Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors

Matar, Georges; Lina, Jean‐Marc; Kaddoum, Georges

doi:10.1109/jbhi.2019.2899070

Cited by 95 publications

(68 citation statements)

References 27 publications

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“…Recently, we addressed this important problem by presenting a compensation detection system using machine learning algorithms based on pressure distribution data [19]. Pressure distribution-based detection systems do not induce unnatural movements or the discomfort of being monitored [20], [21]. In our previous study, 15 healthy participants simulated common compensatory movements and several features were extracted from the pressure distribution data.…”

Section: A Compensation Detectionmentioning

confidence: 99%

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy

Cai

et al. 2020

IEEE J. Biomed. Health Inform.

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Objectives: Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal recovery outcomes. This study investigated the feasibility of the realtime monitoring of compensation in patients with stroke by using pressure distribution data and machine learning algorithms. Whether trunk compensation can be reduced by combining the online detection of compensation and haptic feedback of a rehabilitation robot was also investigated. Methods: Six patients with stroke did three forms of reaching movements while pressure distribution data were recorded as Dataset1. A support vector machine (SVM) classifier was trained with features extracted from Dataset1. Then, two other patients with stroke performed reaching tasks, and the SVM classifier trained by Dataset1 was employed to classify the compensatory patterns online. Based on the real-time monitoring of compensation, a rehabilitation robot provided an assistive force to patients with stroke to reduce compensations. Results: Good classification performance (F1 score > 0.95) was obtained in both offline and online compensation analysis using the SVM classifier and pressure distribution data of patients with stroke. Based on the real-time detection of compensatory patterns, the angles of trunk rotation, trunk lean-forward and trunk-scapula elevation decreased by 46.95%, 32.35% and 23.75%, respectively. Conclusion: High classification accuracies verified the feasibility of detecting compensation in patients with stroke based on pressure distribution data. Since the validity and reliability of the online detection of compensation has been verified, this classifier can Manuscript

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Section: A Compensation Detectionmentioning

confidence: 99%

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy

Cai

et al. 2020

IEEE J. Biomed. Health Inform.

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“…Davoodnia and Etemad et al [38] utilizes a convolutional neural networks (CNN) to classify sleeping posture, which consists of four main blocks is designed to convert the pressure map data manifold into a feature space. Matar et al [5] extracted HoG and LBP features of sleep postures and trained a feed-forward artificial neural network for classification. Olivia et al [34] adopted multiple classifiers for sleep stage recognition.…”

Section: B: Downstream Sleep Recognitionmentioning

confidence: 99%

“…Based on the multi-sensor sleep data, a large quantity of feature representation and classification methods have been applied in the field of sleep recognition. Histogram of oriented gradients (HoG) and local binary patterns (LBP) from data, restricted Boltzmann machines (RBM), convolutional neural network (CNN), k-nearest neighbour (KNN), support vector machines (SVM), have been involved to perform the in-bed posture classification [5]- [7]. Besides, there are also a number of studies that make efforts to identify sleep stages, such as hand-crafted feature classification, k-means and random forests [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and overfitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: ''https://github.com/zhaoaite/SSRM ''.

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“…There are several papers published with a similar topic to our proposed complex solution. Authors in [1] presented a complex system based on an artificial neural network for in-bed posture classification. Unlike our solution, they used the commercially available pressure sensor mattress.…”

Section: Introductionmentioning

confidence: 99%

“…HTTP request-response block sends the data for evaluation in JSON format as a parameter in the HTTP request to the Jetson module. Data looks like: payload: { timestamp: 1605683032, subjectID: "subjet12", notes: "", data: [ [7,8,12,12,20,123,0,0], [8,10,27,21,28,26,0,0], [2,9,6,8,12,8,0, 0], [6,11,11,16,22,13,3,0], [19,32,39,95,103,66,8,0], [13,16,16,28,51,24,1,0], [18,20,24,47,82,40,6, 0], [0, 0, 4, 10, 12, 6, 0, 0], ...…”

mentioning

confidence: 99%

A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor

Hudec

Matúška

Kamencay

et al. 2020

Sensors

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Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.

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Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors

Cited by 95 publications

References 27 publications

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor

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