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Found 14 papers, 1 papers with code
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR
We show that the combination of vector quantization of sensor data along with simple text conditioned auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps.
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition
While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive learning. To evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left wrist. The contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3. 22 \% reaching 84. 71 \% compared to the 81. 49 \% of the IMU baseline model.
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR
Machine learning algorithms are improving rapidly, but annotating training data remains a bottleneck for many applications.
Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field
This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line.
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps
We propose PressureTransferNet, a novel method for Human Activity Recognition (HAR) using ground pressure information.
Don't freeze: Finetune encoders for better Self-Supervised HAR
Recently self-supervised learning has been proposed in the field of human activity recognition as a solution to the labelled data availability problem.
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition
The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data.
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Human Activity Recognition
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Generative Adversarial Network
Human Activity Recognition
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FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors
In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration.
The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity Recognition
In the second case, we tried to recognize actions related to manipulating objects and physical collaboration between users by using a wrist-worn HBC sensing unit.
Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition
To mitigate this problem we propose a method that facilitates the use of information from sensors that are only present during the training process and are unavailable during the later use of the system.
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation
In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features.
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition
The proposed network is based on the adversarial encoder-decoder structure with the MMD realign the data distribution over multiple subjects.
Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos
Furthermore we show that by either including a small amount of real sensor data for model calibration or simply leveraging the fact that (in general) we can easily generate much more simulated data from video than we can collect in terms of real sensor data the advantage of real sensor data can be eventually equalized.
Iterative Deep Compression : Compressing Deep Networks for Classification and Semantic Segmentation
With LeNet-5 on MNIST, pruning reduces the number of parameters by 15. 3 times and storage requirement from 1. 7 MB to 0. 006 MB with accuracy loss of 0. 03%.
