Observational studyPloS one2019
Gait can reveal sleep quality with machine learning models.
Observational study in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed, 22 citations in OpenAlex.
- Handcrafted MRI radiomics of enlarged perivascular spaces and machine learning predict cognitive impairment and sleep disturbance in young adults.Scientific reports · 2026Article
- Sleep efficiency in community-dwelling persons living with dementia: exploratory analysis using machine learning.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Observational
- Association between Self-reported Sleep Quality and Single-task Gait in Young Adults: A Study Using Machine Learning.Sleep science (Sao Paulo, Brazil) · 2023Article
- Association between Self-Reported Prior Night's Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- The Association Between Gait Speed and Sleep Problems Among Chinese Adults Aged 50 and Greater.Frontiers in neuroscience · 2022Article
- Ecological recognition of self-esteem leveraged by video-based gait.Frontiers in psychiatry · 2022Article
- Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes.Scientific data · 2021Article
- A Survey of Human Gait-Based Artificial Intelligence Applications.Frontiers in robotics and AI · 2021Review
- The Associations Between Objectively Measured Gait Speed and Subjective Sleep Quality in First-Year University Students, According to Gender.Nature and science of sleep · 2021Article
- Why Do They Fall? The Impact of Insomnia on Gait of Older Adults: A Case-Control Study.Nature and science of sleep · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep quality is an important health indicator, and the current measurements of sleep rely on questionnaires, polysomnography, etc., which are intrusive, expensive or time consuming. Therefore, a more nonintrusive, inexpensive and convenient method needs to be developed. Use of the Kinect sensor to capture one's gait pattern can reveal whether his/her sleep quality meets the requirements. Fifty-nine healthy students without disabilities were recruited as participants. The Pittsburgh Sleep Quality Index (PSQI) and Kinect sensors were used to acquire the sleep quality scores and gait data. After data preprocessing, gait features were extracted for training machine learning models that predicted sleep quality scores based on the data. The t-test indicated that the following joints had stronger weightings in the prediction: the Head, Spine Shoulder, Wrist Left, Hand Right, Thumb Left, Thumb Right, Hand Tip Left, Hip Left, and Foot Left. For sleep quality prediction, the best result was achieved by Gaussian processes, with a correlation of 0.78 (p < 0.001). For the subscales, the best result was 0.51 for daytime dysfunction (p < 0.001) by linear regression. Gait can reveal sleep quality quite well. This method is a good supplement to the existing methods in identifying sleep quality more ecologically and less intrusively.
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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.