A Simple, Low-Cost Multi-Sensor-Based Smart Wearable Knee Monitoring System
Bibliographic record
Abstract
Maintaining good mobility with ease and freedom of movement is important for an individual's health and active aging. The knee joint, being the primary bearer of the body weight, plays a vital role in mobility. Continuous monitoring of the knee joint can potentially provide important information related to knee health and mobility which can be used for health assessment, early diagnoses of mobility-related problems, and monitoring recovery from injury or surgery. Therefore, we developed a simple, low-cost multi-sensor-based smart wearable device to monitor and assess the knee joint and mobility. The system is composed of miniaturized sensors (motion, temperature, pressure and galvanic skin response) to measure acceleration, angular velocity, skin temperature, muscle pressure and sweat rate of the knee joint during different activities. A database is constructed from 70 healthy adults aged 18-86 years that contains sensor data measured using the proposed knee joint monitoring system. To extract key knee and gait features from the datasets, we employed computationally efficient methods such as complementary filter and wavelet packet decomposition. The variations in the characteristics of the obtained parameters were analyzed in terms of gender and age groups. This simple, easy-to-use, cost-effective, non-invasive and unobtrusive knee monitoring system can be used for real-time monitoring, evaluation and early diagnoses of joint disorders, fall detection, mobility monitoring and rehabilitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".