A cross-sectional study comparing lateral and diagonal maximum weight shift in people with stroke and healthy controls and the correlation with balance, gait and fear of falling

Background: Recently, new methods have emerged that encourage voluntary participation by allowing patients to perform tasks, including exercises or treatments, in a virtual reality (VR) environment. Aim: This study aimed to examine the effects of full immersion virtual reality training on balance and knee function in patients who had undergone total knee replacement. Design: Single blind randomized controlled trial. Setting: Department of Physical Therapy in a rehabilitation center. Population: A total of 30 elderly patients ([Formula: see text]65 years old) who had undergone total knee replacement. Methods: Participants were randomly allocated to an experimental group ([Formula: see text]) and a control group ([Formula: see text]). The experimental group received with a continuous passive motion machine, exercise therapy, and a full immersion VR training program; the control group received only with a continuous passive motion machine and exercise therapy. Biorescue was used to test static and dynamic balance ability, and the Timed Up and Go and Western Ontario and McMaster Universities tests were used to assess knee function. Paired [Formula: see text]-tests were used to examine differences by time in each group, and independent [Formula: see text]-tests were used to examine differences between the groups. Results: In terms of within-group differences by time, both the experimental group and the control group showed significant changes in the anterior, and posterior limits of stability in both sides; static balance; and knee function. In the between-groups comparison, among static balance tests, there was a significant difference in center of mass path length in the standing position with eyes open ([Formula: see text]); among dynamic balance tests, there were significant differences in left, right, anterior, and posterior limits of stability ([Formula: see text]). Conclusions: VR training produced better early balance ability and knee function than what was seen in the control group. We believe that VR training in initial post-operative rehabilitation of total knee replacement patients may increase the rate of recovery. Clinical Rehabilitation Impact: VR exercise programs are effective in early rehabilitation after total knee replacement, and have clinical value as inexpensive methods that can promote active participation.

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“…This instrument has been shown to have high reliability, with a testretest intraclass coefficient (ICC) of 0.83. 24…”

Section: Static and Dynamic Balance Abilitymentioning

confidence: 99%

Effects of Full Immersion Virtual Reality Training on Balance and Knee Function in Total Knee Replacement Patients: A Randomized Controlled Study

Yoon

Son

2020

J. Mech. Med. Biol.

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“…The CoP, which is another informational element for postural control, is often used as much as the GRF. We can judge a person’s state for stability with the location and velocity of the CoP [11,12]. We can also predict the gait speed of a person with the forward velocity of the CoP in the midfoot [13] and get the user intention understanding of exoskeleton for controlling a robot system [14,15].…”

Section: Introductionmentioning

confidence: 99%

Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)

Choi

Lee

Shim

et al. 2018

Sensors

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As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively.

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“…Biswas et al have shown that the characteristics of COP can enhance the predictive power to a constructed index for dynamic stability [ 12 ]. In other studies, COP measures were also used to assess postural stability among stroke patients [ 13 ], patients with post-stroke hemiparesis [ 14 ] and patients with rheumatic disease [ 15 ]. In a recent study, Johansson et al investigated how the COP sway can be used to predict future falls.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model

Zhao

Peng

et al. 2018

Sensors

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Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial–lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior–posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly.

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A cross-sectional study comparing lateral and diagonal maximum weight shift in people with stroke and healthy controls and the correlation with balance, gait and fear of falling

Cited by 28 publications

References 42 publications

Effects of Full Immersion Virtual Reality Training on Balance and Knee Function in Total Knee Replacement Patients: A Randomized Controlled Study

Effects of Full Immersion Virtual Reality Training on Balance and Knee Function in Total Knee Replacement Patients: A Randomized Controlled Study

Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)

Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model

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