Developing a method for quantifying hip joint angles and moments during walking using neural networks and wearables

McCabe, Megan V.; Citters, Douglas W. Van; Chapman, Ryan M.

doi:10.1080/10255842.2022.2044028

Cited by 9 publications

(6 citation statements)

References 57 publications

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“…Force-instrumented insoles were selected in favor of force plates in order to collect repeated gait cycles while performing stair ascent. As in prior investigations, the wearable-ANN approach would be considered successful if average rRMSE was less than 13% [28,31]. Thus, we hypothesized that all variables (sagittal angle, sagittal moment, frontal angle, frontal moment) would achieve rRMSE < 13%.…”

Section: Introductionmentioning

confidence: 90%

“…In traditional biomechanical modeling, this requires precise fixation to patients, is time consuming, and necessitates detailed coordinate transformations to transform wearable data captured in local coordinate frames to an anatomic frame of reference for kinematics/kinetics [25][26][27]. However, prior work in our own lab leveraged a machine learning approach to calculate 2D (sagittal, frontal planes) joint angles/moments from one shin-mounted IMU, one force-instrumented insole, and a simplified artificial neural network (ANN) with two hidden layers and five nodes per layer [28].…”

Section: Introductionmentioning

confidence: 99%

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Hip Joint Angles and Moments during Stair Ascent Using Neural Networks and Wearable Sensors

2023

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End-stage hip joint osteoarthritis treatment, known as total hip arthroplasty (THA), improves satisfaction, life quality, and activities of daily living (ADL) function. Postoperatively, evaluating how patients move (i.e., their kinematics/kinetics) during ADL often requires visits to clinics or specialized biomechanics laboratories. Prior work in our lab and others have leveraged wearables and machine learning approaches such as artificial neural networks (ANNs) to quantify hip angles/moments during simple ADL such as walking. Although level-ground ambulation is necessary for patient satisfaction and post-THA function, other tasks such as stair ascent may be more critical for improvement. This study utilized wearable sensors/ANNs to quantify sagittal/frontal plane angles and moments of the hip joint during stair ascent from 17 healthy subjects. Shin/thigh-mounted inertial measurement units and force insole data were inputted to an ANN (2 hidden layers, 10 total nodes). These results were compared to gold-standard optical motion capture and force-measuring insoles. The wearable-ANN approach performed well, achieving rRMSE = 17.7% and R2 = 0.77 (sagittal angle/moment: rRMSE = 17.7 ± 1.2%/14.1 ± 0.80%, R2 = 0.80 ± 0.02/0.77 ± 0.02; frontal angle/moment: rRMSE = 26.4 ± 1.4%/12.7 ± 1.1%, R2 = 0.59 ± 0.02/0.93 ± 0.01). While we only evaluated healthy subjects herein, this approach is simple and human-centered and could provide portable technology for quantifying patient hip biomechanics in future investigations.

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Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Hip Joint Angles and Moments during Stair Ascent Using Neural Networks and Wearable Sensors

2023

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“…Similar studies were used to choose the number of the hidden layer and transfer function [19,20,33]. We used hyperbolic tangent function for hidden layer because hyperbolic tangent function provides greater sensitivity to input variability and larger working range than sigmoid function [29]. We also used linear transfer function for the output layer.…”

Section: Annmentioning

confidence: 99%

“…Some studies have used ANN with inertial sensor to classify gait, estimate GRF, and orientation of segment [17,[26][27][28]. Most researchers investigated particular ankle, knee, and hip moment in sagittal plane [12], hip moment in sagittal and frontal plane [29], hip moment in frontal plane [30], knee moment in sagittal [31] or frontal plane [13], ankle moment in frontal and sagittal planes [32], and lower limb joints moment in all three planes [18,33]. The previous studies utilized a few subjects and a recent review article by Xiang et al has shown to utilize more subjects (more than 20) [34] and we utilized 73 subjects.…”

Section: Introductionmentioning

confidence: 99%

Continuous estimation of kinetic variables while walking using an accelerometer and artificial neural networks

Mantashloo

Abbasi

Tazji

et al. 2023

Meas. Sci. Technol.

