康复
计算机科学
水准点(测量)
人工智能
可靠性(半导体)
功能性运动
人口
运动学
集合(抽象数据类型)
范围(计算机科学)
物理医学与康复
机器学习
考试(生物学)
物理疗法
医学
物理
古生物学
环境卫生
功率(物理)
生物
经典力学
程序设计语言
地理
量子力学
大地测量学
作者
Marianna Capecci,Maria Gabriella Ceravolo,Francesco Ferracuti,Sabrina Iarlori,Andrea Monteriù,Luca Romeo,Federica Verdini
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering
[Institute of Electrical and Electronics Engineers]
日期:2019-06-14
卷期号:27 (7): 1436-1448
被引量:83
标识
DOI:10.1109/tnsre.2019.2923060
摘要
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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