Kinematic and Electromyographic Analysis of the Trunk and Lower Limbs During Walking in Negative-Heeled Shoes
Bibliographic record
Abstract
BACKGROUND: We investigate kinematic adaptation and muscle activities in the trunk and lower extremities of healthy subjects during treadmill walking in negative-heeled sports shoes versus normal sports shoes. METHODS: Thirteen healthy female university students participated in the study. We analyzed sagittal-movement kinematics and electromyographic findings from the erector spinae, rectus abdominus, rectus femoris, biceps femoris, tibialis anterior, and lateral gastrocnemius muscles of the dominant side in two shod conditions. RESULTS: Negative-heeled gait is characterized by faster cadence, shorter stride length, increased maximal extension angles in the trunk and hip, increased flexion angle in the knee, larger dorsiflexion in the stance phase, and a larger range of motion of the ankle joint. Negative-heeled gait resulted in a significantly larger integrated electromyographic value, a longer duration of electromyographic activity, and a higher mean amplitude of electromyographic activity in the tibialis anterior, lateral gastrocnemius, and biceps femoris muscles. CONCLUSIONS: Negative-heeled gait compared with normal gait places a higher physiologic demand on the tibialis anterior, lateral gastrocnemius, and biceps femoris muscles when walking on a level surface. Thus, negative-heeled shoes could be of value if used in an exercise rehabilitation or training program where inclined walking is not available owing to a flat terrain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".