Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada.
Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada.
Sensors (Basel). 2018 Sep 6;18(9):2970. doi: 10.3390/s18092970.
Individuals with mobility impairments related to age, injury, or disease, often require the help of an assistive device (AD) such as a cane to ambulate, increase safety, and improve overall stability. Instrumenting these devices has been proposed as a non-invasive way to proactively monitor an individual's reliance on the AD while also obtaining information about behaviors and changes in gait. A critical first step in the analysis of these data, however, is the accurate processing and segmentation of the sensor data to extract relevant gait information. In this paper, we present a highly accurate multi-sensor-based gait segmentation algorithm that is robust to a variety of walking conditions using an AD. A matched filtering approach based on loading information is used in conjunction with an angular rate reversal and peak detection technique, to identify important gait events. The algorithm is tested over a variety of terrains using a hybrid sensorized cane, capable of measuring loading, mobility, and stability information. The reliability and accuracy of the proposed multi-sensor matched filter (MSMF) algorithm is compared with variations of the commonly employed gyroscope peak detection (GPD) algorithm. Results of an experiment with a group of 30 healthy participants walking over various terrains demonstrated the ability of the proposed segmentation algorithm to reliably and accurately segment gait events.
个体由于年龄、损伤或疾病而导致行动能力受损，通常需要借助助行器（AD），如拐杖来行走，以提高安全性并改善整体稳定性。为了主动监测个体对 AD 的依赖程度，同时获取有关行为和步态变化的信息，有人提出对这些设备进行仪器化。然而，在分析这些数据时，至关重要的第一步是准确处理和分割传感器数据，以提取相关的步态信息。在本文中，我们提出了一种高度精确的基于多传感器的步态分割算法，该算法使用 AD 对各种行走条件具有鲁棒性。该算法使用基于加载信息的匹配滤波方法，并结合角速率反转和峰值检测技术，来识别重要的步态事件。该算法在使用混合传感器化拐杖的各种地形上进行了测试，该拐杖能够测量负载、移动性和稳定性信息。我们还将提出的多传感器匹配滤波器（MSMF）算法的可靠性和准确性与常用的陀螺仪峰值检测（GPD）算法的变体进行了比较。对 30 名健康参与者在各种地形上行走的实验结果表明，所提出的分割算法能够可靠且准确地分割步态事件。
