ArticleJournal of rehabilitation and assistive technologies engineering
Wearable step counting using a force myography-based ankle strap.
Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 20 citations in OpenAlex.
- A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications.Biosensors · 2022Review
- Building Effective Machine Learning Models for Ankle Joint Power Estimation During Walking Using FMG Sensors.Frontiers in neurorobotics · 2022Article
- Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection.Frontiers in sports and active living · 2022Article
- Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns.Sensors (Basel, Switzerland) · 2020Article
- A Wearable Gait Phase Detection System Based on Force Myography Techniques.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionStep counting can be used to estimate the activity level of people in daily life; however, commercially available accelerometer-based step counters have shown inaccuracies in detection of low-speed walking steps (<2.2 km/h), and thus are not suitable for older adults who usually walk at low speeds. This proof-of-concept study explores the feasibility of using force myography recorded at the ankle to detect low-speed steps.
methodsEight young healthy participants walked on a treadmill at three speeds (1, 1.5, and 2.0 km/h) while their force myography signals were recorded at the ankle using a customized strap embedded with an array of eight force-sensing resistors. A K-nearest neighbour model was trained and tested with the recorded data. Additional three mainstream machine learning algorithms were also employed to evaluate the performance of force myography band as a pedometer.
resultsResults showed a low error rate of the step detection (<1.5%) at all three walking speeds.
conclusionsThis study demonstrates not only the feasibility of the proposed approach but also the potential of the investigated technology to reliably monitor low-speed step counting.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.