Foot and footwear biomechanics and gait

Price, Carina; Schmeltzpfenning, Timo; Nester, Christopher; Brauner, Torsten

doi:10.1016/b978-0-12-821606-4.00013-2

Cited by 15 publications

(14 citation statements)

References 72 publications

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“…A gait cycle may be defined as a series of events occurring from the heel strike of a foot to the heel strike of th same foot. [ 7 ] The normal gait cycle is divided into eight phases: 1. initial contact, 2. loading response, 3. midstance, 4. terminal stance, 5. pre-swing, 6. initial swing, 7. mid-swing, and 8. terminal swing. [ 8 , 9 ]…”

Section: B Ackgroundmentioning

confidence: 99%

Effect of medication and deep brain stimulation on gait in Parkinson's disease and its quantitative analysis using Mobishoe – A comparative study

Kandadai

Meka

Alugolu

et al. 2023

Ann Indian Acad Neurol

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Background: Movement abnormalities pertaining to balance, posture, and gait are observed in Parkinson’s disease patients. Gait characteristics vary widely and their analysis has been performed traditionally in gait labs. Freezing and festination usually occur at an advanced stage of the disease and are associated with reduced quality of life. Therapeutic strategies and surgical interventions are often modulated by the physician depending upon the clinical manifestations. Introduction of accelerometers and wireless data transmission systems made quantitative gait analysis possible and cost-effective. Objective: To assess spatiotemporal gait parameters (step height, length (spatial), and swing support time of each foot and double support time (temporal)) in subjects who underwent deep brain stimulation surgery using a purpose-built instrument—Mobishoe. Methods: A simple footwear-based gait sensing device—Mobishoe was built in-house. Thirty-six participants were included in the study after obtaining consent. Participants were made to wear Mobishoe and walk an empty corridor of 30m before Deep Brain Stimulation (DBS) in the drug on and off stated and post DBS in DBS stimulation on and medication off state (B1M0), DBS stimulation off—medication off state (B0M0), DBS stimulation off—medication on (B0M1), and DBS stimulation on and on medication (B1M1). Data was electronically captured and analyzed offline in MATrix LABoratory (MATLAB). Various gait parameters were extracted and analyzed. Results: Improvement in gait parameters was observed when the subject was on medication, on stimulation, or on both when compared to baseline. Improvement was similar with both medication and stimulation and was synergistic when both were used. Significant improvement was noted in spatial characteristics when the subjects were on both the treatments, which is the ideal treatment modality. Conclusion: Mobishoe is an affordable device which can measure spatiotemporal characteristics of gait. The best improvement was seen when the subjects were on both the treatment groups and the improvement can be justified as a synergistic effect of stimulation and medication.

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Section: B Ackgroundmentioning

confidence: 99%

Effect of medication and deep brain stimulation on gait in Parkinson's disease and its quantitative analysis using Mobishoe – A comparative study

Kandadai

Meka

Alugolu

et al. 2023

Ann Indian Acad Neurol

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“…An example of one subject's data is shown in figure 4. As shown in figure 5, the gait phases of human walking occur periodically in a GC, which are mainly classified into the stance phase and swing phase [5,46,47]. The stance phase is defined as the time each foot performs one ground contact and stays on the ground.…”

Section: Data Preparationmentioning

confidence: 99%

Transferable multi-modal fusion in knee angles and gait phases for their continuous prediction

Guo

Zheng

et al. 2023

J. Neural Eng.

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Objective.The gait phase and joint angle are two essential and complementary components of kinematics during normal walking, whose accurate prediction is critical for lower-limb rehabilitation, such as controlling the exoskeleton robots. Multi-modal signals have been used to promote the prediction performance of the gait phase or joint angle separately, but it is still few reports to examine how these signals can be used to predict both simultaneously. Approach.To address this problem, we propose a new method named transferable multi-modal fusion (TMMF) to perform a continuous prediction of knee angles and corresponding gait phases by fusing multi-modal signals. Specifically, TMMF consists of a multi-modal signal fusion block, a time series feature extractor, a regressor, and a classifier. The multi-modal signal fusion block leverages the Maximum Mean Discrepancy to reduce the distribution discrepancy across different modals in the latent space, achieving the goal of transferable multi-modal fusion. Subsequently, by using the long short-term memory-based network, we obtain the feature representation from time series data to predict the knee angles and gait phases simultaneously. To validate our proposal, we design an experimental paradigm with random walking and resting to collect data containing multi-modal biomedical signals from electromyography, gyroscopes, and virtual reality.Main results.Comprehensive experiments on our constructed dataset demonstrate the effectiveness of the proposed method. TMMF achieves a root mean square error of 0.090±0.022 s in knee angle prediction and a precision of 83.7±7.7\% in gait phase prediction.Significance.We demonstrate the feasibility and validity of using TMMF to predict lower-limb kinematics continuously from multi-modal biomedical signals. This proposed method represents application potential in predicting the motor intent of patients with different pathologies.

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“…We have one single support phase per limb, plus two double support phases where load transitions from one leg to the other. Single support phases usually comprise more than 60% of the cycle [27]. Clinicians rely on this particular phase to evaluate the partial weight-bearing during the rehabilitation process [24,25].…”

Section: Gait Assessment Using a Smart Rollatormentioning

confidence: 99%

“…Single support phases usually comprise more than 60% of the cycle [ 27 ]. Clinicians rely on this particular phase to evaluate the partial weight-bearing during the rehabilitation process [ 24 , 25 ].…”

Section: Gait Assessment Using a Smart Rollatormentioning

confidence: 99%

Walk-IT: An Open-Source Modular Low-Cost Smart Rollator

Fernández-Carmona

Ballesteros

Boladeras

et al. 2022

Sensors

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Rollators are widely used in clinical rehabilitation for gait assessment, but gait analysis usually requires a great deal of expertise and focus from medical staff. Smart rollators can capture gait parameters autonomously while avoiding complex setups. However, commercial smart rollators, as closed systems, can not be modified; plus, they are often expensive and not widely available. This work presents a low cost open-source modular rollator for monitorization of gait parameters and support. The whole system is based on commercial components and its software architecture runs over ROS2 to allow further customization and expansion. This paper describes the overall software and hardware architecture and, as an example of extended capabilities, modules for monitoring dynamic partial weight bearing and for estimation of spatiotemporal gait parameters of clinical interest. All presented tests are coherent from a clinical point of view and consistent with input data.

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Foot and footwear biomechanics and gait

Cited by 15 publications

References 72 publications

Effect of medication and deep brain stimulation on gait in Parkinson's disease and its quantitative analysis using Mobishoe – A comparative study

Effect of medication and deep brain stimulation on gait in Parkinson's disease and its quantitative analysis using Mobishoe – A comparative study

Transferable multi-modal fusion in knee angles and gait phases for their continuous prediction

Walk-IT: An Open-Source Modular Low-Cost Smart Rollator

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