Abstract
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.
Highlights
Studies have shown that one out of every three American adults aged 65 and older falls each year, often resulting in serious injuries and loss of independence [1]
It can be seen that multi-sensor matched filter (MSMF) and gyroscope peak detection (GPD)-O provided significantly lower cycle variability than the GPD-IFS, GPD-UFS and GPD-U approaches (p < 0.001, p < 0.001, p = 0.007, respectively)
MSMF trended towards a lower variability than GPD-O, not significantly (p = 0.06)
Summary
Studies have shown that one out of every three American adults aged 65 and older falls each year, often resulting in serious injuries and loss of independence [1]. Proactive monitoring of gait can enable early interventions to help prevent falls, but it can provide insights into the health and behaviors of an individual [7,10,11,12] As this necessitates observation of behaviors of everyday life, as opposed to controlled assessments in a lab or clinic with a specialist, recent trends in gait research have focused on enabling continuous monitoring in the community [13,14,15]. This is often promoted using either wearable devices or using existing items that have been specially instrumented [14,16,17,18,19,20,21,22,23]
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