A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait
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 indiv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/9/2970 |
| _version_ | 1828154268102688768 |
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| author | Satinder Gill Nitin Seth Erik Scheme |
| author_facet | Satinder Gill Nitin Seth Erik Scheme |
| author_sort | Satinder Gill |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:37:32Z |
| format | Article |
| id | doaj.art-21128770a65848c8b84f301fab254663 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:37:32Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-21128770a65848c8b84f301fab2546632022-12-22T03:59:09ZengMDPI AGSensors1424-82202018-09-01189297010.3390/s18092970s18092970A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted GaitSatinder Gill0Nitin Seth1Erik Scheme2Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, CanadaInstitute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, CanadaInstitute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, CanadaIndividuals 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.http://www.mdpi.com/1424-8220/18/9/2970multi-sensorassistive devicecanegait analysisloading informationinertial measurement unit (IMU)stride segmentation |
| spellingShingle | Satinder Gill Nitin Seth Erik Scheme A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait Sensors multi-sensor assistive device cane gait analysis loading information inertial measurement unit (IMU) stride segmentation |
| title | A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait |
| title_full | A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait |
| title_fullStr | A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait |
| title_full_unstemmed | A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait |
| title_short | A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait |
| title_sort | multi sensor matched filter approach to robust segmentation of assisted gait |
| topic | multi-sensor assistive device cane gait analysis loading information inertial measurement unit (IMU) stride segmentation |
| url | http://www.mdpi.com/1424-8220/18/9/2970 |
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