Arndt Holger, Burkard Stefan, Talavera Guillermo, Garcia Joan, Castells David, Codina Marc, Hausdorff Jeffrey, Mirelman Anat, Harte Richard, Casey Monica, Glynn Liam, Di Rosa Mirko, Rossi Lorena, Stara Vera, Rösevall John, Rusu Cristina, Carenas Carlos, Breuil Fanny, Reixach Elisenda, Carrabina Jordi
Spring Techno GmbH & Co. KG, Bremen Germany.
Universitat Autònoma de Barcelona, CEPHIS, Barcelona, Spain.
Stud Health Technol Inform. 2017;237:193-197.
Constant monitoring of gait in real life conditions is considered the best way to assess Fall Risk Index (FRI) since most falls happen out of the ideal conditions in which clinicians are currently analyzing the patient's behavior. This paper presents the WIISEL platform and results obtained through the use of the first full-wireless insole devices that can measure almost all gait related data directly on the feet (not in the upper part of the body as most existing wearable solutions). The platform consists of a complete tool-chain: insoles, smartphone & app, server & analysis tool, FRI estimation and user access. Results are obtained by combining parameters in a personalized way to build individual fall risk index assessed by experts with the help of data analytics. New FRI has been compared with standards that validate the quality of its prediction in a statistically significant way. That qualitatively relevant information is being provided to the platform users, being either end-users/patients, relatives or caregivers and the related clinicians to ideally assess about their long term evolution.
在现实生活条件下持续监测步态被认为是评估跌倒风险指数(FRI)的最佳方法,因为大多数跌倒发生在临床医生目前分析患者行为的理想条件之外。本文介绍了WIISEL平台以及通过使用首款全无线鞋垫设备获得的结果,该设备几乎可以直接在脚上测量所有与步态相关的数据(不像大多数现有的可穿戴解决方案那样在身体上部)。该平台由一个完整的工具链组成:鞋垫、智能手机及应用程序、服务器及分析工具、FRI估计和用户访问。通过以个性化方式组合参数来获得结果,以构建由专家借助数据分析评估的个体跌倒风险指数。新的FRI已与以统计学上显著方式验证其预测质量的标准进行了比较。这些定性相关信息被提供给平台用户,无论是终端用户/患者、亲属或护理人员以及相关临床医生,以便理想地评估他们的长期健康状况。