Validation of a Gait Analysis Algorithm for Wearable Sensors

Pierleoni, Paola; Belli, Alberto; Palma, Lorenzo; Mercuri, Marco; Verdini, Federica; Fioretti, Sandro; Madgwick, Sebastian; Pinti, Federica

doi:10.1109/issi47111.2019.9043647

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…3) Challenges in wearable sensors application and the importance of sensor placement: We found that gyro drift is a major concern when using IMU-based sensors. There are several methods for reducing or removing the drift, such as using the KF [36], [65], [68], [104] and applying zero velocity update [65], [81], [90], [101], which are widely implemented in the studies examined in this review. Environmental interference, which affects the magnetometer, is also a problems observed in this review.…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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The current gold standard for gait analysis involves performing the gait experiments in a laboratory environment with a constrained space. However, there is growing interest in using flexible, efficient, and inexpensive wearable sensors as tools to perform gait analysis. This review aimed to identify and summarize the current advances in wearable sensors for various aspects of gait analysis, such as the application of wearable gait analysis systems, sensor systems and their attachment locations, and the algorithms used for the analysis. The PRISMA guideline was adopted to find relevant studies from the period 2011 to 2020 from several scientific databases. A total of 76 articles were selected based on the inclusion and exclusion criteria. A wearable inertial measurement unit (IMU) attached to the lower limb region was found to be the most common approach for gait analysis. Temporal, spatial, and spatiotemporal features were the most common quantitative gait features extracted from the wearable sensors. The proposed frameworks showed varying performances, and an increased number of sensors did not necessarily improve the estimation performance metrics. A few studies have integrated various machine learning techniques for classification problems, correction algorithms, crosschecking functions, and scoring functions. Finally, this review paper discusses the challenges and future direction of the research on quantitative gait analysis.

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Section: Discussionmentioning

confidence: 99%

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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“…The work from Li and colleagues [21] validated a multi-sensor system including, for each foot, a force sensor, an IMU and a range sensor using the SP system as reference system, but errors were 9.34% for stride length and 5.90% for stride velocity. In [22], Agostini et al compared the performances of two IMUs with those of a footswitch-based system (STEP 32 footswitches), obtaining errors below 5% for cadence and stride time; and in [23], validated two feetmounted IMUs against SP system obtaining average errors of 5.9% for stride length and 6.3% for stride speed. Panero et al [24] validated two methods, reporting only average error values obtained while using a single lower back IMU (0.01s on stride time) and for two shank mounted IMUs (0s on stride time).…”

Section: Discussionmentioning

confidence: 99%

A wearable multi-sensor system for real world gait analysis

Salis

Bertuletti

Scott

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Gait analysis is commonly performed in standardized environments, but there is a growing interest in assessing gait also in ecological conditions. In this regard, an important limitation is the lack of an accurate mobile gold standard for validating any wearable system, such as continuous monitoring devices mounted on the trunk or wrist. This study therefore deals with the development and validation of a new wearable multi-sensor-based system for digital gait assessment in free-living conditions. In particular, results obtained from five healthy subjects during lab-based and realworld experiments were presented and discussed. The in-lab validation, which assessed the accuracy and reliability of the proposed system, shows median percentage errors smaller than 2% in the estimation of spatio-temporal parameters. The system also proved to be easy to use, comfortable to wear and robust during the out-of-lab acquisitions, showing its feasibility for free-living applications. I. INTRODUCTIONRecent literature has shown the relevance of characterising an individual's mobility in real-world conditions for a complete assessment of typical motor abilities [1,2]. This requires the use of activity monitors, e.g. devices including a single inertial measurement unit (IMU), that can be used without causing discomfort thanks to its limited invasivity. In this sense, the most convenient body positionings are trunk and wrist [3]. However, those locations present criticalities for the analysis of gait in terms of reliability, since the farther from the contact point the IMU is placed, the more difficult the estimation of gait-related parameters is. In this respect, the trunk is far from the ground but near to the centre of mass while the wrist is far from both ground and centre of mass. Although the scientific community is actively working on developing and improving algorithms for the above-mentioned solutions, algorithms validation is still performed in the laboratory while capturing simple gait tasks in spatially and temporally limited observation windows [4,5]. Testing single-sensor algorithms outside the laboratory would require a wearable system that is robust and accurate enough to be used as reference in validating other wearable technologies, i.e. a mobile gold standard

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“…The foot was assumed to be fully flat on the ground (FF) when the MSw event of the contralateral leg occurred, as described by Uchitomi et al (2022 ). Note that FF events are commonly detected by using the magnitude of the acceleration vector measured by the IMUs ( Pierleoni et al, 2019 ), although it has been shown that angular velocity–based algorithms perform significantly better than acceleration-based algorithms, especially for pathological gait ( Arens et al, 2021 ; Laidig et al, 2021 ; Uchitomi et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study

De Miguel-Fernández,

Salazar-Del Rio,

Rey-Prieto

et al. 2023

Front. Bioeng. Biotechnol.

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Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user’s performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS).Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957).Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70–0.80; MAE = 0.48–0.58 Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7 m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00–0.19; MAE = 1.15–1.29 Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter.Discussion: Our preliminary results indicate that the stride-by-stride estimations of shank-worn IMUs show potential to design automatic and adaptive exoskeleton control strategies for people with moderate impairments in gait function due to stroke.

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Validation of a Gait Analysis Algorithm for Wearable Sensors

Cited by 9 publications

References 23 publications

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

A wearable multi-sensor system for real world gait analysis

Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study

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