Body-Earth Mover’s Distance: A Matching-Based Approach for Sleep Posture Recognition

Xu, Xiaowei; Lin, Feng; Wang, Aosen; Hu, Yu; Huang, Ming-Chun; Xu, Wenyao

doi:10.1109/tbcas.2016.2543686

Cited by 45 publications

(18 citation statements)

References 27 publications

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“…The articles that were included in our analysis were published between 2007 and 2020 and were undertaken in the United States [25][26][27][28][29][30][31][32][33][34][35], China [36][37][38][39][40][41][42][43][44], Spain [45][46][47][48][49][50], Japan [51,52], Italy [53,54], Korea [55], and Greece [56]. According to the applied area of the included studies, we divided the articles into three components: predictive model (12 studies), posture recognition (11 studies), and image analysis (9 studies).…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Jiang¹,

Ma²,

Guo³

et al. 2021

JMIR Med Inform

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Background Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

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Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Jiang¹,

Ma²,

Guo³

et al. 2021

JMIR Med Inform

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“…Meanwhile, the classification performance of using DTW-distance-mapping features has been proved to be better than that using DTW as template matching. In addition, the time-consumption complexity of our proposed DTW is O(N 2 ), while that of EMD algorithm is cubic, specifically O(N 3 log n) [53].…”

Section: Discussionmentioning

confidence: 99%

Chinese Sign Language Recognition Based on DTW-Distance-Mapping Features

Cheng

Wei

Liu

et al. 2020

Mathematical Problems in Engineering

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Sign language is an important communication tool between the deaf and the external world. As the number of the Chinese deaf accounts for 15% of the world, it is highly urgent to develop a Chinese sign language recognition (CSLR) system. Recently, a novel phonology- and radical-coded CSL, taking advantages of a limited and constant number of coded gestures, has been preliminarily verified to be feasible for practical CSLR systems. The keynote of this version of CSL is that the same coded gesture performed in different orientations has different meanings. In this paper, we mainly propose a novel two-stage feature representation method to effectively characterize the CSL gestures. First, an orientation-sensitive feature is extracted regarding the distances between the palm center and the key points of the hand contour. Second, the extracted features are transformed by a dynamic time warping- (DTW-) based feature mapping approach for better representation. Experimental results demonstrate the effectiveness of the proposed feature extraction and mapping approaches. The averaged classification accuracy of all the 39 types of CSL gestures acquired from 11 subjects exceeds 93% for all the adopted classifiers, achieving significant improvement compared to the scheme without DTW-distance-mapping.

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“…21 In 2016, Xu et al identified sleep posture through pressure-sensitive sensor sheets, using the moving distance to use the pressure-sensitive image as a weighted 2D image, combined with earth mover's distance and Euclidean metric for similarity measurement, and experimentally demonstrated that the model improved accuracy by 8.01% compared with traditional sleep posture recognition methods. 22 In 2017, Vecchio et al, by giving the experimenter a device to calculate distance between the experimenter and the gyroscope is used to infer the postural changes of the human body, and the data are classified according to the ultra-wideband transceiver with two-way ranging mode combined with an accelerometer and gyroscope data, but this has certain requirements for the stature of the experimenter, and the accuracy is significantly lower in the case of extreme stature of the subject. 23 Lin et al designed a smart insole that recognizes the activities of the patient for the nursing field, and calculated the temporal and spatial distance based on the built-in pressure sensor, quantifying motion similarity and classifying human posture, and experimental results showed that the classification accuracy for eight common activities in nursing rooms was 91.7%.…”

Section: Sensor-based Gesture Recognitionmentioning

confidence: 99%

“…Sazonov's team used wearable sensors to build an SVM‐based framework for SVM and polynomial logic recognition, and used the labeled data for PAC/EE algorithm training, using fast artificial neural networks (FANNs) to train the MLDs model, the model has been experimentally demonstrated to be effective in reducing execution time for real‐time biofeedback systems 21 . In 2016, Xu et al identified sleep posture through pressure‐sensitive sensor sheets, using the moving distance to use the pressure‐sensitive image as a weighted 2D image, combined with earth mover's distance and Euclidean metric for similarity measurement, and experimentally demonstrated that the model improved accuracy by 8.01% compared with traditional sleep posture recognition methods 22 . In 2017, Vecchio et al, by giving the experimenter a device to calculate distance between the experimenter and the gyroscope is used to infer the postural changes of the human body, and the data are classified according to the ultra‐wideband transceiver with two‐way ranging mode combined with an accelerometer and gyroscope data, but this has certain requirements for the stature of the experimenter, and the accuracy is significantly lower in the case of extreme stature of the subject 23 .…”

Section: Related Workmentioning

confidence: 99%

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three‐hidden‐layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k‐nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large‐scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

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Body-Earth Mover’s Distance: A Matching-Based Approach for Sleep Posture Recognition

Cited by 45 publications

References 27 publications

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Chinese Sign Language Recognition Based on DTW-Distance-Mapping Features

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

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