To evaluate how fundamental gait parameters used in walking (stride length, frequency, speed) are selected by cats we compared stride characteristics selected when walking on a solid surface to those selected when they were constrained to specific stride lengths using a pedestal walkway. Humans spontaneously select substantially different stride length-stride frequency-speed relationships in walking when each of these parameters is constrained, as in walking to a metronome beat (frequency constrained), evenly spaced floor markers (stride length constrained) or on a treadmill (speed constrained). In humans such adjustments largely provide energetic economy under the prescribed walking conditions. Cats show a similar shift in gait parameter selection between conditions as observed in humans. This suggests that cats (and by extension, quadrupedal mammals) also select gait parameters to optimize walking cost-effectiveness. Cats with a profound peripheral sensory deficit (from pyridoxine overdose) appeared to parallel the optimization seen in healthy cats, but without the same level of precision. Recent studies in humans suggest that gait optimization may proceed in two stages - a fast perception-based stage that provides the initial gait selection strategy which is then fine-tuned by feedback. The sensory deficit cats appeared unable to accomplish the feedback-dependent aspect of this process.
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http://dx.doi.org/10.1016/j.humov.2014.05.008 | 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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