Measurement of Human Gait Using a Wearable System with Force Sensors and Inertial Sensors

Li, Guangyi; Liu, Tao; Inoue, Yoshio

doi:10.1007/978-3-319-18191-2_12

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Optical motion capture systems for gait analysis perform well in indoor settings but fail to perform in outdoor scenarios, as the systems are sensitive to varying lighting conditions commonly witnessed in the natural environment. A suitable solution is to use inertial sensors that are low cost and equally applicable to outdoor activities as they also perform well in natural light [37].…”

Section: Literature Reviewmentioning

confidence: 99%

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Gohar

Riaz

Shahzad

et al. 2020

Sensors

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Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual’s motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one’s inertial sensor data.

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Section: Literature Reviewmentioning

confidence: 99%

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Gohar

Riaz

Shahzad

et al. 2020

Sensors

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“…Bae et al [5] presented a mobile gait monitoring system for the diagnosis of abnormal gait and rehabilitation. In [6], the authors proposed a wireless wearable sensor system for successive non-laboratory measurement of ground reaction force, joint angles, joint forces, and joint moments. Liu et al [7] developed a wearable ground reaction force sensor system to measure CoP and triaxial GRF in a number of walking trials.…”

Section: S606mentioning

confidence: 99%

Estimation of ground reaction forces and joint moments on the basis on plantar pressure insoles and wearable sensors for joint angle measurement

Ostaszewski¹,

Pauk²

2018

THC

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Abstract.BACKGROUND: Gait analysis is a useful tool medical staff use to support clinical decision making. There is still an urgent need to develop low-cost and unobtrusive mobile health monitoring systems. OBJECTIVE: The goal of this study was twofold. Firstly, a wearable sensor system composed of plantar pressure insoles and wearable sensors for joint angle measurement was developed. Secondly, the accuracy of the system in the measurement of ground reaction forces and joint moments was examined. METHODS: The measurements included joint angles and plantar pressure distribution. To validate the wearable sensor system and examine the effectiveness of the proposed method for gait analysis, an experimental study on ten volunteer subjects was conducted. The accuracy of measurement of ground reaction forces and joint moments was validated against the results obtained from a reference motion capture system. RESULTS: Ground reaction forces and joint moments measured by the wearable sensor system showed a root mean square error of 1% for min. GRF and 27.3% for knee extension moment. The correlation coefficient was over 0.9, in comparison with the stationary motion capture system. CONCLUSIONS: The study suggests that the wearable sensor system could be recommended both for research and clinical applications outside a typical gait laboratory.

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Measurement of Human Gait Using a Wearable System with Force Sensors and Inertial Sensors

Cited by 2 publications

References 19 publications

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Estimation of ground reaction forces and joint moments on the basis on plantar pressure insoles and wearable sensors for joint angle measurement

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