Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks

Lencioni, Tiziana; Carpinella, Ilaria; Rabuffetti, Marco; Marzegan, Alberto; Ferrarin, Maurizio

doi:10.1038/s41597-019-0323-z

Cited by 98 publications

(56 citation statements)

References 40 publications

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“…Generally, these joint torque reference trajectories are obtained from multimodal walking datasets available in the literature. They contain pre-recorded joint trajectories from healthy subjects walking at self-selected speeds (varying between slow, normal, and fast) above force platforms or instrumented treadmills [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

et al. 2021

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Powered Assistive Devices (PADs) have been proposed to enable repetitive, user-oriented gait rehabilitation. They may include torque controllers that typically require reference joint torque trajectories to determine the most suitable level of assistance. However, a robust approach able to automatically estimate user-oriented reference joint torque trajectories, namely ankle torque, while considering the effects of varying walking speed, body mass, and height on the gait dynamics, is needed. This study evaluates the accuracy and generalization ability of two Deep Learning (DL) regressors (Long-Short Term Memory and Convolutional Neural Network (CNN)) to generate user-oriented reference ankle torque trajectories by innovatively customizing them according to the walking speed (ranging from 1.0 to 4.0 km/h) and users’ body height and mass (ranging from 1.51 to 1.83 m and 52.0 to 83.7 kg, respectively). Furthermore, this study hypothesizes that DL regressors can estimate joint torque without resourcing electromyography signals. CNN was the most robust algorithm (Normalized Root Mean Square Error: 0.70 ± 0.06; Spearman Correlation: 0.89 ± 0.03; Coefficient of Determination: 0.91 ± 0.03). No statistically significant differences were found in CNN accuracy (p-value > 0.05) whether electromyography signals are included as inputs or not, enabling a less obtrusive and accurate setup for torque estimation.

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Section: Introductionmentioning

confidence: 99%

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

et al. 2021

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“…One potential method for expanding the available data set is to use simulated IMU sensor data [15]. This would be particularly useful for expanding areas of the data set that were less represented, such as stair and slope walking conditions [44]. Using simulation to generate data of hundreds of different artificial subjects may improve the generalization of DeepBBWAE-Net.…”

Section: Discussionmentioning

confidence: 99%

DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner For Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors In Daily Living

Hossain¹,

Drantez²,

Choi³

et al. 2022

Preprint

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<div>Measurement of human body movement is an essential step in biomechanical analysis. The current standard for human motion capture systems uses infrared cameras to track reflective markers placed on the subject. While these systems can accurately track joint kinematics, the analyses are spatially limited to the lab environment. Though Inertial Measurement Unit (IMU) can eliminate the spatial limitations of the motion capture system, those systems are impractical for use in daily living due to the need for many sensors, typically one per body segment. Due to the need for practical and accurate estimation of joint kinematics, this study implements a reduced number of IMU sensors and employs machine learning algorithm to map sensor data to joint angles. Our developed algorithm estimates hip, knee, and ankle angles in the sagittal plane using two shoe-mounted IMU sensors in different practical walking conditions: treadmill, level overground, stair, and slope conditions. Specifically, we proposed five deep learning networks that use combinations of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) as base learners for our framework. Using those five baseline models, we proposed a novel framework, DeepBBWAE-Net, that implements ensemble techniques such as bagging, boosting, and weighted averaging to improve kinematic predictions. DeepBBWAE-Net predicts joint kinematics for the three joint angles under all the walking conditions with a Root Mean Square Error (RMSE) 6.93-29.0% lower than base models individually. This is the first study that uses a reduced number of IMU sensors to estimate kinematics in multiple walking environments.</div>

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“…In each trial only the central stride (the one on the platform) was analyzed. Since (i) PwMS significantly increased walking speed from T0 to T1 and (ii) there are speed-dependent effects on the timing patterns of muscle activity and on kinematic and kinetic parameters ( 34 ), we extracted two different speed-matched normative datasets across all trials at different speeds recorded from HS. This procedure provides two speed-matched datasets for comparison with trials of PwMS, one for baseline assessment and another dataset for post-treatment trials.…”

Section: Methodsmentioning

confidence: 99%

Improved Gait of Persons With Multiple Sclerosis After Rehabilitation: Effects on Lower Limb Muscle Synergies, Push-Off, and Toe-Clearance

et al. 2020

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Introduction: Persons with MS (PwMS) have markedly reduced push-off and toe-clearance during gait compared to healthy subjects (HS). These deficits may result from alterations in neuromotor control at the ankle. To optimize rehabilitation interventions for PwMS, a crucial step is to evaluate if and how altered neuromotor control, as represented by muscle synergies, improves with rehabilitation. In this study we investigated changes in ankle motor control and associated biomechanical parameters during gait in PwMS, occurring with increase in speed after gait rehabilitation. Methods: 3D motion and EMG data were collected while 11 PwMS (age 50.3 + 11.1; EDSS 5.2 + 1.2) walked overground at self-selected speed before (T0) and after 20 sessions (T1) of intensive treadmill training. Muscle synergies were extracted using non-negative matrix factorization. Gait parameters were computed according to the LAMB protocol. Pearson's correlation coefficient was used to evaluate the similarity of motor modules between PwMS and HS. To assess differences in distal module activations representing neuromotor control at the ankle [Forward Propulsion (FPM) and Ground Clearance modules (GCM)], each module's activation timing was integrated over 100% of the gait cycle and the activation percentage index (API) was computed in six phases. Ten age matched HS provided two separate speed-matched normative datasets for T0 and T1. For speed independent comparison for the PwMs Z scores were calculated for all their gait variables. Results: In PwMS velocity increased significantly from T0 to T1 (0.74–0.90 m/s, p < 0.05). The activation profiles (API) of FPM and GCM of PwMS improved in pre-swing ( p < 0.05): FPM (Mean [95% CI] [%]: T0: 12.5 [5.7–19.3] vs. T1: 9.0 [2.7–15.3]); GCM (T0: 26.7 [18.2–35.3] vs. T1: 24.5 [18.2–30.7]). This was associated with an increase in toe clearance (80.3 to 103.6 mm, p < 0.05) and a higher ankle power peak in pre-swing (1.53–1.93 W/kg, p < 0.05). Conclusion: Increased gait speed of PwMS after intensive gait training was consistent with improvements in spatio-temporal gait parameters. The most important finding of this study was the re-organization of distal leg modules related to neurophysiological changes induced by rehabilitation. This was associated with an improved ankle performance.

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Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks

Cited by 98 publications

References 40 publications

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner For Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors In Daily Living

Improved Gait of Persons With Multiple Sclerosis After Rehabilitation: Effects on Lower Limb Muscle Synergies, Push-Off, and Toe-Clearance

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