Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark

• URL: http://arxiv.org/abs/2101.01665v2
• Date: Wed, 6 Jan 2021 09:19:21 GMT
• Title: Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark
• Authors: Reem Abdel-Salam, Rana Mostafa and Mayada Hadhood
• Abstract summary: We conduct an extensive literature review on top-performing techniques in human activity recognition based on wearable sensors. We apply a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.

Related papers

• Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric [56.73624246192218]
  We conduct a pioneering study on human activity AI-generated videos (AGVs) We focus on visual quality evaluation and the identification of semantic distortions. We develop an objective evaluation metric, named AI-Generated Human activity Video Quality metric (GHVQ), to automatically analyze the quality of human activity AGVs.
  arXiv  Detail & Related papers  (2024-11-25T17:58:43Z)
• A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos [81.54357891748087]
  We collect talking head videos generated from four generative methods. We conduct controlled psychophysical experiments on visual quality, lip-audio synchronization, and head movement naturalness. Our experiments validate consistency between model predictions and human annotations, identifying metrics that align better with human opinions than widely-used measures.
  arXiv  Detail & Related papers  (2024-03-11T04:13:38Z)
• A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition [63.26015736148707]
  This paper introduces a novel methodology to resolve the issue of optimal sensor placement for Human Activity Recognition. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach.
  arXiv  Detail & Related papers  (2023-07-06T10:38:14Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors [56.554277096170246]
  We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-the-wild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels.
  arXiv  Detail & Related papers  (2023-05-15T16:02:56Z)
• Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey [1.7205106391379026]
  Machine learning-based wearable human activity recognition (WHAR) models enable the development of smart and connected community applications. The widespread adoption of these WHAR models is impeded by their degraded performance in the presence of data distribution heterogeneities. Traditional machine learning algorithms and transfer learning techniques have been proposed to address the underpinning challenges of handling such data heterogeneities. Domain adaptation is one such transfer learning techniques that has gained significant popularity in recent literature.
  arXiv  Detail & Related papers  (2023-04-07T01:33:42Z)
• 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)
• Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers [0.0]
  Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification. This study investigates a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare.
  arXiv  Detail & Related papers  (2021-03-02T16:50:52Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)
• B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows [1.7639472693362923]
  This paper proposes an open-source framework, named B-HAR, for the definition, standardization, and development of a baseline framework. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.
  arXiv  Detail & Related papers  (2021-01-23T12:42:41Z)
• Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking [27.061240686613182]
  The field of Human Activity Recognition (HAR) focuses on obtaining and analysing data captured from monitoring devices (e.g. sensors) We address the issue of accurately recognising human activities using different Machine Learning (ML) techniques.
  arXiv  Detail & Related papers  (2020-05-15T00:46:55Z)

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.