IEEE Trans Neural Syst Rehabil Eng. 2016 Nov;24(11):1210-1217. doi: 10.1109/TNSRE.2016.2532844. Epub 2016 Mar 30.
Sit-to-stand and Stand-to-sit transfers (STS) provide relevant information regarding the functional limitation of mobility-impaired patients. The characterization of STS pattern using a single trunk fixed inertial sensor has been proposed as an objective tool to assess changes in functional ability and balance due to disease. Despite significant research efforts, STS quantification remains challenging due to the high inter- and between- subject variability of this motion pattern. The present study aims to improve the performance of STS detection and classification by fusing the information from barometric pressure (BP) and inertial sensors while keeping a single sensor located at the trunk. A total number of 345 STSs were recorded from 12 post-stroke patients monitored in a semi-structured conditioned protocol. Model-based features of BP signal were combined with kinematic parameters from accelerometer and/or gyroscope and used in a logistic regression-based classifier to detect STS and then identify their types. The correct classification rate was 90.6% with full sensor (BP and inertial) configuration and 75.4% with single inertial sensor. Receiver-Operating-Characteristics analysis was carried out to characterize the robustness of the models. The results demonstrate the potential of BP sensor to improve the detection and classification of STSs when monitoring is performed unobtrusively in every-day life.
坐站和站坐转移（STS）提供了有关行动受限的患者移动功能限制的相关信息。使用单个躯干固定惯性传感器对 STS 模式进行特征描述已被提出作为评估因疾病而导致的功能能力和平衡变化的客观工具。尽管进行了大量研究，但由于这种运动模式的个体间和个体内高度变异性，STS 量化仍然具有挑战性。本研究旨在通过融合气压（BP）和惯性传感器的信息来提高 STS 检测和分类的性能，同时保持位于躯干的单个传感器。从 12 名在半结构化条件协议中监测的中风后患者中记录了总共 345 次 STS。基于模型的 BP 信号特征与加速度计和/或陀螺仪的运动参数相结合，并用于基于逻辑回归的分类器中，以检测 STS 并识别其类型。全传感器（BP 和惯性）配置的正确分类率为 90.6%，单惯性传感器的正确分类率为 75.4%。进行了接收器工作特性分析以表征模型的稳健性。结果表明，当在日常生活中进行非侵入性监测时，BP 传感器具有提高 STS 检测和分类的潜力。
