Methodology and validation for identifying gait type using machine learning on IMU data

Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R2 values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor

Chow

Tremblay

Yeung

et al. 2021

“…Mahoney et al [20] used ANN models to classify several locomotion modes, such as walking, jogging, and running. They trained the model with accelerometer data, gyroscope data, and a combination of both.…”

Section: Introductionmentioning

confidence: 99%

Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons

Liu

Gutierrez-Farewik

2021

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors—specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and −5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side’s mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.

“…They also gave instant tactile, visual and auditory feedback, which were found to be beneficial for the movement of a wide type of population such as healthy adults [43], amputees [44], older adults [45], runners [46] and stroke survivors [47]. Wearable sensors were validated against gait laboratory equipment on kinematic and kinetic measurements [48][49][50][51][52][53][54] in sports-related movements [55]. Accuracies of wearable sensors varied, as there were always challenges in the accurate position of sensors [56], data processing and simplification methods in calculating joint angles and segment acceleration [57] and protocols to calibrate the devices [58].…”

Section: Introductionmentioning

confidence: 99%

Effects of Wearable Devices with Biofeedback on Biomechanical Performance of Running—A Systematic Review

Giraldo-Pedroza

Lee

Lam

et al. 2020

This present review includes a systematic search for peer-reviewed articles published between March 2009 and March 2020 that evaluated the effects of wearable devices with biofeedback on the biomechanics of running. The included articles did not focus on physiological and metabolic metrics. Articles with patients, animals, orthoses, exoskeletons and virtual reality were not included. Following the PRISMA guidelines, 417 articles were first identified, and nineteen were selected following the removal of duplicates and articles which did not meet the inclusion criteria. Most reviewed articles reported a significant reduction in positive peak acceleration, which was found to be related to tibial stress fractures in running. Some previous studies provided biofeedback aiming to increase stride frequencies. They produced some positive effects on running, as they reduced vertical load in knee and ankle joints and vertical displacement of the body and increased knee flexion. Some other parameters, including contact ground time and speed, were fed back by wearable devices for running. Such devices reduced running time and increased swing phase time. This article reviews challenges in this area and suggests future studies can evaluate the long-term effects in running biomechanics produced by wearable devices with biofeedback.