Search Results for author:
Found 46 papers, 7 papers with code
BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation
Once the student model is trained, the model only takes as inputs the BLE-RSSI data for inference, retaining the advantages of ubiquity and low cost of BLE RSSI.
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.
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey
To this end, we first sorted the explorations into three domains according to the involved body forms: body-part electric field, whole-body electric field, and body-to-body electric field, and enumerated the state-of-art works in the domains with a detailed survey of the backed sensing tricks and targeted applications.
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition
Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors.
The Power of Training: How Different Neural Network Setups Influence the Energy Demand
This work offers a heuristic evaluation of the effects of variations in machine learning training regimes and learning paradigms on the energy consumption of computing, especially HPC hardware with a life-cycle aware perspective.
Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks
The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works.
A Novel Local-Global Feature Fusion Framework for Body-weight Exercise Recognition with Pressure Mapping Sensors
We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps.
Unreflected Acceptance -- Investigating the Negative Consequences of ChatGPT-Assisted Problem Solving in Physics Education
In a study, students with a background in physics were assigned to solve physics exercises, with one group having access to an internet search engine (N=12) and the other group being allowed to use ChatGPT (N=27).
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.
Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries
Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications.
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.
Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods
To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap.
SynthCal: A Synthetic Benchmarking Pipeline to Compare Camera Calibration Algorithms
Accurate camera calibration is crucial for various computer vision applications.
Origami Single-end Capacitive Sensing for Continuous Shape Estimation of Morphing Structures
It was observed through embedding areas of origami structures with conductive materials as single-end capacitive sensing patches, that the sensor signals change coherently with the motion of the structure.
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.
ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset
The proposed method utilizes a U-Net-based network architecture that incorporates cloth and human attributes to guide the realistic virtual try-on synthesis.
Social AI and the Challenges of the Human-AI Ecosystem
In order to understand the impact of AI on socio-technical systems and design next-generation AIs that team with humans to help overcome societal problems rather than exacerbate them, we propose to build the foundations of Social AI at the intersection of Complex Systems, Network Science and AI.
MeciFace: Mechanomyography and Inertial Fusion-based Glasses for Edge Real-Time Recognition of Facial and Eating Activities
The increasing prevalence of stress-related eating behaviors and their impact on overall health highlights the importance of effective and ubiquitous monitoring systems.
CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control
We present CaptAinGlove, a textile-based, low-power (1. 15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control.
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.
Ranked #1 on
Human Activity Recognition
on MM-Fit
Generative Adversarial Network
Human Activity Recognition
+1
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.
A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image
Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can produce hundreds of full-body high-resolution volumetric images of organisms.
InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition
The sensors were placed unobtrusively in a sports cap to monitor facial muscle activities related to facial expressions.
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation
Ground pressure exerted by the human body is a valuable source of information for human activity recognition (HAR) in unobtrusive pervasive sensing.
Non-contact, real-time eye blink detection with capacitive sensing
This work described a novel non-contact, wearable, real-time eye blink detection solution based on capacitive sensing technology.
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.
Smart Cup: An impedance sensing based fluid intake monitoring system for beverages classification and freshness detection
We studied the frequency sensitivity of the electrochemical impedance spectrum regarding distinct beverages and the importance of features like amplitude, phase, and real and imaginary components for beverage classification.
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.
Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition
Meanwhile, we evaluate the impact of the infrared array sensor on the recognition accuracy of these activities.
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.
Magnetic Field Based Hand Tracking
In more detail, the transmitters generate the oscillating magnetic fields with a registered sequence, the receiver senses the strength of the induced magnetic field by a customized three axes coil, which is configured as the LC oscillator with the same oscillating frequency so that an induced current shows up when the receiver is located in the field of the generated magnetic field.
Estimation of 3D Body Shape and Clothing Measurements from Frontal- and Side-view Images
The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space.
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.
Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks
The experimental results show that the proposed feature extraction method can effectively predict the RUL and outperforms the conventional RUL prediction approaches based on deep neural networks.
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter.
BIG-bench Machine Learning
Interpretable Machine Learning
+8
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.
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level.
Ranked #1 on
Skills Evaluation
on eSports Sensors Dataset
Discriminative feature generation for classification of imbalanced data
In this paper, we propose a novel supervised discriminative feature generation (DFG) method for a minority class dataset.
Two-stage generative adversarial networks for document image binarization with color noise and background removal
Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition.
Ranked #1 on
Binarization
on LRDE DBD
Fusion of Global-Local Features for Image Quality Inspection of Shipping Label
These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality.
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life.
Self-Adaptation of Activity Recognition Systems to New Sensors
Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account.
Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing.
A New Vision of Collaborative Active Learning
Active learning (AL) is a learning paradigm where an active learner has to train a model (e. g., a classifier) which is in principal trained in a supervised way, but in AL it has to be done by means of a data set with initially unlabeled samples.
