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- research-articleSeptember 2024
A Multigrain-Multilabel (MGML) Dataset for Smartphone-Based Human Activity Recognition
AbstractThe efficacy of machine learning-based Human Activity Recognition (HAR) heavily relies on the datasets. Existing benchmark HAR datasets on smartphone accelerometer sensors provide mostly single-labeled, fine-grained activities like walking, ...
- research-articleAugust 2024JUST ACCEPTED
Experience: A Comparative Analysis of Multivariate Time-Series Generative Models: A Case Study on Human Activity Data
Human activity recognition (HAR) is an active research field that has seen great success in recent years due to advances in sensory data collection methods and activity recognition systems. Deep artificial intelligence (AI) models have contributed to the ...
- ArticleAugust 2024
SeWi: A Framework Enhancing CSI-Based Human Activity Recognition
Advanced Intelligent Computing Technology and ApplicationsPages 164–175https://doi.org/10.1007/978-981-97-5594-3_14AbstractWith the widespread application of WiFi devices, utilizing Channel State Information (CSI) for human activity recognition has garnered significant attention. However, the reliability of WiFi sensing signals is often compromised due to the ...
- research-articleAugust 2024JUST ACCEPTED
A Systematic Review of Digital Twin Technology for Home Care
ACM Transactions on Computing for Healthcare (HEALTH), Just Accepted https://doi.org/10.1145/3681797The concept of digital twin has captured significant attention in recent years and its potential application within the domain of home care has been explored in several studies. This review endeavors to provide a comprehensive overview of digital twin ...
- research-articleJune 2024
Input-Adaptation Approach for Human Activity Recognition
PETRA '24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive EnvironmentsPages 369–374https://doi.org/10.1145/3652037.3663947A methodology for Human Activity Recognition using Deep Neural Networks (DNNs) is presented in this paper. The proposed approach introduces a model-driven input adaptation technique that involves the conversion of binary sensor data collected from ...
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- research-articleJune 2024
Many-to-Many Prediction for Effective Modeling of Frequent Label Transitions in Time Series
PETRA '24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive EnvironmentsPages 265–272https://doi.org/10.1145/3652037.3652049Time-series classification is vital in health monitoring and human activity recognition, as well as in areas such as financial forecasting, process control, and a wide array of forecasting tasks. Traditional time-series models segment data into windows ...
- short-paperJune 2024
Gaze-Based Intention Recognition for Human-Robot Collaboration
- Valerio Belcamino,
- Miwa Takase,
- Mariya Kilina,
- Alessandro Carfì,
- Fulvio Mastrogiovanni,
- Akira Shimada,
- Sota Shimizu
AVI '24: Proceedings of the 2024 International Conference on Advanced Visual InterfacesArticle No.: 36, Pages 1–5https://doi.org/10.1145/3656650.3656675This work aims to tackle the intent recognition problem in Human-Robot Collaborative assembly scenarios. Precisely, we consider an interactive assembly of a wooden stool where the robot fetches the pieces in the correct order and the human builds the ...
- ArticleMay 2024
Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling
Advances in Knowledge Discovery and Data MiningPages 3–14https://doi.org/10.1007/978-981-97-2262-4_1AbstractHuman Activity Recognition (HAR) has been studied for decades, from data collection, learning models, to post-processing and result interpretations. However, the inherent hierarchy in the activities remains relatively under-explored, despite its ...
- ArticleMay 2024
TFAugment: A Key Frequency-Driven Data Augmentation Method for Human Activity Recognition
Advances in Knowledge Discovery and Data MiningPages 284–296https://doi.org/10.1007/978-981-97-2238-9_22AbstractData augmentation enhances Human Activity Recognition (HAR) models by diversifying training data through transformations, improving their robustness. However, traditional techniques with random masking pose challenges by introducing randomness ...
- Work in ProgressMay 2024
ModifyAug: Data Augmentation for Virtual IMU Signal based on 3D Motion Modification Used for Real Activity Recognition
CHI EA '24: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing SystemsArticle No.: 243, Pages 1–7https://doi.org/10.1145/3613905.3650806In wearable human activity recognition (HAR), the generation and utilization of virtual IMU data has recently gained attention. The use of virtual data can improve the robustness, effective features, and customized motion types chosen in the HAR system. ...
