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Special Issue "Body Worn Sensors and Related Applications"

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

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Prof. Dr. Bijan Najafi
Website1 Website2
Guest Editor
Baylor College of Medicine, Houston, Texas, United States
Interests: wearables; digital health; frailty; exergame; diabetic foot
Special Issues and Collections in MDPI journals
Dr. Anisoara Paraschiv-Ionescu
Website
Guest Editor
École Polytechnique Fédérale de Lausanne
Interests: wearables; pain management; emotion-sensing; daily-life context, data analytics; behavior patterns
Dr. Hossein Rouhani
Website
Guest Editor
University of Alberta, Edmonton, AB, Canada
Interests: biomechanics; biomedical instrumentation; rehabilitative technologies; wearables; fall prevention

Special Issue Information

Dear Colleagues,

Various aspects of the economic burden of many healthcare systems in both developed and developing countries are associated with an aging population, in which 70% will need some kind of long-term care. This, of course, is exacerbated by the fact that our population continues to age and live longer. For instance, it is estimated that in the USA alone, more than 10,000 Americans reach Medicare age every day, some of whom will develop multiple chronic conditions and account for a large share of Medicare spending. Management of chronic diseases requires patient behavior change, and thus, a greater emphasis must be placed on the patient’s central and active role. This constitutes an important shift in current clinical practice. At present, systems relegate the patient to the role of a passive recipient of care, thereby missing the opportunity to leverage what they can do to promote personal health. Healthcare for chronic conditions must be reoriented around the patient and caregivers. In addition, more emphasis should be allocated to self-managed prevention at individual level.

The widespread uptake and acceptance of technology represents an opportunity to address this rising challenge. In particular, thanks to advances in wearables and digital health technologies, this is an opportunity to empower patients and/or their caregivers to be engaged as a part of the healthcare ecosystem. However, there are still fundamental gaps in adopting such technologies for the management of chronic illness and preventive care. For instance, while advanced signal processing, artificial intelligence, and remote monitoring have transformed the landscape of digital health industries, it is still unclear what clinically meaningful information could be extracted from these technologies to enable healthcare professionals to provide personalized care, empower patients to take care of their own health, and assist caregivers in effectively coordinating care.

This Special Issue is focused on applications of wearables, digital health, and data analytics to facilitate management of chronic conditions or preventive care. Some examples of these applications could be new studies that utilize sensors to extract digital biomarkers associated with cognitive decline, motor capacity deterioration because of specific conditions (e.g., dementia, Parkinson’s disease, stroke, cancer, diabetes), technologies to improvement care management for those suffering from chronic condition (e.g., chronic pain), sensors to improve patient adherence, sensors to better monitor sleep and stress, sensors for remote monitoring health and wellbeing, and sensors to manage environmental conditions that may impact health and wellbeing, such as humidity, temperature, light, CO2, etc. Of interest are also advanced data analytics developed to extract meaningful information from raw sensor data, to fuse multimodal data, to quantify interaction between physiological system (e.g., cardiorespiratory and motor system, emotion/cognition and motor system), and to quantify and visualize the pattern of individuals’ behavior in the context of everyday life.

Prof. Bijan Najafi
Dr. Anisoara Paraschiv-Ionescu
Dr. Hossein Rouhani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearables
  • digital health
  • digital twin
  • Internet of Thing
  • mHealth
  • remote patient monitoring
  • chronic illness
  • precision environment
  • personalized medicine
  • care coordination
  • outcome research
  • fall prevention
  • frailty
  • diabetes
  • cancer
  • aging in place
  • wellness
  • well built
  • exergame
  • dementia
  • cognitive impairment
  • diabetic foot
  • wound healing
  • patient care
  • pain management
  • emotion-sensing
  • daily-life context
  • data analytics
  • behavior patterns
  • human motion biomechanics
  • rehabilitative technologies

Published Papers (10 papers)

