Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals

• URL: http://arxiv.org/abs/2112.11224v1
• Date: Mon, 20 Dec 2021 17:00:27 GMT
• Title: Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals
• Authors: Wenjin Tao, Haodong Chen, Md Moniruzzaman, Ming C. Leu, Zhaozheng Yi, Ruwen Qin
• Abstract summary: We propose a novel attention-based approach to human activity recognition using multiple IMU sensors worn at different body locations. An attention-based fusion mechanism is developed to learn the importance of sensors at different body locations and to generate an attentive feature representation. The proposed approach is evaluated using five public datasets and it outperforms state-of-the-art methods on a wide variety of activity categories.
• Score: 4.558966602878624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human Activity Recognition (HAR) using wearable devices such as smart watches embedded with Inertial Measurement Unit (IMU) sensors has various applications relevant to our daily life, such as workout tracking and health monitoring. In this paper, we propose a novel attention-based approach to human activity recognition using multiple IMU sensors worn at different body locations. Firstly, a sensor-wise feature extraction module is designed to extract the most discriminative features from individual sensors with Convolutional Neural Networks (CNNs). Secondly, an attention-based fusion mechanism is developed to learn the importance of sensors at different body locations and to generate an attentive feature representation. Finally, an inter-sensor feature extraction module is applied to learn the inter-sensor correlations, which are connected to a classifier to output the predicted classes of activities. The proposed approach is evaluated using five public datasets and it outperforms state-of-the-art methods on a wide variety of activity categories.

Related papers

• 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)
• Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes [0.0]
  We propose a novel graph-guided neural network approach for Human Activity Recognition (HAR) in smart homes. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms.
  arXiv  Detail & Related papers  (2023-11-16T02:43:13Z)
• 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)
• 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)
• UMSNet: An Universal Multi-sensor Network for Human Activity Recognition [10.952666953066542]
  This paper proposes a universal multi-sensor network (UMSNet) for human activity recognition. In particular, we propose a new lightweight sensor residual block (called LSR block), which improves the performance. Our framework has a clear structure and can be directly applied to various types of multi-modal Time Series Classification tasks.
  arXiv  Detail & Related papers  (2022-05-24T03:29:54Z)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition [9.358282765566807]
  We propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model. It aligns the data of each sensor separately to achieve local alignment, while uniformly aligning the data of all sensors to ensure global alignment. Experiments are conducted on two public WHAR datasets, and the experimental results show that our model can yield a competitive performance.
  arXiv  Detail & Related papers  (2021-08-17T13:45:32Z)
• SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
  We present SensiX, a personal edge platform that stays between sensor data and sensing models. We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
  arXiv  Detail & Related papers  (2020-12-04T23:06:56Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)
• Human Activity Recognition from Wearable Sensor Data Using Self-Attention [2.9023633922848586]
  We present a self-attention based neural network model for activity recognition from body-worn sensor data. We performed experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-out-subject evaluation.
  arXiv  Detail & Related papers  (2020-03-17T14:16:57Z)
• Learning Selective Sensor Fusion for States Estimation [47.76590539558037]
  We propose SelectFusion, an end-to-end selective sensor fusion module. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities. We extensively evaluate all fusion strategies in both public datasets and on progressively degraded datasets.
  arXiv  Detail & Related papers  (2019-12-30T20:25:16Z)

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.