Deep Neural Network-Based Gait Classification Using Wearable Inertial Sensor Data

Jung, Dawoon; Nguyen, Mau Dung; Han, Jooin; Park, Mina; Lee, Kwanhoon; Yoo, Seonggeun; Kim, Jin Wook; Mun, Kyung-Ryoul

doi:10.1109/embc.2019.8857872

Cited by 16 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, in Di Lazzaro et al, 35 SVM reached 97% accuracy with features extracted from Pull Test, TUG, tremor and bradykinesia items. Four studies examined participants on different speeds 37 , 41 , 44 , 47 while one study examined participants on maximum speed. 46 Other studies either did not specify gait speed 35 or allow participants to conduct the task at their preferred speed.…”

Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

View full text Add to dashboard Cite

Background The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. Methods Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. Results A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. Conclusions Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

show abstract

Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This plantar pressure dataset contains 48 subjects and 7462 samples in total, including 23 high risk subjects and 25 low risk subjects. As shown in Table I, the dataset is divided into four different MSST setting in order to traverse the whole dataset and produce comprehensive experimental results, as 4-fold cross validation did [79,80]. Noting there is no information leakage between divisions when adopting leave-one-division-out.…”

Section: A Experimental Setupmentioning

confidence: 99%

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment

Wu,

Shu,

Song

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

In cross-subject fall risk classification based on plantar pressure, a challenge is that data from different subjects have significant individual information. Thus, the models with insufficient generalization ability can't perform well on new subjects, which limits their application in daily life. To solve this problem, domain adaptation methods are applied to reduce the gap between source and target domain. However, these methods focus on the distribution of the source and the target domain, but ignore the potential correlation among multiple source subjects, which deteriorates domain adaptation performance. In this paper, we proposed a novel method named domain adaptation with subject fusion (SFDA) for fall risk assessment, greatly improving the cross-subject assessment ability. Specifically, SFDA synchronously carries out source target adaptation and multiple source subject fusion by domain adversarial module to reduce source-target gap and distribution distance within source subjects of same class. Consequently, target samples can learn more task-specific features from source subjects to improve the generalization ability. Experiment results show that SFDA achieved mean accuracy of 79.17 % and 73.66 % based on two backbones in a cross-subject classification manner, outperforming the state-of-the-art methods on continuous plantar pressure dataset. This study proves the effectiveness of SFDA and provides a novel tool for implementing cross-subject and few-gait fall risk assessment.

show abstract

“…In another study, Jung et al proposed an effective approach for gait recognition of three different groups using 2D-CNN based wearable IMU sensors, trained with gait spectrograms obtained from time-frequency domain analysis of raw acceleration and angular velocity data. This classifier showed an accuracy of 93.02% in classifying athlete, normal foot and deformed foot groups [24]. The main contribution on human gait recognition of this study is the development of a 2D-CNN model inspired by [23] for the use of IMU signal TFA images.…”

Section: Introductionmentioning

confidence: 96%

“…CNN deep learning algorithms are frequently employed in human gait recognition. Raw signals and time-frequency spectrum images of IMU sensors are widely used as CNN inputs [21][22][23][24]. The methodology of Jung et al study is based on the study that provides reliable multi-classification using deep convolutional neural networks and spectrographic approach using IMU data to classify pathological gait phases without discernible differences [23].…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for human gait recognition from time-frequency analysis images of inertial measurement unit signal

Kuduz,

Kaçar

2023

ijamec

View full text Add to dashboard Cite

Biomechanical analysis using deep learning has been increasingly used in recent studies to identify human activity. Wearable sensor data from inertial measurement units (IMUs) is widely used for recognizing human activity, but has several drawbacks owing to its high volume and diversity. To overcome these issues, the time-domain and power spectral characteristics of IMU data can be extracted using digital signal processing (DSP) methods. Our research aimed to investigate time-frequency analysis (TFA) methods for classifying the spatio-temporal gait characteristics of physical walking performed by healthy subjects. In this study, open-source biomechanical sensor signal dataset was used. The DSP step was first carried out by segmenting IMU data from the four body segments of 22 healthy subjects, and then by applying Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT) methods. Moreover, the resultants of linear accelerometer signals were applied in a similar manner. The image datasets obtained from this step were applied to a deep convolutional neural network (CNN) model to classify human walking speed (WS) into three classes: fast, normal, and slow. The performance of the 2D-CNN model and the impact of DSP methods using IMU data were evaluated. In conclusion, the highest test classification results demonstrated that STFT-all (85.9%), CWT-all (79.3%), and CWT-resAcc (76.3%) based CNN models present a remarkably precise and easy-to-implement classification problem, with the highest test accuracy, when all IMU channels are subjected to STFT. The classification accuracies of 2D-CNN models were compared to commonly used ML models. The Deep CNN model is a useful gait evaluation tool for healthy subjects. Furthermore, it can enable the diagnosis and phase assessment of gait abnormalities and detect gait biomarkers in rehabilitative wearables.

show abstract

Deep Neural Network-Based Gait Classification Using Wearable Inertial Sensor Data

Cited by 16 publications

References 11 publications

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment

A deep learning approach for human gait recognition from time-frequency analysis images of inertial measurement unit signal

Contact Info

Product

Resources

About