Literature DB >> 31217121

The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation.

Marianna Capecci, Maria Gabriella Ceravolo, Francesco Ferracuti, Sabrina Iarlori, Andrea Monteriu, Luca Romeo, Federica Verdini.   

Abstract

This paper proposes a free dataset, available at the following link,1named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, depth videos, and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject's performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise.1https://univpm-my.sharepoint.com/:f:/g/personal/p008099_staff_univpm_it/EiwbKIzk6N9NoJQx4J8aubIBx0o7tIa1XwclWp1NmRkA-w?e=F3jtBk.

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Year:  2019        PMID: 31217121     DOI: 10.1109/TNSRE.2019.2923060

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  5 in total

Review 1.  A review of computational approaches for evaluation of rehabilitation exercises.

Authors:  Yalin Liao; Aleksandar Vakanski; Min Xian; David Paul; Russell Baker
Journal:  Comput Biol Med       Date:  2020-03-04       Impact factor: 4.589

2.  A Deep Learning Framework for Assessing Physical Rehabilitation Exercises.

Authors:  Yalin Liao; Aleksandar Vakanski; Min Xian
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-01-13       Impact factor: 3.802

Review 3.  Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review.

Authors:  Lucas Medeiros Souza do Nascimento; Lucas Vacilotto Bonfati; Melissa La Banca Freitas; José Jair Alves Mendes Junior; Hugo Valadares Siqueira; Sergio Luiz Stevan
Journal:  Sensors (Basel)       Date:  2020-07-22       Impact factor: 3.576

4.  VI-Net-View-Invariant Quality of Human Movement Assessment.

Authors:  Faegheh Sardari; Adeline Paiement; Sion Hannuna; Majid Mirmehdi
Journal:  Sensors (Basel)       Date:  2020-09-15       Impact factor: 3.576

5.  Design and Research of Remote Monitoring System for Sports Injury Rehabilitation Training.

Authors:  Hongyan Liu; Panlong Qin; Ruiming Qi
Journal:  J Healthc Eng       Date:  2021-09-27       Impact factor: 2.682

  5 in total

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