_42 @cite_3 @cite_12
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Activity recognition is fundamental to many of the services envisaged in pervasive computing and ambient intelligence scenarios. However, delivering sufficiently robust activity recognition systems that could be deployed with confidence in an arbitrary real-world setting remains an outstanding challenge. Developments in wireless, inexpensive and unobtrusive sensing devices have enabled the capture of large data volumes, upon which a variety of machine learning techniques have been applied in order to facilitate interpretation of and inference upon such data. Much of the documented research in this area has in the main focused on recognition across pre-segmented sensor data. Such approaches are insufficient for near real-time analysis as is required for many services, such as those envisaged by ambient assisted living. This paper presents a novel near real-time sensor segmentation approach that incorporates the notions of both sensor and time correlation. Abstract To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90 accuracy by obtaining k based on Calinski–Harabasz index, or by choosing appropriate values for ɛ and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand. Abstract Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research. With the rapid development of wearable sensors and pervasive computing technologies including smartphones, acceleration-based human activity recognition is receiving increased attention for medical research applications. Motivated by the “weightlessness” feature, here we apply a bidirectional feature during the feature extraction phase of activity recognition; however, since the bidirectional feature has two components, they cannot simply be concatenated into a long vector, but can be naturally treated as a second-order tensor. Therefore, we propose a new tensor-based feature selection method termed tensor manifold discriminant projections (TMDP). TMDP simultaneously considers: 1) applying an optimization criterion that can directly process the tensor spectral analysis problem, thereby decreasing the computational cost compared to traditional tensor-based feature selection methods; 2) extracting local rank information by finding a tensor subspace that preserves the rank order information of the within-class input samples; and 3) extracting discriminant information by maximizing the sum of distances between every sample and their interclass sample mean. Experiments on the naturalistic mobile devices-based human activity 2.0 dataset are performed to demonstrate the effectiveness and robustness of TMDP.