Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
- URL: http://arxiv.org/abs/2403.15422v1
- Date: Tue, 12 Mar 2024 22:22:14 GMT
- Title: Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
- Authors: Xiaozhou Ye, Kouichi Sakurai, Nirmal Nair, Kevin I-Kai Wang,
- Abstract summary: Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing.
HAR confronts challenges, particularly in data distribution assumptions.
This review investigates how machine learning addresses data heterogeneity in HAR.
- Score: 0.8142555609235358
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with less annotated data. This review investigates how machine learning addresses data heterogeneity in HAR, by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.
Related papers
- Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery [56.622854875204645]
We present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth gene-gene interactions.
A novel weighted diversified sampling algorithm computes the diversity score of each data sample in just two passes of the dataset.
arXiv Detail & Related papers (2024-10-21T03:35:23Z) - A Comprehensive Survey on Data Augmentation [55.355273602421384]
Data augmentation is a technique that generates high-quality artificial data by manipulating existing data samples.
Existing literature surveys only focus on a certain type of specific modality data.
We propose a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities.
arXiv Detail & Related papers (2024-05-15T11:58:08Z) - Data Augmentation in Human-Centric Vision [54.97327269866757]
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks.
It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection.
Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
arXiv Detail & Related papers (2024-03-13T16:05:18Z) - Representation Learning for Wearable-Based Applications in the Case of
Missing Data [20.37256375888501]
multimodal sensor data in real-world environments is still challenging due to low data quality and limited data annotations.
We investigate representation learning for imputing missing wearable data and compare it with state-of-the-art statistical approaches.
Our study provides insights for the design and development of masking-based self-supervised learning tasks.
arXiv Detail & Related papers (2024-01-08T08:21:37Z) - Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition [3.2319909486685354]
A key problem holding up progress in wearable sensor-based human activity recognition is the unavailability of diverse and labeled training data.
We propose an unsupervised statistical feature-guided diffusion model specifically optimized for wearable sensor-based human activity recognition.
By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data.
arXiv Detail & Related papers (2023-05-30T15:12:59Z) - Dataset Bias in Human Activity Recognition [57.91018542715725]
This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance.
We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
arXiv Detail & Related papers (2023-01-19T12:33:50Z) - Personalized Decentralized Multi-Task Learning Over Dynamic
Communication Graphs [59.96266198512243]
We propose a decentralized and federated learning algorithm for tasks that are positively and negatively correlated.
Our algorithm uses gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other.
We conduct experiments on a synthetic Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset.
arXiv Detail & Related papers (2022-12-21T18:58:24Z) - Video-based Pose-Estimation Data as Source for Transfer Learning in
Human Activity Recognition [71.91734471596433]
Human Activity Recognition (HAR) using on-body devices identifies specific human actions in unconstrained environments.
Previous works demonstrated that transfer learning is a good strategy for addressing scenarios with scarce data.
This paper proposes using datasets intended for human-pose estimation as a source for transfer learning.
arXiv Detail & Related papers (2022-12-02T18:19:36Z) - TASKED: Transformer-based Adversarial learning for human activity
recognition using wearable sensors via Self-KnowledgE Distillation [6.458496335718508]
We propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED)
In the proposed method, we adopt the teacher-free self-knowledge distillation to improve the stability of the training procedure and the performance of human activity recognition.
arXiv Detail & Related papers (2022-09-14T11:08:48Z) - Invariant Feature Learning for Sensor-based Human Activity Recognition [11.334750079923428]
We present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices.
Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset.
arXiv Detail & Related papers (2020-12-14T21:56:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.