SBIR Phase II: Predicting Musculoskeletal Injury Risk of Material Handling Workers with Novel Wearable Devices

SBIR 第二阶段：使用新型可穿戴设备预测物料搬运工人的肌肉骨骼损伤风险

• 批准号: 1660093
• 负责人: Haytham Elhawary
• 金额: $ 75万
• 依托单位: One Million Metrics Corp
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660093&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Predicting; Musculoskeletal

This Small Business Innovation Research (SBIR) Phase II project has the objective of demonstrating that discrete, belt mounted internet-connected wearable devices used by industrial workers can detect high risk lifting activities, promote safe lifting practices and behavior change, and predict the risk of musculoskeletal injuries due to unsafe lifting. Each year over 600,000 workers suffer a musculoskeletal injury due to lifting related activities, which cost US companies over $15bn annually. Worker injuries affect employee morale, absenteeism, productivity loss and employee turnover, all of which are challenges to the efficient running of a company and are a unnecessary cause of human suffering. By developing a wearable device that can detect high risk lifting activity and provide immediate feedback to workers, safer lifting practices can be promoted and a reduction in the number of unsafe lifts registered, leading to a reduction in injuries.The project includes three main technical objectives: i) the development of machine learning algorithms to detect lifting events from sensor data, and to measure risk related metrics associated to those lifting events. When a lift is considered high risk, real-time feedback will be provided to the worker; ii) the deployment of the device in an industrial setting at several customer sites for 12 months, with the number of high risk lifts performed by workers quantified over time to measure the ability of the system to drive behavior change in the workforce; and ii) the development of a model that can predict the likelihood of musculoskeletal injures based on the risk metrics measured. It is expected that the outcomes of the project demonstrate a significant reduction in the risk of suffering musculoskeletal injuries, paving the way for a clear return on investment value proposition for the industrial companies and their insurance carriers who are potential customers.

这个小型企业创新研究（SBIR）二期项目的目标是证明工业工人使用的离散的、皮带式的互联网连接可穿戴设备可以检测高风险的起重活动，促进安全的起重实践和行为改变，并预测不安全起重造成的肌肉骨骼损伤的风险。每年有超过60万名工人因举重相关活动而遭受肌肉骨骼损伤，这给美国公司造成的损失每年超过150亿美元。工伤影响员工士气、缺勤、生产力损失和员工流动，所有这些都是对公司有效运作的挑战，是人类痛苦的不必要原因。通过开发一种可穿戴设备，可以检测高风险的起重活动，并向工人提供即时反馈，可以促进更安全的起重实践，减少不安全电梯的数量，从而减少伤害。该项目包括三个主要技术目标：i)开发机器学习算法，从传感器数据中检测起重事件，并测量与这些起重事件相关的风险相关指标。当电梯被认为是高风险时，将实时反馈给工人；Ii)在几个客户地点的工业环境中部署该设备12个月，并随时间量化工人进行高风险提升的次数，以衡量系统驱动劳动力行为改变的能力；ii)开发一种模型，可以根据测量的风险指标预测肌肉骨骼损伤的可能性。预计该项目的结果将显著降低遭受肌肉骨骼损伤的风险，为工业公司及其潜在客户的保险公司提供明确的投资回报价值主张铺平道路。