RunnerDNA: Interpretable indicators and model to characterize human activity pattern and individual difference

• URL: http://arxiv.org/abs/2201.07370v1
• Date: Wed, 19 Jan 2022 01:09:30 GMT
• Title: RunnerDNA: Interpretable indicators and model to characterize human activity pattern and individual difference
• Authors: Yao Yao, Zhuolun Wang, Peng Luo, Hanyu Yin, Ziqi Liu, Jiaqi Zhang, Nengjing Guo, Qingfeng Guan
• Abstract summary: The concept of RunnerDNA, consisting of five interpretable indicators, balance, stride, steering, stability, and amplitude, was proposed to describe human activity at the individual level. We collected smartphone multi-sensor data from 33 volunteers who engaged in physical activities such as walking, running, and bicycling. The indicators were then used to build random forest models and recognize movement activities and the identity of users.
• Score: 8.820303797376752
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human activity analysis based on sensor data plays a significant role in behavior sensing, human-machine interaction, health care, and so on. The current research focused on recognizing human activity and posture at the activity pattern level, neglecting the effective fusion of multi-sensor data and assessing different movement styles at the individual level, thus introducing the challenge to distinguish individuals in the same movement. In this study, the concept of RunnerDNA, consisting of five interpretable indicators, balance, stride, steering, stability, and amplitude, was proposed to describe human activity at the individual level. We collected smartphone multi-sensor data from 33 volunteers who engaged in physical activities such as walking, running, and bicycling and calculated the data into five indicators of RunnerDNA. The indicators were then used to build random forest models and recognize movement activities and the identity of users. The results show that the proposed model has high accuracy in identifying activities (accuracy of 0.679) and is also effective in predicting the identity of running users. Furthermore, the accuracy of the human activity recognition model has significant improved by combing RunnerDNA and two motion feature indicators, velocity, and acceleration. Results demonstrate that RunnerDNA is an effective way to describe an individual's physical activity and helps us understand individual differences in sports style, and the significant differences in balance and amplitude between men and women were found.

Related papers

• CLAN: A Contrastive Learning based Novelty Detection Framework for Human Activity Recognition [3.0108863071498035]
  CLAN is a two-tower contrastive learning-based novelty detection framework for human activity recognition. It is tailored to challenges with human activity characteristics, including the significance of temporal and frequency features. Experiments on four real-world human activity datasets show that CLAN surpasses the best performance of existing novelty detection methods.
  arXiv  Detail & Related papers  (2024-01-17T03:57:36Z)
• Generating Human-Centric Visual Cues for Human-Object Interaction Detection via Large Vision-Language Models [59.611697856666304]
  Human-object interaction (HOI) detection aims at detecting human-object pairs and predicting their interactions. We propose three prompts with VLM to generate human-centric visual cues within an image from multiple perspectives of humans. We develop a transformer-based multimodal fusion module with multitower architecture to integrate visual cue features into the instance and interaction decoders.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data [2.398608007786179]
  We present an unsupervised approach to project the human activities into an embedding space in which similar activities will be located closely together. Results of experiments on three labeled benchmark datasets demonstrate the effectiveness of the framework.
  arXiv  Detail & Related papers  (2023-07-21T08:52:47Z)
• 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)
• 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)
• Multi-Channel Time-Series Person and Soft-Biometric Identification [65.83256210066787]
  This work investigates person and soft-biometrics identification from recordings of humans performing different activities using deep architectures. We evaluate the method on four datasets of multi-channel time-series human activity recognition (HAR) Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.
  arXiv  Detail & Related papers  (2023-04-04T07:24:51Z)
• 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)
• HHAR-net: Hierarchical Human Activity Recognition using Neural Networks [2.4530909757679633]
  This research aims at building a hierarchical classification with Neural Networks to recognize human activities. We evaluate our model on the Extrasensory dataset; a dataset collected in the wild and containing data from smartphones and smartwatches.
  arXiv  Detail & Related papers  (2020-10-28T17:06:42Z)
• Action similarity judgment based on kinematic primitives [48.99831733355487]
  We investigate to which extent a computational model based on kinematics can determine action similarity. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features.
  arXiv  Detail & Related papers  (2020-08-30T13:58:47Z)
• Human Activity Recognition based on Dynamic Spatio-Temporal Relations [10.635134217802783]
  The description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. We develop a method for human activity recognition that tackles these two issues.
  arXiv  Detail & Related papers  (2020-06-29T15:49:34Z)

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.