- research-articleJune 2024
Human Activity Recognition based on Local Linear Embedding and Geodesic Flow Kernel on Grassmann manifolds▪
Expert Systems with Applications: An International Journal (EXWA), Volume 241, Issue Chttps://doi.org/10.1016/j.eswa.2023.122696AbstractHuman Activity Recognition (HAR) plays a crucial role in various applications(e.g., medical treatment, video surveillance and sports monitoring). Transfer learning is a promising solution to cross-domain identification problems in HAR. However, ...
Highlights- Domain transfer solves the problem of missing tags of sample data.
- Identify the most similar source domain among multiple domains to the target domain.
- Manifold dimension reduction removes redundant information from sensor data.
- research-articleJuly 2024
Federated Learning Framework for Human Activity Recognition Using Smartphones
Procedia Computer Science (PROCS), Volume 235, Issue CPages 2069–2078https://doi.org/10.1016/j.procs.2024.04.196AbstractHuman activity recognition plays a vital role in various applications like healthcare, sports, and smart environments. … With the increase in the use of smart wearables and sensors, the concern for privacy has increased drastically in recognizing ...
- research-articleDecember 2023
A multi-resolution fusion approach for human activity recognition from video data in tiny edge devices
AbstractHuman Activity Recognition (HAR) is the process of automatic recognition of Activities of Daily Living (ADL) from human motion data captured in various data modalities by wearable and ambient sensors. Advances in Deep Learning, especially ...
Highlights- A novel two-stream multi-resolution fusion architecture for HAR from video data.
- The context stream inputs the lower-resolution images.
- The fovea stream takes center-cropped portions of the images as inputs.
- Quantizing the ...
- research-articleNovember 2023
Transformer-based models to deal with heterogeneous environments in Human Activity Recognition
Personal and Ubiquitous Computing (PUC), Volume 27, Issue 6Pages 2267–2280https://doi.org/10.1007/s00779-023-01776-3AbstractHuman Activity Recognition (HAR) on mobile devices has been demonstrated to be possible using neural models trained on data collected from the device’s inertial measurement units. These models have used convolutional neural networks (CNNs), long ...
- ArticleFebruary 2024
Convolutional Autoencoder for Vision-Based Human Activity Recognition
AbstractHuman activity recognition (HAR) is a crucial component for many current applications, including those in the healthcare, security, and entertainment sectors. At the current state of the art, deep learning outperforms machine learning with its ...
- research-articleMay 2024
TinyML-Driven On-Device Personalized Human Activity Recognition and Auto-Deployment to Smart Bands
AIMLSystems '23: Proceedings of the Third International Conference on AI-ML SystemsArticle No.: 3, Pages 1–9https://doi.org/10.1145/3639856.3639859Human activity recognition has revolutionized health and fitness monitoring, although significant hindrances still exist. One such difficulty is making a system that considers each person’s unique physical activity features, routines, and preferences to ...
- research-articleOctober 2023
AttenDenseNet for the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge
UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable ComputingPages 569–574https://doi.org/10.1145/3594739.3610757The Suessex-Huawei Locomotion-Transportation (SHL) Recognition Challenge presents a large real-world dataset derived from multimodal smartphone sensors, with the aim of accurately distinguishing between eight different states of locomotion and ...
- research-articleOctober 2023
Toward Pioneering Sensors and Features Using Large Language Models in Human Activity Recognition
UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable ComputingPages 475–479https://doi.org/10.1145/3594739.3610741In this paper, we propose a feature pioneering method using Large Language Models (LLMs). In the proposed method, we use ChatGPT 1 to find new sensor locations and new features. Then we evaluate the machine learning model which uses the found features ...
- research-articleOctober 2023
Human Activity Recognition for Packing Processes using CNN-biLSTM
UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable ComputingPages 439–444https://doi.org/10.1145/3594739.3610735Human activity recognition has several applications in healthcare, sports, and industrial settings. In the latter, it can monitor industry workers and evaluate if the required activities are appropriately performed. In this paper, we employ state-of-the-...
- ArticleSeptember 2023
Evaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition
- Johanna-Karinna García-Restrepo,
- Paola Patricia Ariza-Colpas,
- Shariq Butt-Aziz,
- Marlon Alberto Piñeres-Melo,
- Sumera Naz,
- Emiro De-la-hoz-Franco
Computer Information Systems and Industrial ManagementPages 253–269https://doi.org/10.1007/978-3-031-42823-4_19AbstractOne of the technical aspects that contribute to improving the quality of life for older adults is the automation of physical spaces using sensors and actuators, which facilitates the performance of their daily activities. The interaction between ...