35 citations · 102 across the 51 of their papers we have counts for
53 papers
Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shifts
Seungyeol Baek, Yoonbyung Chai, Yonghyeon Lee +2
Human Activity Recognition (HAR) with self-administered wearables, such as at-home rehabilitation and exercise monitoring, often requires reattaching inertial measurement units (IM…
Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusion
Seungyeol Baek, Jaspreet Singh, Lala Shakti Swarup Ray +3
Human operators are still frequently exposed to hazardous environments such as disaster zones and industrial facilities, where intuitive and reliable teleoperation of mobile robots…
ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training
Lala Shakti Swarup Ray, Vitor Fortes Rey, Bo Zhou +2
Prolonged seated activity is increasingly common in modern environments, raising concerns around musculoskeletal health, ergonomics, and the design of responsive interactive system…
On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generators
Jasmin Frkatovic, Akash Malemath, Ivan Kankeu +7
We investigate the capabilities of Quantum Generative Adversarial Networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the gen…
SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
Lala Shakti Swarup Ray, Mengxi Liu, Deepika Gurung +3
Human Activity Recognition (HAR) with wearable sensors is essential for applications in healthcare, fitness, and human-computer interaction. Bio-impedance sensing offers unique adv…
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition
Lala Shakti Swarup Ray, Lars Krupp, Vitor Fortes Rey +3
Sensor-based human activity recognition (HAR) has predominantly focused on Inertial Measurement Units and vision data, often overlooking the capabilities unique to pressure sensors…
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition
Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh +2
Human activity recognition (HAR) with deep learning models relies on large amounts of labeled data, often challenging to obtain due to associated cost, time, and labor. Self-superv…
Initial Findings on Sensor based Open Vocabulary Activity Recognition via Text Embedding Inversion
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +1
Conventional human activity recognition (HAR) relies on classifiers trained to predict discrete activity classes, inherently limiting recognition to activities explicitly present i…
Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task
Harish Haresamudram, Chi Ian Tang, Sungho Suh +2
In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to r…
OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +1
Understanding human-to-human interactions, especially in contexts like public security surveillance, is critical for monitoring and maintaining safety. Traditional activity recogni…
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets
Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey +3
The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research priorit…
Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization
Daniel Geissler, Bo Zhou, Sungho Suh +1
A fundamental step in the development of machine learning models commonly involves the tuning of hyperparameters, often leading to multiple model training runs to work out the best…
RAD: Region-Aware Diffusion Models for Image Inpainting
Sora Kim, Sungho Suh, Minsik Lee
Diffusion models have achieved remarkable success in image generation, with applications broadening across various domains. Inpainting is one such application that can benefit sign…
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition
Mengxi Liu, Sungho Suh, Juan Felipe Vargas +3
In the human activity recognition research area, prior studies predominantly concentrate on leveraging advanced algorithms on public datasets to enhance recognition performance, li…
DisQu: Investigating the Impact of Disorder in Quantum Generative Models
Yannick Werner, Jasmin Frkatovic, Vitor Fortes Rey +5
Disordered Quantum many-body Systems (DQS) and Quantum Neural Networks (QNN) have many structural features in common. However, a DQS is essentially an initialized QNN with random w…
TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines
Hymalai Bello, Daniel Geißler, Sungho Suh +2
Smaller machine learning models, with less complex architectures and sensor inputs, can benefit wearable sensor-based human activity recognition (HAR) systems in many ways, from co…
ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion
Lala Shakti Swarup Ray, Daniel Geißler, Mengxi Liu +3
Despite the widespread integration of ambient light sensors (ALS) in smart devices commonly used for screen brightness adaptation, their application in human activity recognition (…
LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions
Lars Krupp, Jonas Bley, Isacco Gobbi +11
Individual teaching is among the most successful ways to impart knowledge. Yet, this method is not always feasible due to large numbers of students per educator. Quantum computing…
Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition
Parham Zolfaghari, Vitor Fortes Rey, Lala Ray +3
The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to…
BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation
Hymalai Bello, Sungho Suh, Bo Zhou +1
Smart factories leverage advanced technologies to optimize manufacturing processes and enhance efficiency. Implementing worker tracking systems, primarily through camera-based meth…
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +3
In human activity recognition (HAR), the availability of substantial ground truth is necessary for training efficient models. However, acquiring ground pressure data through physic…
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition
Mengxi Liu, Zimin Zhao, Daniel Geißler +3
Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with h…
The Power of Training: How Different Neural Network Setups Influence the Energy Demand
Daniel Geißler, Bo Zhou, Mengxi Liu +2
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…
Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks
Sungho Suh, Dhruv Aditya Mittal, Hymalai Bello +3
Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batter…