Wearable technology mediated biofeedback to modulate spine motor control: a scoping review
Bibliographic record
Abstract
BACKGROUND: Lower back pain (LBP) is a disability that affects a large proportion of the population and treatment for this condition has been shifting towards a more individualized, patient-centered approach. There has been a recent uptake in the utilization and implementation of wearable sensors that can administer biofeedback in various industrial, clinical, and performance-based settings. Despite this, there is a strong need to investigate how wearable sensors can be used in a sensorimotor (re)training approach, including how sensory biofeedback from wearable sensors can be used to improve measures of spinal motor control and proprioception. RESEARCH QUESTION: The purpose of this scoping review was to examine the wide range of wearable sensor-mediated biofeedback frameworks currently being utilized to enhance spine posture and motor function. METHODS: A comprehensive scoping review was conducted in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines extension for Scoping Reviews (PRISMA-ScR) across the following databases: Embase, PubMed, Scopus, Cochrane, and IEEEXplore. Articles related to wearable biofeedback and spine movement were reviewed dated from 1980 - 2020. Extracted data was collected as per a predetermined checklist including the type, timing, trigger, location, and magnitude of sensory feedback being applied to the body. RESULTS: A total of 23 articles were reviewed and analysed. The most used wearable sensor to inform biofeedback were inertial measurement units (IMUs). Haptic (vibrotactile) feedback was the most common sensory stimulus. Most studies used an instantaneous online trigger to initiate sensory feedback derived from information pertaining to gross lumbar angles or the absolute orientations of the thorax or pelvis. CONCLUSIONS: This is the first study to review wearable sensor-derived sensory biofeedback to modulate spine motor control. Although the type of wearable sensor and feedback were common, this study highlights the lack of consensus regarding the timing and structure of sensory feedback, suggesting the need to optimize any sensory feedback to a specific use case. The findings from this study help to improve the understanding surrounding the ecological utility of wearable sensor-mediated biofeedback in industrial, clinical, and performance settings to enhance the sensorimotor control of the lumbar spine.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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; both teacher heads agree on what is shown here.
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".