Development and Use of Devices for Ergonomic Assessment and Comfort Enhancement in Industrial Environments

工业环境中人体工程学评估和舒适度增强设备的开发和使用

• 批准号: RGPIN-2019-05240
• 负责人: Oliver, Michele
• 金额: $ 2.62万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738140
• 关键词: Development; Use; Devices; Ergonomic; Assessment

The first objective of this proposal is to understand the effects of muscle fatigue on posture, muscle activation, muscle latency and vibration transmissibility, and then to use the information to further develop active whole-body vibration (WBV) attenuation devices for seats in large mobile equipment. It is well known that large mobile machines expose operators to WBV exposure which results in seated operators experiencing muscle fatigue. However, what is not well understood are the effects of muscle fatigue on posture, muscle activation, muscle latency and vibration transmissibility during WBV exposure. Over the course of this proposal, a series of studies will systematically quantify this for individual sinusoidal vibration axes and combined axis exposures at a number of discrete frequencies before and after participants complete a low back fatiguing protocol. Studies will be conducted on a lab located hexapod robot simulator where participants will sit on a seat attached to the robot platform.  Similar investigations will be conducted using simulations that use vibration recordings obtained from a working machine but implemented on the laboratory located robot where participants will use a VR headset along with joystick controls and gas and brake pedals. Over the course of this proposal a newly designed and built active vibration control device will also be further developed and tested using the same protocols noted above. The second objective of the proposal will be to further develop and use a recently designed wearable wrist device for the assessment of kinematics and kinetics and the prediction of muscles forces during the performance of simulated workplace tasks. Data obtained from the wrist wearable device and fingertip force sensors during simulated workplace tasks performed in the laboratory will be used to create a model of the hand and forearm which will be able to predict muscle forces. The long term objective of this research program is to develop a suite of reasonably priced active vibration control devices which can be used on any seat for a variety of large mobile equipment over a variety of terrains in order to improve ISO 2631-1[10] predicted operator comfort. The second long term objective is to develop and use devices for ergonomic assessment which will provide input data for validated models that predict muscle forces. The ability to accurately predict muscle forces could allow us as engineers to virtually prototype hand tools and tasks with an aim to design them in a way that minimizes muscle force requirements while maintaining neutral wrist postures. Another long term objective is the implementation of machine learning techniques to detect subtle kinematic changes that might occur as a worker is beginning to fatigue so that the device alerts them that they need to take a break and/or change what they're doing.

本提案的第一个目标是了解肌肉疲劳对姿势、肌肉激活、肌肉潜伏期和振动传递率的影响，然后利用这些信息进一步开发用于大型移动的设备中座椅的主动全身振动（WBV）衰减装置。众所周知，大型移动的机器使操作员暴露于WBV暴露，这导致坐着的操作员经历肌肉疲劳。然而，在WBV暴露期间，肌肉疲劳对姿势、肌肉激活、肌肉潜伏期和振动传递率的影响还没有很好的了解。在该提案的过程中，一系列研究将系统地量化参与者完成下背部疲劳协议之前和之后的一些离散频率下的单个正弦振动轴和组合轴暴露。研究将在位于实验室的六足机器人模拟器上进行，参与者将坐在连接到机器人平台的座椅上。类似的调查将使用模拟进行，模拟使用从工作机器获得的振动记录，但在位于实验室的机器人上实施，参与者将使用VR耳机沿着操纵杆控制以及油门和刹车踏板。在本提案的过程中，还将使用上述相同的协议进一步开发和测试新设计和建造的主动振动控制装置。 该提案的第二个目标将是进一步开发和使用最近设计的可穿戴手腕设备，用于评估运动学和动力学，并预测模拟工作场所任务执行期间的肌肉力量。在实验室进行的模拟工作场所任务期间，从手腕可穿戴设备和指尖力传感器获得的数据将用于创建手和前臂的模型，该模型将能够预测肌肉力量。该研究计划的长期目标是开发一套价格合理的主动振动控制设备，可用于各种地形上各种大型移动的设备的任何座椅上，以提高ISO 2631-1[10]预测的操作员舒适度。第二个长期目标是开发和使用人体工程学评估设备，为预测肌肉力量的有效模型提供输入数据。准确预测肌肉力量的能力可以让我们作为工程师对手动工具和任务进行虚拟原型设计，目的是在保持中立手腕姿势的同时最大限度地减少肌肉力量需求。另一个长期目标是实施机器学习技术，以检测工人开始疲劳时可能发生的细微运动学变化，以便设备提醒他们需要休息和/或改变他们正在做的事情。