Validation and reliability testing of a new, fully integrated gait analysis insole

Braun, Benedikt J.; Veith, Nils T.; Hell, R; Döbele, Stefan; Roland, Michael; Rollmann, Mika F.; Holstein, Joerg H.; Pohlemann, Tim

doi:10.1186/s13047-015-0111-8

Cited by 89 publications

(82 citation statements)

References 35 publications

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“…Some examples of alternative technologies include instrumented walkways (Bilney et al, 2003), depth sensors (Dolatabadi et al, 2016), photoelectric cell systems (Gomez Bernal. et al, 2016), inertial measurement units (Trojaniello et al, 2014), and in-shoe pressure sensors (Braun et al, 2015), each of which has its own advantages and disadvantages. However, some of these technologies still require expensive equipment, may have limited options for deployment, and provide minimal data compared to motion capture systems.…”

Section: Introductionmentioning

confidence: 99%

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Kanko¹,

Strutzenberger²,

Brown³

et al. 2020

Preprint

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Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. The aim of this work was to determine whether spatiotemporal gait parameters measured using Theia3D markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture. Thirty healthy adult participants performed treadmill walking at self-selected speeds while 2D video and marker-based motion capture data were collected simultaneously. Kinematic-based gait events were used to measure nine spatiotemporal gait parameters from both systems independently. The parameters were compared using their group means, Bland-Altman methods, Pearson correlation coefficients, paired-samples t-tests, and intraclass correlation coefficients (ICC(A-1) and ICC(C-1)). Group means between systems were indistinguishable across all nine gait parameters, and the Bland-Altman plots showed no systematic biases or clinically meaningful differences between the systems. Pearson coefficients indicated near-perfect correlations (r ≥0.96) between systems for all but two parameters, which had strong correlations (double-limb support time, r=0.87; swing time, r=0.88). T-tests indicated differences in stance, swing, and double-limb support time parameters, but strong correlations were found for all ICCs, the lowest being 0.84 for both double-limb support time and swing time. The measurements made by the Theia3D markerless motion capture system demonstrated concurrent validity with those from the marker-based system, indicating sufficient accuracy for research, clinical, and other uses.

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Section: Introductionmentioning

confidence: 99%

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Kanko¹,

Strutzenberger²,

Brown³

et al. 2020

Preprint

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“…Crea et al also designed an insole that embeds a grid of 64 pressure sensing elements that use Light Emitting Diodes (LED) and light sensor pairs to measure pressure distribution and center of pressure [36]. The insole used in [37] uses 13 capacitive pressure sensors with a 3D accelerometer, while [38] created a more sophisticated body sensor network with 2 IMUs, an accelerometer, and ultrasonic sensors.…”

Section: Introductionmentioning

confidence: 99%

Cyber-Physical System Framework for Measurement and Analysis of Physical Activities

2019

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Several recent studies in Cyber-Physical Systems (CPS) focus on monitoring human movement and capturing data for further processing and analysis. However, there is a lack of studies that address the configurability and modularity of these systems, which is important for designing customized systems with customized devices. We propose a solution to solve this through a modular framework that automatically recognizes and configures new devices and provides real-time data wirelessly. The proposed framework creates a Digital Twin of the physical device and mirrors its attributes and sensory information into the cyber world so they can be used in real-time and post-routine analysis. As a proof of concept, a configurable CPS model for physical activities monitoring is designed and implemented. The designed gait monitoring and analysis system delivers spatiotemporal data from multiple multi-sensory devices to a central data handling and backup cloud server over conventional IEEE802.11 Wi-Fi. An experiment involving a young athlete examined whether or not the CPS components would recognize each other over foreign networks and communicate accurate information.

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“…Therefore, routinely acquired computed tomography data sets are subjected to an image processing workflow resulting in personalized computational models. These models can be either equipped with monitoring data of the patient itself [9] or with avaiable in vivo data [7]. This leads to patient-specific simulations allowing the analysis of the stresses and strains of bone-implantsystems and are the starting point for optimization algorithms in orthopedic trauma surgery, cf.…”

Section: Introductionmentioning

confidence: 99%

Personalized simulation of a bone–implant–system during a step forward

Roland

Tjardes

Bouillon

et al. 2017

Proc Appl Math and Mech

Self Cite

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In this study, a workflow is presented for a personalized simulation of a bone-implant-system of a fractured tibia during a step forward. The workflow is based on routinely acquired tomographic data, segmentation, material assignment, mesh generation, setup of realistic boundary conditions and a finite element simulation (FEM). In the absence of patient-specific monitoring data, a dataset from the OrthoLoad database is implemented as individualized boundary conditions. This allows a simulation of the bone-implant-system close to reality of patient's step forward.

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Validation and reliability testing of a new, fully integrated gait analysis insole

Cited by 89 publications

References 35 publications

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Cyber-Physical System Framework for Measurement and Analysis of Physical Activities

Personalized simulation of a bone–implant–system during a step forward

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