Developmental knee joint deformities are a common problem in pediatric orthopedics. Children with a valgus or varus deformity of the distal femur or the proximal tibia are commonly treated with hemiepiphysiodesis. Gait analysis in patients with lower limb deformities plays an important role in clinical practice. The purpose of our study was to assess gait parameters in patients who underwent hemiepiphysiodesis procedures of the distal femur or proximal tibia due to a knee deformity and to compare them with those in healthy controls. We prospectively evaluated 35 patients (14 females and 21 males) after hemiepiphysiodesis and compared the results with a healthy control group (26 participants). Gait was analyzed with a G-Sensor device (BTS Bioengineering Corp., Quincy, MA, USA). We assessed the following gait parameters: gait cycle duration, step length, support phase duration, swing phase duration, double support duration, single support duration, cadence, velocity, and step length. We assessed these gait parameters in a group of patients before and after treatment with hemiepiphysiodesis. We compared the patients' results before and after treatment to those of a healthy control group. The level of significance was set at < 0.05. The mean follow-up period was 13 months. There was no difference in the results of gait assessments in patients prior to and after treatment. The median step length was 47.09% in the treated limb after treatment and 54.01% in the intact limb ( = 0.018). There were no other differences in gait parameters in the treated limbs and the healthy, intact limbs in the patient group after treatment. There were no significant differences in the patients before and after treatment compared with those in the healthy control group in all gait parameters. Valgus or varus knee deformity correction with the use of hemiepiphysiodesis does not significantly improve preoperative gait parameters. The biomechanical outcomes of hemiepiphysiodesis in the treatment of valgus or varus knee deformity are good. We observed no differences in gait cycle duration, step length, support phase duration, swing phase duration, double support duration, single support duration, gait velocity, cadence, or step length between the experimental and healthy control groups.
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http://dx.doi.org/10.3390/jcm14020444 | DOI Listing |
Sensors (Basel)
January 2025
Centre for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations.
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January 2025
Department of Electrical and Information Engineering, Kiel University, 24143 Kiel, Germany.
Clinical motion analysis plays an important role in the diagnosis and treatment of mobility-limiting diseases. Within this assessment, relative (point-to-point) tracking of extremities could benefit from increased accuracy. Given the limitations of current wearable sensor technology, supplementary spatial data such as distance estimates could provide added value.
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January 2025
College of Sport and Health Science, Ritsumeikan University, Kusatsu 525-8577, Shiga, Japan.
This study aimed to assess the intraday reliability of markerless gait analysis using an RGB-D camera versus a traditional three-dimensional motion analysis (3DMA) system with and without a simulated walking assistant. Gait assessments were conducted on 20 healthy adults walking on a treadmill with a focus on spatiotemporal parameters gathered using the RGB-D camera and 3DMA system. The intraday reliability of the RGB-D camera was evaluated using intraclass correlation coefficients (ICC 1, 1), while its consistency with the 3DMA system was determined using ICC (2, 1).
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January 2025
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.
Parkinson's disease (PD) is characterized by a slow, short-stepping, shuffling gait pattern caused by a combination of motor control limitations due to a reduction in dopaminergic neurons. Gait disorders are indicators of global health, cognitive status, and risk of falls and increase with disease progression. Therefore, the use of quantitative information on the gait mechanisms of PD patients is a promising approach, particularly for monitoring gait disorders and potentially informing therapeutic interventions, though it is not yet a well-established tool for early diagnosis or direct assessment of disease progression.
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January 2025
German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, Germany.
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy.
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