Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network

Moon, Kiwon; Lee, Sung Q; Öztürk, Yusuf; Gaidhani, Apoorva; Cox, Jeremiah A.

doi:10.3390/s19225024

Cited by 12 publications

(8 citation statements)

References 25 publications

(24 reference statements)

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“…In general, IMUs consist of accelerometers, gyroscopes, and magnetometers. In [ 37 ], gait cycles of a user were monitored by a sensor network that used a pair of wireless IMU sensors. In [ 10 ], gait information was collected from five IMUs placed on the chest, lower back, right wrist, knee, and ankle of the participants.…”

Section: Introductionmentioning

confidence: 99%

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Moon

Minaya

et al. 2020

Sensors

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Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%.

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Section: Introductionmentioning

confidence: 99%

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Moon

Minaya

et al. 2020

Sensors

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“…To avoid limitations of OMC systems like the need for expensive equipment in a controlled environment and time-consuming data processing, previous studies have developed different algortithms to estimate joint kinematics from IMUs [44][45][46][47][48][49][50][51][52][53][54][55]. One of these algorithms was using ltering approaches to cope with IMU sensor noise and integration drift [44,[52][53][54][55].…”

Section: Discussionmentioning

confidence: 99%

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

Moghadam

Yeung

Choisne

2022

Preprint

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Gait analysis outside the laboratory has been possible by recent advancements in wearable sensors like inertial measurement units (IMUs) and Electromypgraphy (EMG) sensors. The aim of this study was to compare performance of four different non-linear regression machine learning (ML) models to estimate lower-limb joints’ kinematics, kinetics, and muscle forces using IMUs and EMGs’ data. Seventeen healthy volunteers (9F, 28 ± 5 yrs) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets) as well as 7 IMUs and 16 EMGs. The most important features from sensors’ data were extracted using Tsfresh python package and fed into 4 ML models; Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) for targets’ prediction. The RF model outperformed the other ML models by providing lower prediction errors in all intended targets. This study suggested that a combination of wearable sensors’ data with an RF model is a promising tool to overcome limitations of traditional optical motion capture for 3D gait analysis.

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“…When people walk in their natural form, their gait/footstep can be detected by mobile devices carried on them (Hanlon and Anderson 2009) through the Inertial Measurement Unit (IMU). AutoQual leverages co-located mobile device to detect the timing of the footfall, which has been explored for position and gait cycle detection (Mokaya et al 2013;Grimmer et al 2019;Moon et al 2019). In this work, we use a three-axis accelerometer sensor on the calf to measure the pedestrian's footstep timing.…”

Section: Gait-based Mobile-infrastructural Sensing Signal Temporal Associationmentioning

confidence: 99%

AutoQual: task-oriented structural vibration sensing quality assessment leveraging co-located mobile sensing context

Zhang

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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In this paper, we introduce AutoQual, a mobile-based assessment scheme for infrastructure sensing task performance prediction under new deployment environments. With the growth of the Internet-of-Things (IoT), many non-intrusive sensing systems have been explored for various indoor applications, such as structural vibration sensing. This indirect sensing approach’s learning performance is prone to deployment variance when signals propagate through the environment. As a result, current systems heavily rely on expert knowledge and manual assessment to achieve effective deployments and high sensing task performance. In order to mitigate this expert effort, we propose to systematically study factors that reflect deployment environment characteristics and methods to measure them autonomously. We present AutoQual that measures a series of assessment factors (AFs) reflecting how the deployment environment impacts the system performance. AutoQual outputs a task-oriented sensing quality (TSQ) score by integrating measured AFs trained from known deployments as a prediction of untested system’s performance. In addition, AutoQual achieves this assessment without manual effort by leveraging co-located mobile sensing context to extract structural vibration signal for processing automatically. We evaluate AutoQual by using it to predict untested systems’ performance over multiple sensing tasks. We conduct real-world experiments and investigate 48 deployments in 11 environments. AutoQual achieves less than 0.10 average absolute error when auto-assessing multiple tasks at untested deployments, which shows a $$\le 0.018$$ ≤ 0.018 absolute error difference compared to the manual assessment approach.

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Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network

Cited by 12 publications

References 25 publications

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

AutoQual: task-oriented structural vibration sensing quality assessment leveraging co-located mobile sensing context

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