ArticleSensors (Basel, Switzerland)2021
Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Novel Frailty Assessment Based on Multidimensional Physical Frailty Parameters Using Unsupervised Clustering in Respiratory Diseases: A Pilot Study.Journal of clinical medicine · 2026Article
- Machine learning algorithms to predict frailty in older adults in China: a cross-sectional study.BMC geriatrics · 2025Article
- Development and validation of a multi-modal contactless sensing system for surgical risk analysis in a real-world environment.PLOS digital health · 2025Article
- Article
- Dynamic frailty risk prediction in elderly hip replacement: a deep learning approach to personalized rehabilitation.BMC medical informatics and decision making · 2025Article
- Development and validation of machine learning models for classifying cancer-related sarcopenia using Kinect-based mixed-reality exercises in breast cancer survivors.Translational cancer research · 2025Article
- Article
- The effects of the generative adversarial network and personalized virtual reality platform in improving frailty among the elderly.Scientific reports · 2025Article
- Article
- Article
- A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA).Frontiers in aging · 2025Article
- Machine Learning for Movement Pattern Changes during Kinect-Based Mixed Reality Exercise Programs in Women with Possible Sarcopenia: Pilot Study.Annals of geriatric medicine and research · 2024Article
- Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults.Health care science · 2024Article
- Kinect-based objective assessment for early frailty identification in patients with Parkinson's disease.Aging clinical and experimental research · 2023Article
- Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.Journal of medical Internet research · 2023Article
- Feasibility of a kinect-based system in assessing physical function of the elderly for home-based care.BMC geriatrics · 2023Article
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- Discrimination of Frailty Phenotype by KinectJAR life · 2023Article
- Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty.Frontiers in public health · 2023Observational
- Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure.Journal of clinical medicine · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried's frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.
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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.