AIMC Topic: Wearable Electronic Devices

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Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.

IEEE journal of biomedical and health informatics
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, whic...

Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach.

Sensors (Basel, Switzerland)
Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns...

An Artificial Neural Network Framework for Gait-Based Biometrics.

IEEE journal of biomedical and health informatics
As the popularity of wearable and the implantable body sensor network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN device...

Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors.

Sensors (Basel, Switzerland)
This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventiona...

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors.

Sensors (Basel, Switzerland)
Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors c...

Complex-valued unsupervised convolutional neural networks for sleep stage classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be...

Ensemble Learning Approach via Kalman Filtering for a Passive Wearable Respiratory Monitor.

IEEE journal of biomedical and health informatics
OBJECTIVE: Utilizing passive radio frequency identification (RFID) tags embedded in knitted smart-garment devices, we wirelessly detect the respiratory state of a subject using an ensemble-based learning approach over an augmented Kalman-filtered tim...

Robot-assisted training using Hybrid Assistive Limb® for cerebral palsy.

Brain & development
PURPOSE: The Hybrid Assistive Limb® (HAL®, CYBERDYNE) is a wearable robot that provides assistance to a patient while they are walking, standing, and performing leg movements based on the wearer's intended movement. The effect of robot-assisted train...

Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0.

Sensors (Basel, Switzerland)
According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a m...

Cuff Pressure Algometry in Patients with Chronic Pain as Guidance for Circumferential Tissue Compression for Wearable Soft Exoskeletons: A Systematic Review.

Soft robotics
In this article, we report on a systematic review of the literature on pressure-pain thresholds induced and assessed by computerized cuff pressure algometry (CPA). The motivation for this review is to provide design guidance on pressure levels for we...