Evidence map›Paper›PMID 37205279›Full record

ArticleDigital biomarkers

Analytical Validation of a Webcam-Based Assessment of Speech Kinematics: Digital Biomarker Evaluation following the V3 Framework.

Leif Simmatis, Saeid Alavi Naeini, Deniz Jafari, Michael Kai Yue Xie, Chelsea Tanchip, Niyousha Taati, Scotia McKinlay, Rupinder Sran, Justin Truong, Diego L Guarin and 2 more

Abstract read
In one paragraph

Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Differentiating upper- and lower motor neuron diseases using automated acoustic analysis.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2025
    Article
  2. Article
  3. Improving Dysarthric Speech Segmentation With Emulated and Synthetic Augmentation.IEEE journal of translational engineering in health and medicine · 2024
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Leif SimmatisUniversity Health Network, Toronto Rehabilitation Institute, Toronto, ON, Canada.
Saeid Alavi NaeiniUniversity Health Network, Toronto Rehabilitation Institute, Toronto, ON, Canada.
Deniz JafariUniversity Health Network, Toronto Rehabilitation Institute, Toronto, ON, Canada.
Michael Kai Yue XieDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Chelsea TanchipDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Niyousha TaatiDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Scotia McKinlayDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Rupinder SranDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Justin TruongDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Diego L GuarinDepartment of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA.
Babak TaatiUniversity Health Network, Toronto Rehabilitation Institute, Toronto, ON, Canada.
Yana YunusovaUniversity Health Network, Toronto Rehabilitation Institute, Toronto, ON, Canada.

Funding

SPEECH MOVEMENT CLASSIFICATION FOR ASSESSING AND TREATING ALSR01DC013547 · NIDCD · MGH INSTITUTE OF HEALTH PROFESSIONS · PI GREEN, JORDAN R · 2014 to 2018
$3.2M
NIDCD NIH HHS R01 DC013547
6 · The paper itself

Abstract

Introduction: Kinematic analyses have recently revealed a strong potential to contribute to the assessment of neurological diseases. However, the validation of home-based kinematic assessments using consumer-grade video technology has yet to be performed. In line with best practices for digital biomarker development, we sought to validate webcam-based kinematic assessment against established, laboratory-based recording gold standards. We hypothesized that webcam-based kinematics would possess psychometric properties comparable to those obtained using the laboratory-based gold standards. Methods: We collected data from 21 healthy participants who repeated the phrase "buy Bobby a puppy" (BBP) at four different combinations of speaking rate and volume: Slow, Normal, Loud, and Fast. We recorded these samples twice back-to-back, simultaneously using (1) an electromagnetic articulography ("EMA"; NDI Wave) system, (2) a 3D camera (Intel RealSense), and (3) a 2D webcam for video recording via an in-house developed app. We focused on the extraction of kinematic features in this study, given their demonstrated value in detecting neurological impairments. We specifically extracted measures of speed/acceleration, range of motion (ROM), variability, and symmetry using the movements of the center of the lower lip during these tasks. Using these kinematic features, we derived measures of (1) agreement between recording methods, (2) test-retest reliability of each method, and (3) the validity of webcam recordings to capture expected changes in kinematics as a result of different speech conditions. Results: Kinematics measured using the webcam demonstrated good agreement with both the RealSense and EMA (ICC-A values often ≥0.70). Test-retest reliability, measured using the absolute agreement (2,1) formulation of the intraclass correlation coefficient (i.e., ICC-A), was often "moderate" to "strong" (i.e., ≥0.70) and similar between the webcam and EMA-based kinematic features. Finally, the webcam kinematics were typically as sensitive to differences in speech tasks as EMA and the 3D camera gold standards. Discussion and Conclusions: Our results suggested that webcam recordings display good psychometric properties, comparable to laboratory-based gold standards. This work paves the way for a large-scale clinical validation to continue the development of these promising technologies for the assessment of neurological diseases via home-based methods.

Indexed as

Facial trackingRemote assessmentSpeechValidation

Identifiers

PMID37205279
PMCPMC10187538

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.