Institute of Environmental Medicine, Karolinska Institutet, Solnavägen 1, 17177 Solna, Sweden.
School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, 14157 Huddinge, Sweden.
Sensors (Basel). 2019 Mar 11;19(5):1225. doi: 10.3390/s19051225.
Preventive healthcare has attracted much attention recently. Improving people's lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals' work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability.
最近，预防保健引起了广泛关注。改善人们的生活方式、促进健康饮食和健康是很重要的，但不应忽视与工作相关的疾病的重要性。肌肉骨骼疾病（MSD）是最常见的与工作相关的健康问题之一。人类工效学家已经评估了 MSD 的风险因素，并建议对工作场所进行改变。然而，现有的方法主要基于视觉观察，其可靠性相对较低，仅涵盖工作日的一部分。这些建议涉及整个工作场所和工作组织，但很少包括个人的工作技术。在这项工作中，我们提出了一个精确和普及的人机工程学平台，用于持续风险评估。该系统从可穿戴传感器收集数据，这些数据由移动计算层同步和处理，从中可以得出暴露统计数据和风险评估，最后存储在服务器层，以便在个人和群体层面进行进一步分析。该平台还可以为工人提供持续的反馈，以支持行为改变。在亚马逊网络服务实例中部署的云平台显示出足够的系统灵活性，可以负担得起中小企业的要求，同时也可以扩展到更大的公司。系统可用性量表的 76.6 分表明了可接受的可用性等级。
