Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons

Nogueira, Samuel L.; Siqueira, Adriano A. G.; Inoue, Roberto S.; Terra, Marco H.

doi:10.3390/s140101835

Cited by 19 publications

(29 citation statements)

References 11 publications

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“…The authors have previously presented a global and cooperative approach to address the limitation of local KF approaches in [6]. In the global models, measurements of all sensors are assumed to be related to each other.…”

Section: Backgoundmentioning

confidence: 99%

Global Kalman filter approaches to estimate absolute angles of lower limb segments

et al. 2017

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BackgroundIn this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF.ResultsThe results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance.ConclusionThe results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0346-7) contains supplementary material, which is available to authorized users.

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Section: Backgoundmentioning

confidence: 99%

Global Kalman filter approaches to estimate absolute angles of lower limb segments

et al. 2017

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“…To discriminate between gait phases, motion capture systems, with the integration of six-component force platforms, are considered the gold standard [ 16 , 17 ] even though they suffer from several limitations, such as soft issue artifacts, the inability to conduct analysis outdoors, and the necessity of a subjective evaluation of phase transition in gait models including more than four phases [ 18 , 19 ]. In order to overcome the previously cited limits, inertial sensors were extensively used in the last decade, due to their low cost, their wearability, and their efficiency in the recognition of gait patterns [ 20 , 21 , 22 ]. In particular, the angular velocities of lower limbs allowed researchers to discriminate between gait phases more accurately with respect to other inertial quantities, such as linear accelerations, due to greater peak-to-peak variability of angular velocity during gait [ 12 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy

Taborri

Scalona

Palermo

et al. 2015

Sensors

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Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs) represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to avoid the intra-subject one in two, four and six gait-phase models in pediatric subjects. The inter-subject procedure consists in the identification of a standardized parameter set to adapt the model to measurements. We tested the inter-subject procedure both on scalar and distributed classifiers. Ten healthy children and ten hemiplegic children, each equipped with two Inertial Measurement Units placed on shank and foot, were recruited. The sagittal component of angular velocity was recorded by gyroscopes while subjects performed four walking trials on a treadmill. The goodness of classifiers was evaluated with the Receiver Operating Characteristic. The results provided a goodness from good to optimum for all examined classifiers (0 < G < 0.6), with the best performance for the distributed classifier in two-phase recognition (G = 0.02). Differences were found among gait partitioning models, while no differences were found between training procedures with the exception of the shank classifier. Our results raise the possibility of avoiding subject-specific training in HMM for gait-phase recognition and its implementation to control exoskeletons for the pediatric population.

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“…Alternatively, the detection of human motion can be evaluated by means of self-contained wearable systems, which do not rely on camera-based systems and can be also used outdoors for continuous data logging. The most used sensors are: wireless pressure sensing shoe insoles [ 11 , 12 ], shoe-mounted foot switches [ 13 ], smart textiles [ 14 ], accelerometers [ 15 ], gyroscopes [ 5 , 16 , 17 ], and the composite inertial measurement unit system IMU [ 18 , 19 ]. The cited references represent a few of the numerous works available in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

Taborri

Rossi

Palermo

et al. 2014

Sensors

116

101

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In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

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Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons

Cited by 19 publications

References 11 publications

Global Kalman filter approaches to estimate absolute angles of lower limb segments

Global Kalman filter approaches to estimate absolute angles of lower limb segments

Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

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