PhysioSense: An Open-Source Multi-Modal Monitoring Framework for Human Movement and Behavior Analysis
The PhysioSense framework enables the capture and recording of data using a variety of tools for applications in human movement analysis, ergonomic assessment, skill capture, and human-robot interaction.
Assessing Single and Dual-Sensor IMU Setup for 3D Foot Modelling in Running
This study compares two IMU-based approaches for modelling foot segment motion: a dual-sensor setup, where both hindfoot and forefoot orientations are directly measured, and a single-sensor setup, where forefoot (FF) orientation is estimated from the hindfoot (HF) orientation.
Selection of insole pressure sensors for ground reaction force estimation through studying principal component analysis
The study aimed to identify the optimal number and location of pressure sensors in insoles, specifically Moticon and Fscan, for accurate estimation of Ground Reaction Force (GRF) components during walking, utilizing Principal Component Analysis (PCA) and Artificial Neural Networks (ANN).
Flexible 3D-printed sensors for wearable motion analysis and assistive devices
This study aims to evaluate and develop wearable, 3D-printable transducer technologies, specifically ferroelectrets, to enhance human gait analysis, motion intention detection, and health monitoring for mobile assistive devices.
GaitWay Gait Data-Based VR Locomotion Prediction System Robust to Visual Distraction
This study involved 11 participants navigating a visually distracting three-tiered VR environment while performing designated tasks.
ErgoPulse: Electrifying Your Lower Body With Biomechanical Simulation-based Electrical Muscle Stimulation Haptic System in Virtual Reality
This study presents ErgoPulse, a system that integrates biomechanical simulation with electrical muscle stimulation (EMS) to provide
kinesthetic force feedback to the lower-body in virtual reality (VR).
Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy
In this paper, we evaluated the impact of twelve pre-normalization methods using three PCA-ML methods on the accuracy of GRF component estimation.
Detecting and Assessing Older Adults’ Stressful Interactions with the Built Environment: An Elderly-Centric and Wearable Sensing-Based Approach
The overall goal of this research is: 1 to enable practitioners to detect stressful older adults-environment interactions in near real-time and 2 to bring to the limelight the influence of urban environment configuration
Guiding 3D human pose estimation using feet pressure sensors
The aim of the present thesis is to study whether we can improve the 3D pose estimation in these cases by incorporating knowledge about foot contact.
Incorporation of Pressure Insoles into Inverse Dynamic Analysis
This study aimed to advance the state-of-the-art on IMU-based inverse dynamics analysis by incorporating pressure insoles as the source of the vertical components of the GRFs, with a view to improving the model fidelity.
Dual-Modal 3D Human Pose Estimation using Insole Foot Pressure Sensors
The proposed system utilizes pressure and IMU sensors embedded in insoles to capture the body weight’s pressure distribution at the feet and its 6 DoF acceleration.
In-plane density gradation of shoe midsoles for optimal energy absorption performance
The present work investigated the effectiveness of in-plane density gradation in shoe midsoles using novel polyurea foams as the material candidate.
Finite Element Modeling and Validation of a Human Foot through experimental studies
This paper attempts to develop and validate a realistic finite element model of a normal human foot with bones, muscles, tendons and ligaments mimicking the actual foot using Image Reconstruction Techniques (IRT).
UnderPressure: Deep Learning for Foot Contact Detection, Ground Reaction Force Estimation and Footskate Cleanup
We first publicly release UNDERPRESSURE, a novel motion capture database labelled with pressure insoles data serving as reliable knowledge of foot contact with the ground.
In-plane Density Gradation of Shoe Midsoles for Optimized Cushioning Performance
The present work investigates the effectiveness of in-plane density gradation in shoe midsoles using a new class of polyurea foams as the material candidate.
Machine learning-based identification and classification of physical fatigue levels: A novel method based on a wearable insole device
This study aims to utilize a wearable insole device to identify and classify physical fatigue levels in construction workers.
Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning
This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device.
An Automatic Foot and Shank IMU Synchronization Algorithm: Proof-of-concept
To enable ongoing studies in the field, we have developed a biomechanical sensing platform (BSP) which consists of 5 wireless body-worn IMUs, foot pressure insole units (ISUs) and an Android based application.
Plantar Pressure Distribution And Gait Stability: Normal VS High Heel
In this paper, the plantar pressure distribution and the center of pressure movement were studied and gait stability of high heels versus normal heels was observed.
Field Test: Results of tandem walk performance following long-duration spaceflight
Aim to develop a recovery timeline of functional sensorimotor performance during the first 24 hours and several days after a spaceflight landing.
Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning
This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping, pulling a cart backward, squatting,
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Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables
The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%.
