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Recent advances in machine learning and deep neural networks have led to the realization of many important applications in the area of personalized medicine. Whether it is detecting activities of daily living or analyzing images for cancerous cells, machine learning algorithms have become the dominant choice for such emerging applications. In particular, the state-of-the-art algorithms used for human activity recognition (HAR) using wearable inertial sensors utilize machine learning algorithms to detect health events and to make predictions from sensor data. Currently, however, there remains a gap in research on whether or not and how activity recognition algorithms may become the subject of adversarial attacks. In this paper, we take the first strides on (1) investigating methods of generating adversarial example in the context of HAR systems; (2) studying the vulnerability of activity recognition models to adversarial examples in feature and signal domain; and (3) investigating the effects of adversarial training on HAR systems. We introduce Adar, a novel computational framework for optimization-driven creation of adversarial examples in sensor-based activity recognition systems. Through extensive analysis based on real sensor data collected with human subjects, we found that simple evasion attacks are able to decrease the accuracy of a deep neural network from 95.1% to 3.4% and from 93.1% to 16.8% in the case of a convolutional neural network. With adversarial training, the robustness of the deep neural network increased on the adversarial examples by 49.1% in the worst case while the accuracy on clean samples decreased by 13.2%.
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On Influencing Factors in Human Activity Recognition Using Wireless Networks
Driven by the development of machine learning and the development of wireless techniques, lots of research efforts have been spent on the human activity recognition (HAR). Although various deep learning algorithms can achieve high accuracy for recognizing human activities, existing works lack of a theoretical performance upper bound which is the best accuracy that is only limited by the influencing factors in wireless networks such as indoor physical environments and settings of wireless sensing devices regardless of any HAR algorithm. Without the understanding of performance upper bound, mistakenly configuring the influencing factors can reduce the HAR accuracy drastically no matter what deep learning algorithms are utilized. In this paper, we propose the HAR performance upper bound which is the minimum classification error probability that doesn't depend on any HAR algorithms and can be considered as a function of influencing factors in wireless sensing networks for CSI based human activity recognition. Since the performance upper bound can capture the impacts of influencing factors on HAR accuracy, we further analyze the influences of those factors with varying situations such as through the wall HAR and different human activities by MATLAB simulations.
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- Award ID(s):
- 1652502
- PAR ID:
- 10163054
- Date Published:
- Journal Name:
- 2019 IEEE Global Communications Conference (GLOBECOM)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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End-to-end deep learning models are increasingly applied to safety-critical human activity recognition (HAR) applications, e.g., healthcare monitoring and smart home control, to reduce developer burden and increase the performance and robustness of prediction models. However, integrating HAR models in safety-critical applications requires trust, and recent approaches have aimed to balance the performance of deep learning models with explainable decision-making for complex activity recognition. Prior works have exploited the compositionality of complex HAR (i.e., higher-level activities composed of lower-level activities) to form models with symbolic interfaces, such as concept-bottleneck architectures, that facilitate inherently interpretable models. However, feature engineering for symbolic concepts-as well as the relationship between the concepts-requires precise annotation of lower-level activities by domain experts, usually with fixed time windows, all of which induce a heavy and error-prone workload on the domain expert. In this paper, we introduce X-CHAR, an eXplainable Complex Human Activity Recognition model that doesn't require precise annotation of low-level activities, offers explanations in the form of human-understandable, high-level concepts, while maintaining the robust performance of end-to-end deep learning models for time series data. X-CHAR learns to model complex activity recognition in the form of a sequence of concepts. For each classification, X-CHAR outputs a sequence of concepts and a counterfactual example as the explanation. We show that the sequence information of the concepts can be modeled using Connectionist Temporal Classification (CTC) loss without having accurate start and end times of low-level annotations in the training dataset-significantly reducing developer burden. We evaluate our model on several complex activity datasets and demonstrate that our model offers explanations without compromising the prediction accuracy in comparison to baseline models. Finally, we conducted a mechanical Turk study to show that the explanations provided by our model are more understandable than the explanations from existing methods for complex activity recognition.more » « less
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Hedden, Abigail S; Mazzaro, Gregory J (Ed.)Human activity recognition (HAR) with radar-based technologies has become a popular research area in the past decade. However, the objective of these studies are often to classify human activity for anyone; thus, models are trained using data spanning as broad a swath of people and mobility profiles as possible. In contrast, applications of HAR and gait analysis to remote health monitoring require characterization of the person-specific qualities of a person’s activities and gait, which greatly depends on age, health and agility. In fact, the speed or agility with which a person moves can be an important health indicator. In this study, we propose a multi-input multi-task deep learning framework to simultaneously learn a person’s activity and agility. In this initial study, we consider three different agility states: slow, nominal, and fast. It is shown that joint learning of agility and activity improves the classification accuracy for both activity and agility recognition tasks. To the best of our knowledge, this study is the first work considering both agility characterization and personalized activity recognition using RF sensing.more » « less
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Human activity recognition (HAR) is the process of using mobile sensor data to determine the physical activities performed by individuals. HAR is the backbone of many mobile healthcare applications, such as passive health monitoring systems, early diagnosing systems, and fall detection systems. Effective HAR models rely on deep learning architectures and big data in order to accurately classify activities. Unfortunately, HAR datasets are expensive to collect, are often mislabeled, and have large class imbalances. State-of-the-art approaches to address these challenges utilize Generative Adversarial Networks (GANs) for generating additional synthetic data along with their labels. Problematically, these HAR GANs only synthesize continuous features — features that are represented by real numbers — recorded from gyroscopes, accelerometers, and other sensors that produce continuous data. This is limiting since mobile sensor data commonly has discrete features that provide additional context such as device location and the time-of-day, which have been shown to substantially improve HAR classification. Hence, we studied Conditional Tabular Generative Adversarial Networks (CTGANs) for data generation to synthesize mobile sensor data containing both continuous and discrete features, a task never been done by state-of-the-art approaches. We show HAR-CTGANs generate data with greater realism resulting in allowing better downstream performance in HAR models, and when state-of-the-art models were modified with HAR-CTGAN characteristics, downstream performance also improves.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOBECOM38437.2019.9014016
