Prediction of Plantar Forces During Gait Using Wearable Sensors and Deep Neural Networks

Nagashima, Mikihisa; Cho, Sung-Gwi; Ding, Ming; Ricardez, Gustavo Alfonso Garcia; Takamatsu, Jun; Ogasawara, Tsukasa

doi:10.1109/embc.2019.8857752

Cited by 8 publications

(14 citation statements)

References 6 publications

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“…Recent advances in deep learning have intriguingly shifted researchers from conventional machine learning approaches towards deep learning techniques. Most recently, gait analysis is conducted using wearable IMUs, magnetometer, 3D marker, H skeleton data, sEMG with convolutional neural network (CNN), long-short-term memory (LSTM), recurrent neural network (RNN) [25][26][27][28][29][30][31]. As a state-of-the-art technology, our focus is to investigate deep learning algorithms to analyze gait and estimate ankle joint power using wearable IMUs.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques in Estimating Ankle Joint Power Using Wearable IMUs

et al. 2021

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Estimating ankle joint power can be used to identify gait abnormities, which is usually achieved by employing a complicated biomechanical model using heavy equipment settings. This paper demonstrates deep learning approaches to estimate ankle joint power from two Inertial Measurement Unit (IMU) sensors attached at foot and shank. The purpose of this study was to investigate deep learning models in estimating ankle joint power in practical scenarios, in terms of variance in walking speeds, reduced number of extracted features and inter-subject model adaption. IMU data was collected from nine healthy participants during five walking trials at different speeds on a force-plate-instrumented treadmill while an optical motion tracker was used as ground truth. Three state-of-the-art deep neural architectures, namely Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and, fusion of CNN and LSTM (CNN-LSTM), were developed, trained, and evaluated in predicting ankle joint power by extracting few simple, meaningful features. The proposed architectures were found efficient and promising with higher estimation accuracies (correlation coefficient, R > 0.92 and adjusted R-squared value > 83%) and lower errors (mean squared error < 0.06, and mean absolute error < 0.13) in interparticipant evaluations. Performance evaluations among the three deep regressors showed that LSTM performed comparatively better. Lower standard deviations in mean squared error (0.029) and adjusted R-squared value (5.5%) proved the proposed model's efficiency for all participants.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques in Estimating Ankle Joint Power Using Wearable IMUs

et al. 2021

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“…Stetter et al evaluated their algorithm on healthy volunteers with bowlegs, which was suggested to mimic the varus misalignment in patients with knee osteoarthritis [33]. Female popu lation was underrepresented in 55 articles (~80%), while 6 articles (~8%) [30,32,[34][35][36][37] did not report complete information on the gender distribution of the study population.…”

Section: Participant Characteristicsmentioning

confidence: 99%

“…For validation of estimated kinetics, quantities like ground reaction forces were simultaneously measured in all studies using force plates [18,29,31,34,[40][41][42][43][44][45][46][47][48], instrumented treadmills [14,32,38,39,[49][50][51][52][53][54][55][56][57][58], plantar sensors [35], force shoes [59], shoes with loadcell(s) [60,61], or insoles [62][63][64][65]. Additional kinematic data were also measured in some of the studies using systems such as optical motion capture systems [30,33,36,37,, a stereophotogrammetry system [88], or a marker-less video system [89], along with IMUs.…”

Section: Measurement Systems and Sensor Placementmentioning

confidence: 99%

Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review

Krishnakumar,

van Beijnum,

Baten

et al. 2024

Sensors

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After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.

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“…One of the most popular ways to detect the gait phase is to have a pressure sensor on the bottom of the wearer's foot to analyze the ground reaction force (GRF) during walking [8][9][10]. Methods used to identify the gait phase include the center of pressure (COP).…”

Section: Introductionmentioning

confidence: 99%

“…Nagashima et al predicted plantar force during gait to detect walking distance with a deep neural network (DNN). The DNN is used to learn the non-linear relationship between the measured sensor data and future sensor force data by using a trained network [10]. However, when detecting the gait phase, problems are often found in the wearer's physical condition, such as the sensor position, stride speed, and weight [11].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm

Rattanasak

Uthansakul

et al. 2022

Sensors

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Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer.

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Prediction of Plantar Forces During Gait Using Wearable Sensors and Deep Neural Networks

Cited by 8 publications

References 6 publications

Deep Learning Techniques in Estimating Ankle Joint Power Using Wearable IMUs

Deep Learning Techniques in Estimating Ankle Joint Power Using Wearable IMUs

Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review

Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm

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