ActiveHARNet

Gudur, Gautham Krishna; Sundaramoorthy, Prahalathan; Umaashankar, Venkatesh

doi:10.1145/3325413.3329790

Cited by 27 publications

(5 citation statements)

References 19 publications

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“…To address these challenges, recent research has made use of on-device learning and inference. In [38,39], models that support on-device incremental learning and inference-leading to a significant efficiency boost during inference-were proposed. A dynamic active learning-based approach that selects informative samples and identifies new activities to improve performance and reduce annotation cost was proposed in [35].…”

Section: Privacy-preserving On-device Approachesmentioning

confidence: 99%

HARE: Unifying the Human Activity Recognition Engineering Workflow

Konak,

van de Water,

Döring

et al. 2023

Sensors

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Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.

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Section: Privacy-preserving On-device Approachesmentioning

confidence: 99%

HARE: Unifying the Human Activity Recognition Engineering Workflow

Konak,

van de Water,

Döring

et al. 2023

Sensors

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“…AL with the advantage of less input labeled data requirement has been used with deep models to gain the strength of deep learning. The research combined AL with deep learning e.g., DeActive [66,67], ActiveHARNet [68] proposed a deep and AL-enabled activity recognition model, and their experimentation on real-world data showed optimum accuracy using less labeled instances. Bettini et.…”

Section: Semi-supervised Approachesmentioning

confidence: 99%

Less is more: Efficient behavioral context recognition using Dissimilarity-Based Query Strategy

Akram¹,

Farhan²,

Basharat³

2023

PLoS ONE

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With the advancement of ubiquitous computing, smartphone sensors are generating a vast amount of unlabeled data streams ubiquitously. This sensor data can potentially help to recognize various behavioral contexts in the natural environment. Accurate behavioral context recognition has a wide variety of applications in many domains like disease prevention and independent living. However, despite the availability of enormous amounts of sensor data, label acquisition, due to its dependence on users, is still a challenging task. In this work, we propose a novel context recognition approach i.e., Dissimilarity-Based Query Strategy (DBQS). Our approach DBQS leverages Active Learning based selective sampling to find the informative and diverse samples in the sensor data to train the model. Our approach overcomes the stagnation problem by considering only new and distinct samples from the pool that were not previously explored. Further, our model exploits temporal information in the data in order to further maintain diversity in the dataset. The key intuition behind the proposed approach is that the variations during the learning phase will train the model in diverse settings and it will outperform when assigned a context recognition task in the natural setting. Experimentation on a publicly available natural environment dataset demonstrates that our proposed approach improved overall average Balanced Accuracy(BA) by 6% with an overall 13% less training data requirement.

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“…In addition, Stickic, M. et al conducted comparisons through active and semi-supervised learning to reduce the labeling of PLCouple1 datasets for behavior recognition [ 24 ]. Gudur, G. K. et al conducted a study applying active learning to reduce labeling of HAR data, resulting in good performance in reduced labeling [ 25 ]. However, deep learning models have yet to be applied to models for prediction.…”

Section: Related Researchmentioning

confidence: 99%

Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

Ashiquzzaman

Lee

et al. 2021

Sensors

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In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

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ActiveHARNet

Cited by 27 publications

References 19 publications

HARE: Unifying the Human Activity Recognition Engineering Workflow

HARE: Unifying the Human Activity Recognition Engineering Workflow

Less is more: Efficient behavioral context recognition using Dissimilarity-Based Query Strategy

Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

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