Detection of Gait Patterns in Challenging Environments
Bibliographic record
Abstract
One in three individuals over the age of 65 years (elderly) will fall at least once a year (James, 1993). This probability increases to one in two adults over 80 years (DHA, 2005). Consequently, gait modifications associated with ageing have been linked with increased falls’ probability (Berg, Alessio, Mills, & Tong, 1997; Lord, Sherrington, & Menz, 2001). Despite an increasing research interest in recent times into the aetiology of falls, particularly in the elderly (>65 years), falls continue to be a major public health concern in Australia and worldwide. Fall-related injuries are the leading cause of accidental death in the elderly population, and account for the largest cause of hospitalisation for this population (Lord et al., 2001), with many elderly individuals experiencing physical, social, or functional ramifications following a fall. Consequently, the economic cost of falls to the public health system is escalating, with the total cost of fall injuries reported to be higher than road traffic injuries (DOH, 2004). The majority of falls associated costs include physician consultation, hospital stays, nursing homecare, rehabilitation, medical equipment, home modification and care, community based services, and prescription drugs and administration (DOH, 2004; Lord et al., 2001). Healthcare and related costs associated with falls are expected to double over the next 50 years (Close & Lord, 2006).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".