基于惯性数据特征估计升力的多元线性回归方法。

IF 0.4 Q3 Medicine
Leandro Donisi, Edda Maria Capodaglio, Federica Amitrano, Giuseppe Cesarelli, Gaetano Pagano, Giovanni D'Addio
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引用次数: 0

摘要

摘要:与工作有关的肌肉骨骼疾病是主要的职业健康问题之一。大量证据表明,与工作相关的身体风险因素是腰背部疾病的主要来源,特别是影响繁重和重复的体力提升活动。本研究的目的是在负载提升任务中,探索从执行对象的加速度和角速度信号中提取的时域特征与提升的负载之间的相关性,并探索多元线性回归模型预测提升负载的可行性。在负重从0 kg逐渐增加1 kg到18 kg的过程中,通过放置在受试者胸前的惯性传感器,获取受试者在空间三个方向上的加速度和角速度信号。依次从采集的信号中提取三个时域特征(均方根、标准差和最小最大值)。首先,对各特征与提升荷载(计算r)进行相关性分析;然后利用最具代表性的时域特征(强相关性)建立多元线性回归模型(计算r平方)。采用Pearson相关进行统计分析,并根据相关分析得到信息量最大的时域特征多元线性回归模型。相关性分析表明,6个特征(z轴加速度提取的3个特征和y轴角速度提取的3个特征)与提升载荷之间存在很强的相关性(r > 0,7)。采用这六个特征的预测多元线性回归模型的rsquared大于0,9。研究表明,将运动学特征与多元回归模型相结合是一种有效的方法,可以根据放置在胸前的惯性传感器获得的原始信号自动计算举起的载荷。结果证实了这种方法的潜在应用,间接监测工人在活动期间举起的负荷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multiple linear regression approach to extimate lifted load from features extracted from inertial data.

Summary: Work-related musculoskeletal disorders are among the main occupational health problems. Substantial evidence has shown that work-related physical risk factors are the main source of low back complaints, particularly affecting heavy and repetitive manual lifting activities. The aim of the study is, during load lifting tasks, to explore the correlation between the time domain features extracted from the acceleration and angular velocity signals of the performing subject and the load lifted, and to explore the feasibility of a multiple linear regression model to predict the lifted load. The acceleration and angular velocity signals were acquired along the three directions of space by means of an inertial sensor placed on the subject's chest, during lifting activities with load gradually increased by 1 kg from 0 kg to 18 kg. Successively three time-domain features (Root Mean Square, Standard Deviation and MinMax value) were extracted from the acquired signals. First a correlation analysis was carried out between each individual feature and the load lifted (calculating r); then the time-domain features that proved most representative (strong correlation) were used to create a multiple linear regression model (calculating R-square). The statistical analysis was carried out by means of the Pearson correlation and multiple linear regression model was fed with the most informative time-domain features according to the correlation analysis. The correlation analysis showed a strong correlation (r > 0,7) between six features (three extracted from z-axes acceleration and three extracted from y-axes angular velocity) and the lifted load. The predictive multiple linear regression model, fed with these six features achieved a Rsquare greater than 0,9.The study demonstrated that the proposed combination of kinematic features and a multiple regression model represents a valid approach to automatically calculate the load lifted based on raw signals obtained by means of an inertial sensor placed on the chest. The results confirm the potential application of this methodology to indirectly monitor the load lifted by workers during their activity.

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来源期刊
Giornale italiano di medicina del lavoro ed ergonomia
Giornale italiano di medicina del lavoro ed ergonomia PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
0.80
自引率
0.00%
发文量
10
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