Gait-based Person Identification using Multiple Inertial Sensors

Adel, Osama; Nafea, Yousef; Hesham, Ahmed; Gomaa, Walid

doi:10.5220/0009791506210628

Cited by 7 publications

(2 citation statements)

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“…In many research studies, gait parameters are extracted from multiple places, such as the hip, knee and ankle joints, and researchers investigate combining the data collected from the different sites [18] Furthermore, other studies have stated how results could be enhanced by using multiple sensors in gait analysis and gait classification [19,20]. Gao et al have identified the benefits of using multiple sensors with light-weight algorithms as compared with using single wearable sensors with computationally demanding processing to train complex classifiers [21].…”

Section: Introductionmentioning

confidence: 99%

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

Jayasinghe

Hwang

Harwin

2022

Sensors

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A person’s walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the clothing data, and it was possible to distinguish the stance and swing phases of walking based on features in the clothing data. Furthermore, simultaneously recording data from the waist, thigh, and shank was helpful in capturing the movement of the whole leg.

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Section: Introductionmentioning

confidence: 99%

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

Jayasinghe

Hwang

Harwin

2022

Sensors

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“…For person identification, a single sensor on the pelvis [27], [28], foot [19] and ankles [29] have been illustrated. The effect of sensor location on identification performance has been discussed in [30] and more recently in [31], however, the number of subjects used is not enough in both ( 10 and 20 respectively) and they use complete stride for classification instead of a fraction. To the best of the author's knowledge, the comparison of different gait phases in terms of person identification accuracy has not been clearly investigated in the literature.…”

Section: Introductionmentioning

confidence: 99%

Gait-based Human Identification through Minimum Gait-phases and Sensors

Arshad¹,

Jung²,

Park³

et al. 2021

Preprint

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The incredible pace at which the world's elderly population is growing will put severe burdens on current healthcare systems and resources. To alleviate this concern the health care systems must rely on the transformation of eldercare and old homes to use Ambient Assisted Living (AAL). Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in such environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to physical appearances. However, an important aspect of gait identification through wearables and image-based systems alike is accurate identification when limited information is available for example, when only a fraction of the whole gait cycle or only a part of the subject's body is visible. In this paper, we present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features and we investigate the performance of using only single gait phases for the identification task using a minimum number of sensors. Gait data were collected from 60 individuals through pelvis and foot sensors. Six different machine learning algorithms were used for identification. It was shown that it is possible to achieve high accuracy of over 95.5% by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100% identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined. The ANN was found to be more robust to less number of data features compared to SVM and was concluded as the best machine algorithm for the purpose.

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Deep Architectures in Visual Transfer Learning

Gomaa

2021

Vision, Sensing and Analytics: Integrative Approaches

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Gait-based Person Identification using Multiple Inertial Sensors

Cited by 7 publications

References 0 publications

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

Gait-based Human Identification through Minimum Gait-phases and Sensors

Deep Architectures in Visual Transfer Learning

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