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Novel Approaches for Advancing Wearable Sensing Technologies

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 22333

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Dr. Emmanouil Rousakis

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Guest Editor

Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
Interests: synthetic chemistry; porphyrins; optical sensors; wearable sensors; oxygen sensing; translational research

Special Issue Information

Dear Colleagues,

Wearable sensing devices for health monitoring are becoming increasingly widespread in the general population, in addition to their expanding use in the hospital settings they were once limited to. This necessitates innovations aimed at making them smaller and more ergonomic, more sensitive and accurate, easy to use, and configured to provide readouts that are straightforward to interpret.

These advancements in wearable sensing technologies require bringing together expertise from cross-disciplinary areas, from sensor molecule synthesis and materials science, to engineering and data processing. This is supported by rapidly expanding technologies such as 3D printing, and data processing with the addition of machine learning capabilities.

For this Special Issue, we would like to invite the submission of manuscripts focusing on new, application-oriented research for improving device performance, design, and data reporting, overall leading to innovations in the development of new wearable sensing technologies for health and physiological performance monitoring. Studies describing novel approaches in any of the areas of synthesis and material development, electronics and engineering, device design, or data analysis, with a focus on improving health and performance monitoring using wearable devices, are welcome.

Dr. Emmanouil Rousakis
Guest Editor

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Keywords

wearable sensor design
sensing technologies
health monitoring devices
novel sensing materials
engineering of wearable electronics
3D printed sensor devices
sensor applications in healthcare
smart wearables

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Published Papers (4 papers)

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Research

17 pages, 2045 KiB

Open AccessArticle

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

by Paul D. Rosero-Montalvo, Edison A. Fuentes-Hernández, Manuel E. Morocho-Cayamcela, Luz M. Sierra-Martínez and Diego H. Peluffo-Ordóñez

Sensors 2021, 21(13), 4422; https://doi.org/10.3390/s21134422 - 28 Jun 2021

Cited by 9 | Viewed by 3424

Abstract

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children. Full article

(This article belongs to the Special Issue Novel Approaches for Advancing Wearable Sensing Technologies)

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14 pages, 2090 KiB

Open AccessArticle

Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson’s Disease

by Marta Francisca Corrà, Arash Atrsaei, Ana Sardoreira, Clint Hansen, Kamiar Aminian, Manuel Correia, Nuno Vila-Chã, Walter Maetzler and Luís Maia

Sensors 2021, 21(12), 3974; https://doi.org/10.3390/s21123974 - 9 Jun 2021

Cited by 15 | Viewed by 3973

Abstract

Accurate assessment of Parkinson’s disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD. Full article

(This article belongs to the Special Issue Novel Approaches for Advancing Wearable Sensing Technologies)

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21 pages, 8585 KiB

Open AccessArticle

Low-Latency Haptic Open Glove for Immersive Virtual Reality Interaction

by Donghyun Sim, Yoonchul Baek, Minjeong Cho, Sunghoon Park, A. S. M. Sharifuzzaman Sagar and Hyung Seok Kim

Sensors 2021, 21(11), 3682; https://doi.org/10.3390/s21113682 - 25 May 2021

Cited by 25 | Viewed by 8403

Abstract

Recent advancements in telecommunications and the tactile Internet have paved the way for studying human senses through haptic technology. Haptic technology enables tactile sensations and control using virtual reality (VR) over a network. Researchers are developing various haptic devices to allow for real-time tactile sensation, which can be used in various industries, telesurgery, and other mission-critical operations. One of the main criteria of such devices is extremely low latency, as low as 1 ms. Although researchers are attempting to develop haptic devices with low latency, there remains a need to improve latency and robustness to hand sizes. In this paper, a low-latency haptic open glove (LLHOG) based on a rotary position sensor and min-max scaling (MMS) filter is proposed to realize immersive VR interaction. The proposed device detects finger flexion/extension and adduction/abduction motions using two position sensors located in the metacarpophalangeal (MCP) joint. The sensor data are processed using an MMS filter to enable low latency and ensure high accuracy. Moreover, the MMS filter is used to process object handling control data to enable hand motion-tracking. Its performance is evaluated in terms of accuracy, latency, and robustness to finger length variations. We achieved a very low processing delay of 145.37 μs per finger and overall hand motion-tracking latency of 4 ms. Moreover, we tested the proposed glove with 10 subjects and achieved an average mean absolute error (MAE) of

{3.091}^{\circ}

for flexion/extension, and

{2.068}^{\circ}

for adduction/abduction. The proposed method is therefore superior to the existing methods in terms of the above factors for immersive VR interaction. Full article

(This article belongs to the Special Issue Novel Approaches for Advancing Wearable Sensing Technologies)

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13 pages, 3037 KiB

Open AccessArticle

Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning

by Kristin McClure, Brett Erdreich, Jason H. T. Bates, Ryan S. McGinnis, Axel Masquelin and Safwan Wshah

Sensors 2020, 20(22), 6481; https://doi.org/10.3390/s20226481 - 13 Nov 2020

Cited by 34 | Viewed by 5832

Abstract

Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit. Full article

(This article belongs to the Special Issue Novel Approaches for Advancing Wearable Sensing Technologies)

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