Purpose: We have previously observed substantially higher oxygen uptake in soldiers walking on terrain at night than when performing the same walk in bright daylight. The aims of the present study were to investigate the influence of vision on mechanical efficiency during slow, horizontal, constant-speed walking, and to determine whether any vision influence is modified by load carriage.
Methods: Each subject (n = 15) walked (3.3 km/h) for 10 min on a treadmill in four different conditions: (1) full vision, no carried load, (2) no vision, no carried load, (3) full vision with a 25.5-kg rucksack, (4) no vision with a 25.5-kg rucksack.
Results: Oxygen uptake was 0.94 ± 0.12 l/min in condition (1), 1.15 ± 0.20 l/min in (2), 1.15 ± 0.12 l/min in (3) and 1.35 ± 0.19 l/min in (4). Thus, lack of vision increased oxygen uptake by about 19%. Analyses of movement pattern, by use of optical markers attached to the limbs and torso, revealed considerably shorter step length (12 and 10%) in the no vision (2 and 4) than full vision conditions (1 and 3). No vision conditions (2 and 4) increased step width by 6 and 6%, and increased vertical foot clearance by 20 and 16% compared to full vision conditions (1 and 3).
Conclusion: The results suggest that vision has a marked influence on mechanical efficiency even during entrained, repetitive movements performed on an obstacle-free horizontal surface under highly predictable conditions.
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http://dx.doi.org/10.1007/s00421-022-04914-6 | DOI Listing |
Deep learning models are used to minimize the number of polyps that goes unnoticed by the experts and to accurately segment the detected polyps during interventions. Although state-of-the-art models are proposed, it remains a challenge to define representations that are able to generalize well and that mediate between capturing low-level features and higher-level semantic details without being redundant. Another challenge with these models is that they are computation and memory intensive, which can pose a problem with real-time applications.
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December 2024
Shanghai Jiaotong University School of Medicine, Shanghai, China.
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Institute for Life Course Health Research, Stellenbosch University, Cape Town, Western Cape, South Africa.
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