Thigh-Worn Sensor for Measuring Initial and Final Contact During Gait in a Mobility Impaired Population: Validation Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Adapting physical activity monitors to detect gait events (ie, at initial and final contact) has the potential to build a more personalized approach to gait rehabilitation after stroke. Meeting laboratory standards for detecting these events in impaired populations is challenging, without resorting to a multisensor solution. The Teager-Kaiser energy operator (TKEO) estimates the instantaneous energy of a signal; its enhanced sensitivity has successfully detected gait events from the acceleration signals of individuals with impaired mobility, but has not been applied to stroke. Objective: This study aimed to test the criterion validity of TKEO gait event detection (and derived spatiotemporal metrics) using data from thigh mounted physical activity monitors compared with concurrent 3D motion capture in chronic survivors of stroke. Methods: Participants with a history of stroke(n=13, mean age 59, SD 14 years), time since stroke (mean 1.5, SD 0.5 years), walking speed (mean 0.93ms-1 , SD 0.38 m/s) performed two 10m walks at their comfortable speed, while wearing two ActivPAL 4+ (AP4) sensors (anterior of both thighs) and LED cluster markers on the pelvis and ankles which were tracked by a motion capture system. The TKEO signal processing technique was then used to extract gait events (initial and final contact) and calculate stance durations which were compared with motion capture data. Results: There was very good agreement between the AP4 and motion capture data for stance duration (AP4 0.85s, motion capture system 0.88s, 95% CI of difference -0.07 to 0.13, intraclass correlation coefficient [3,1]=0.79). Conclusions: The TKEO method for gait event detection using AP4 data provides stance time durations that are comparable with laboratory-based systems in a population with chronic stroke. Providing accurate stance time durations from wearable sensors could extend gait training out of clinical environments. Limitations include ecological and external validity. Future work should confirm findings with a larger sample of participants with a history of stroke.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it