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Measuring the gait variables outside the laboratory is so important because they can be used to analyze walking in the long run and during real life situations. Wearable sensors like accelerometer show high potential in these applications. So, the aim of this study was continuous estimation of kinetic variables while walking using an accelerometer and artificial neural networks. Seventy-three subjects (26 women and 47 men) voluntarily participated in this study. The subjects walked at the slow, moderate, and fast speeds on a walkway which covered with 10 Vicon camera. Acceleration was used as input for a Feedforward Neural Networks (FFNN) to predict the lower limb moments (in sagittal, frontal, and transverse planes), power, and Grand Reaction Force (GRF) (in medial-lateral (M-L), anterior-posterior (A-P), and vertical directions during walking. Normalized Root Mean Square Error (nRMSE), and Pearson correlation coefficient () were computed between the measured and predicted variables. Statistical Parametric Mapping (SPM) was used to compare the measured and predicted variables. The results of this study showed approximately values of 91 to 99 and nRMSE values of 4 to 15 percent for GRF, power, and moment between the measured and predicted data. The SPM showed no significant difference between the measured and predicted variables in throughout stance phase. This work has shown the potential of predicting kinetic variables (GRF, moment, and power) in various speeds of walking using the accelerometer. The proposed estimation procedure utilizing a mixture of biomechanics and Artificial Neural Networks (ANN) can be used to solve the tradeoff between richness of data and ease of measuring inherent in wearable sensors

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“…Dorschky et al also found that the addition of synthetic IMU data improved their model predictions [13]. McCabe et al incorporated a force-measuring insole with an IMU on the shank to predict hip joint loading along with kinematics using a neural network model [14]. However, these studies were restricted to walking and treadmill activities.…”

Section: Introductionmentioning

confidence: 99%

BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors

Sharifi-Renani,

Mahoor,

Clary

2023

Sensors

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Through wearable sensors and deep learning techniques, biomechanical analysis can reach beyond the lab for clinical and sporting applications. Transformers, a class of recent deep learning models, have become widely used in state-of-the-art artificial intelligence research due to their superior performance in various natural language processing and computer vision tasks. The performance of transformer models has not yet been investigated in biomechanics applications. In this study, we introduce a Biomechanical Multi-activity Transformer-based model, BioMAT, for the estimation of joint kinematics from streaming signals of multiple inertia measurement units (IMUs) using a publicly available dataset. This dataset includes IMU signals and the corresponding sagittal plane kinematics of the hip, knee, and ankle joints during multiple activities of daily living. We evaluated the model’s performance and generalizability and compared it against a convolutional neural network long short-term model, a bidirectional long short-term model, and multi-linear regression across different ambulation tasks including level ground walking (LW), ramp ascent (RA), ramp descent (RD), stair ascent (SA), and stair descent (SD). To investigate the effect of different activity datasets on prediction accuracy, we compared the performance of a universal model trained on all activities against task-specific models trained on individual tasks. When the models were tested on three unseen subjects’ data, BioMAT outperformed the benchmark models with an average root mean square error (RMSE) of 5.5 ± 0.5°, and normalized RMSE of 6.8 ± 0.3° across all three joints and all activities. A unified BioMAT model demonstrated superior performance compared to individual task-specific models across four of five activities. The RMSE values from the universal model for LW, RA, RD, SA, and SD activities were 5.0 ± 1.5°, 6.2 ± 1.1°, 5.8 ± 1.1°, 5.3 ± 1.6°, and 5.2 ± 0.7° while these values for task-specific models were, 5.3 ± 2.1°, 6.7 ± 2.0°, 6.9 ± 2.2°, 4.9 ± 1.4°, and 5.6 ± 1.3°, respectively. Overall, BioMAT accurately estimated joint kinematics relative to previous machine learning algorithms across different activities directly from the sequence of IMUs signals instead of time-normalized gait cycle data.

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Developing a method for quantifying hip joint angles and moments during walking using neural networks and wearables

Cited by 9 publications

References 57 publications

Hip Joint Angles and Moments during Stair Ascent Using Neural Networks and Wearable Sensors

Hip Joint Angles and Moments during Stair Ascent Using Neural Networks and Wearable Sensors

Continuous estimation of kinetic variables while walking using an accelerometer and artificial neural networks

BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors

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