Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking

Choi, Ahnryul; Jung, Hyunwoo; Mun, Joung Hwan

doi:10.3390/s19132974

Cited by 30 publications

(25 citation statements)

References 43 publications

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“…LSTM networks have been used in this manner to analyze a variety of sequential data, ranging from natural language processing (Wang and Jiang, 2016) to stock market forecasting (Selvin et al, 2017). In the field of biomechanics, LSTM networks have been used to make frame-by-frame predictions of GRF waveforms using motion capture data (Mundt et al, 2020) and predictions of the center of mass position relative to center of pressure from IMU data during walking (Choi, Jung and Mun, 2019). To our knowledge, LSTM networks have not been used to predict GRF waveforms from wearable device data exclusively during running.…”

Section: Introductionmentioning

confidence: 99%

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Alcantara

Edwards

Millet

et al. 2021

Preprint

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BackgroundGround reaction forces (GRFs) are important for understanding the biomechanics of human movement but the measurement of GRFs is generally limited to a laboratory environment. Wearable devices like accelerometers have been used to measure biomechanical variables outside the laboratory environment, but they cannot directly measure GRFs. Previous studies have used neural networks to predict the entire GRF waveform during the stance phase from wearable device data, but these networks require normalization of GRFs to the duration of a step or stance phase, resulting in a loss of the GRF waveform’s temporal component. Additionally, previous studies have predicted GRF waveforms during level-ground, but not uphill or downhill running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes, while maintaining the GRF waveform’s temporal component, could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment.PurposeWe sought to develop a recurrent neural network capable of predicting normal GRF waveforms across a range of running speeds and slopes using data from accelerometers located on the sacrum and shoe.Methods19 subjects completed 30-s running trials on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, and 4.17 m/s) per slope. One biaxial accelerometer was adhered to the sacrum and two uniaxial accelerometers were adhered to the right shoe during all trials. Accelerometers on the shoe were used to classify foot strike patterns as rearfoot, midfoot, or forefoot, and sacral acceleration data were divided into overlapping 12-ms windows, allowing the neural network to iteratively predict the experimentally-measured normal GRF waveform frame-by-frame. The mean, SD, and range of accelerometer data for each 12-ms window were included as neural network input features, along with the subject’s body mass, height, running speed, slope, and percentage of a trial’s steps classified as a rearfoot, midfoot, or forefoot strike. We assessed the accuracy and generalizability of the neural network using leave-one-subject-out cross validation, which provided an ensemble of Root Mean Square Error (RMSE) and relative RMSE (rRMSE) values comparing the normal GRF waveform predicted by the neural network to the normal GRF waveform measured by the force-measuring treadmill. Additionally, we calculated the mean absolute percent error (MAPE) of step frequency, contact time, normal impulse, normal GRF active peak, and loading rate between the predicted and measured GRF waveforms.ResultsThe average ± SD RMSE was 0.16 ± 0.04 BW and rRMSE was 6.4 ± 1.5% for neural network predictions of each subject’s normal GRF waveform compared to measured GRF waveforms across all conditions. RMSE values were lower during slow uphill running (2.5 m/s, +10°; 0.13 ± 0.07 BW) compared to fast downhill running (4.17 m/s, −10°; 0.20 ± 0.05 BW). The MAPE ± SD for step frequency was 0.1 ± 0.1%, contact time was 4.9 ± 4.0%, normal impulse was 6.4 ± 6.9%, normal GRF active peak was 8.5 ± 8.2%, and loading rate was 27.6 ± 36.1%.ConclusionsWe developed a recurrent neural network that uses accelerometer data to predict the continuous normal GRF waveform across a range of running speeds and slopes. The neural network does not require preliminary identification of the stance phase, maintains the temporal component of the GRF waveform, can be applied to up- and downhill running, and facilitates the prediction of kinetic and kinematic variables outside the laboratory environment. This represents a substantial step towards accurately quantifying and monitoring the external loads experienced by the body when running outdoors.

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Section: Introductionmentioning

confidence: 99%

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Alcantara

Edwards

Millet

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Deeper and more specific analyses should be performed to accurately explore this topic. Moreover, it would be of interest to evaluate if the application of methods based on machine learning techniques, as already proposed by some authors in this field [ 42 , 54 , 55 ], could enhance the accuracy of the CoM trajectory estimation, or reduce the number of sensors to be used in the biomechanical model. Future studies should be focused on these research areas.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the high interest in the use of wearable sensors for the measurements of human CoM dynamics, in most literature cases, the CoM assessment with inertial sensors was computed during dynamic tasks such as walking, running, or skiing, in which the CoM dynamics assumed large fluctuations in the 3D space [ 6 , 34 , 35 , 37 , 38 , 41 , 42 ]. Only a few studies verified the accuracy of IMU-based methods for the measurement of CoM trajectories during standing tasks [ 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

Germanotta

Mileti

Conforti

et al. 2021

Sensors

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The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

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“…To improve the navigation ability of inertial/GPS integrated navigation system, LSTM is used to improve the system’s error prediction ability [ 17 ]. LSTM is used to estimate the com-cop inclination angle during walking based on inertial sensors [ 18 ]. LSTM-RNN is also proposed to denoise the MEMS IMU output signals [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

IMU Data and GPS Position Information Direct Fusion Based on LSTM

Guang

Gao

Liu

et al. 2021

Sensors

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In recent years, the application of deep learning to the inertial navigation field has brought new vitality to inertial navigation technology. In this study, we propose a method using long short-term memory (LSTM) to estimate position information based on inertial measurement unit (IMU) data and Global Positioning System (GPS) position information. Simulations and experiments show the practicability of the proposed method in both static and dynamic cases. In static cases, vehicle stop data are simulated or recorded. In dynamic cases, uniform rectilinear motion data are simulated or recorded. The value range of LSTM hyperparameters is explored through both static and dynamic simulations. The simulations and experiments results are compared with the strapdown inertial navigation system (SINS)/GPS integrated navigation system based on kalman filter (KF). In a simulation, the LSTM method’s computed position error Standard Deviation (STD) was 52.38% of what the SINS computed. The biggest simulation radial error estimated by the LSTM method was 0.57 m. In experiments, the LSTM method computed a position error STD of 23.08% using only SINSs. The biggest experimental radial error the LSTM method estimated was 1.31 m. The position estimated by the LSTM fusion method has no cumulative divergence error compared to SINS (computed). All in all, the trained LSTM is a dependable fusion method for combining IMU data and GPS position information to estimate position.

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Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking

Cited by 30 publications

References 43 publications

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

IMU Data and GPS Position Information Direct Fusion Based on LSTM

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