SBIR Phase I: Comprehensive, Human-Centered, Safety System Using Physiological and Behavioral Sensing to Predict and Prevent Workplace Accidents

SBIR 第一阶段：利用生理和行为感知来预测和预防工作场所事故的综合性、以人为本的安全系统

• 批准号: 2321538
• 负责人: Daniel Timco
• 金额: $ 27.34万
• 依托单位: INTELLISAFE ANALYTICS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321538&HistoricalAwards=false
• 关键词: SBIR; Phase; Comprehensive; Human; Centered

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to better protect workers from hazards in the workplace through the use of wearable technology to identify and predict accidents. Human-factor related accidents account for 80% of injuries and are not being addressed with currently available safety products. This solution utilizes wearable technology to automate the collection of physiological and behavioral data from workers. The data is incorporated into machine learning models to identify safety incidents and near-misses. This innovative approach to worker safety enhances scientific and technological understanding by using machine learning to interpret signals generated by a worker’s physiology and behaviors. Responses to hazards in the workplace are used to trigger alerts that predict and prevent workplace accidents. This safety system provides the basis for machine learning models that predict the likelihood of accidents so safety personnel can intervene before the worker is injured. The goal of this project is to prevent injuries, save lives, and enable companies to realize savings in insurance costs, liabilities, and lost time from the job.This SBIR Phase I project aims to develop a safety system that uses the human body’s built-in sensors to identify safety hazards. By automating the continuous collection of real-time physiological and behavioral data using wearable technology, machine learning models will be developed to identify safety incidents, enabling the prediction and prevention of accidents. The intellectual merit of the research is to: 1) verify that humans respond in similar, measurable ways to slips and trips, 2) develop machine learning models to accurately identify slips and trips and their intensity, 3) develop machine learning models to assess the risk of future safety accidents, and 4) verify that data can be processed through the entire workflow to provide real-time alerts to the worker and safety personnel. Data will be collected from human subjects subjected to slips and trips using research-grade wearables. The anticipated output of this research will provide the basis for a safety system used to trigger safety alerts and identify risk levels to save lives and prevent accidents related to slips and trips.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响是通过使用可穿戴技术来识别和预测事故，更好地保护工人免受工作场所的危险。与人为因素相关的事故占受伤事故的 80%。目前可用的安全产品尚未解决这一问题。该解决方案利用可穿戴技术自动收集工人的生理和行为数据，并将这些数据纳入机器学习模型中，以识别安全事件和未遂事故。安全提升科技通过使用机器学习解释工人的生理和行为产生的信号来理解对工作场所危险的反应，从而触发预测和预防工作场所事故的警报。该安全系统为预测事故可能性的机器学习模型提供了基础。因此安全人员可以在工人受伤之前进行干预。该项目的目标是防止伤害、拯救生命，并使公司能够节省保险成本、责任和工作时间。该 SBIR 第一阶段项目旨在开发一个使用人类的安全系统通过使用可穿戴技术自动连续收集实时生理和行为数据，将开发机器学习模型来识别安全事件，从而实现预测和预防事故的智能优点。该研究的目的是：1) 验证人类对滑倒和绊倒是否以类似、可测量的方式做出反应，2) 开发机器学习模型以准确识别滑倒和绊倒及其强度，3) 开发机器学习模型以评估未来安全风险事故，以及4) 验证数据可以通过整个工作流程进行处理，以便向工人和安全人员提供实时警报，该数据将使用研究级可穿戴设备从遭受滑倒和绊倒的人类受试者中收集。为用于触发安全警报和识别风险级别的安全系统提供基础，以拯救生命并防止与滑倒和绊倒相关的事故。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持影响审查标准。