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Research

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Open AccessArticle
Pervasive Lying Posture Tracking
Sensors 2020, 20(20), 5953; https://doi.org/10.3390/s20205953 - 21 Oct 2020
Abstract
Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding [...] Read more.
Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Indirect Recognition of Predefined Human Activities
Sensors 2020, 20(17), 4829; https://doi.org/10.3390/s20174829 - 26 Aug 2020
Abstract
The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2 [...] Read more.
The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics
Sensors 2020, 20(17), 4741; https://doi.org/10.3390/s20174741 - 22 Aug 2020
Abstract
Our aim in this study was to investigate whether the behaviors of dairy cows on pasture, predicted with accelerometer data and combined with GPS data, can be used to better understand the relationship between behaviors and pasture characteristics. During spring 2018, 26 Holstein [...] Read more.
Our aim in this study was to investigate whether the behaviors of dairy cows on pasture, predicted with accelerometer data and combined with GPS data, can be used to better understand the relationship between behaviors and pasture characteristics. During spring 2018, 26 Holstein cows were equipped with a 3D-accelerometer and a GPS sensor fixed on a neck-collar for five days. The cows grazed alternatively in permanent and in temporary grasslands. The structural elements, soil moisture, slope and botanical characteristics were identified. Behaviors were predicted every 10 s from the accelerometer data and combined with the GPS data. The time-budgets expressed in each characterized zone of 8 m × 8 m were calculated. The relation between the time-budgets and pasture characteristics was explored with a linear mixed model. In the permanent grassland, dairy cows spent more time under a tree to ruminate (p < 0.001) and to rest (p < 0.001) and more time to graze in areas with Holcus lanatus (p < 0.001). In the temporary grassland, behavior was influenced by the external environment (presence of other animals on the farm; p < 0.05). Thus, this methodology seems relevant to better understand the relationship between the behaviors of dairy cows and grazing conditions to develop precision grazing. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Quantification of Triple Single-Leg Hop Test Temporospatial Parameters: A Validated Method Using Body-Worn Sensors for Functional Evaluation after Knee Injury
Sensors 2020, 20(12), 3464; https://doi.org/10.3390/s20123464 - 19 Jun 2020
Abstract
Lower extremity kinematic alterations associated with sport-related knee injuries may contribute to an unsuccessful return to sport or early-onset post-traumatic osteoarthritis. Also, without access to sophisticated motion-capture systems, temporospatial monitoring of horizontal hop tests during clinical assessments is limited. By applying an alternative [...] Read more.
Lower extremity kinematic alterations associated with sport-related knee injuries may contribute to an unsuccessful return to sport or early-onset post-traumatic osteoarthritis. Also, without access to sophisticated motion-capture systems, temporospatial monitoring of horizontal hop tests during clinical assessments is limited. By applying an alternative measurement system of two inertial measurement units (IMUs) per limb, we obtained and validated flying/landing times and hop distances of triple single-leg hop (TSLH) test against motion-capture cameras, assessed these temporospatial parameters amongst injured and uninjured groups, and investigated their association with the Knee Injury and Osteoarthritis Outcome Score (KOOS). Using kinematic features of IMU recordings, strap-down integration, and velocity correction techniques, temporospatial parameters were validated for 10 able-bodied participants and compared between 22 youth with sport-related knee injuries and 10 uninjured youth. With median (interquartile range) errors less than 10(16) ms for flying/landing times, and less than 4.4(5.6)% and 2.4(3.0)% of reference values for individual hops and total TSLH progression, differences between hopping biomechanics of study groups were highlighted. For injured participants, second flying time and all hop distances demonstrated moderate to strong correlations with KOOS Symptom and Function in Daily Living scores. Detailed temporospatial monitoring of hop tests is feasible using the proposed IMUs system. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network
Sensors 2020, 20(11), 3278; https://doi.org/10.3390/s20113278 - 09 Jun 2020
Abstract
Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In [...] Read more.
Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In this study, we designed the hardware for a three-lead patch-type ECG device and employed a long short-term memory (LSTM) network that can overcome the limitations of the linear regression algorithm used for ECG reconstruction. The LSTM network can overcome the issue of reduced horizontal components of the vector in the electric signal obtained from the patch-type device attached to the anterior chest. The reconstructed 12-lead ECG that uses the LSTM network was tested against a standard 12-lead ECG in 30 healthy subjects and ECGs of 30 patients with pathologic findings. The average correlation coefficient of the LSTM network was found to be 0.95. The ability of the reconstructed ECG to detect pathologic abnormalities was identical to that of the standard ECG. In conclusion, the reconstruction of a standard 12-lead ECG using a three-lead patch-type device is feasible, and such an ECG is an equivalent alternative to a standard 12-lead ECG. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Toward Using Wearables to Remotely Monitor Cognitive Frailty in Community-Living Older Adults: An Observational Study
Sensors 2020, 20(8), 2218; https://doi.org/10.3390/s20082218 - 14 Apr 2020
Cited by 1
Abstract
Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated [...] Read more.
Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog- individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog- (p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Application of Wearables to Facilitate Virtually Supervised Intradialytic Exercise for Reducing Depression Symptoms
Sensors 2020, 20(6), 1571; https://doi.org/10.3390/s20061571 - 12 Mar 2020
Abstract
Regular exercise can reduce depression. However, the uptake of exercise is limited in patients with end-stage renal disease undergoing hemodialysis. To address the gap, we designed a gamified non-weight-bearing intradialytic exercise program (exergame). The intradialytic exergame is virtually supervised based on its interactive [...] Read more.
Regular exercise can reduce depression. However, the uptake of exercise is limited in patients with end-stage renal disease undergoing hemodialysis. To address the gap, we designed a gamified non-weight-bearing intradialytic exercise program (exergame). The intradialytic exergame is virtually supervised based on its interactive feedback via wearable sensors attached on lower extremities. We examined the effectiveness of this program to reduce depression symptoms compared to nurse-supervised intradialytic exercise in 73 hemodialysis patients (age = 64.5 ± 8.7years, BMI = 31.6 ± 7.6kg/m2). Participants were randomized into an exergame group (EG) or a supervised exercise group (SG). Both groups received similar exercise tasks for 4 weeks, with three 30 min sessions per week, during hemodialysis treatment. Depression symptoms were assessed at baseline and the fourth week using the Center for Epidemiologic Studies Depression Scale. Both groups showed a significant reduction in depression score (37%, p < 0.001, Cohen’s effect size d = 0.69 in EG vs. 41%, p < 0.001, d = 0.65 in SG) with no between-group difference for the observed effect (p > 0.050). The EG expressed a positive intradialytic exercise experience including fun, safety, and helpfulness of sensor feedback. Together, results suggested that the virtually supervised low-intensity intradialytic exergame is feasible during routine hemodialysis treatment. It also appears to be as effective as nurse-supervised intradialytic exercise to reduce depression symptoms, while reducing the burden of administrating exercise on dialysis clinics. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Does the Presence of Cognitive Impairment Exacerbate the Risk of Falls in People with Peripheral Neuropathy? An Application of Body-Worn Inertial Sensors to Measure Gait Variability
Sensors 2020, 20(5), 1328; https://doi.org/10.3390/s20051328 - 29 Feb 2020
Cited by 1
Abstract
People with peripheral neuropathy (PN) are at risk of falling. Many people with PN have comorbid cognitive impairment, an independent risk factor of falls, which may further increase the risk of falling in people with PN. However, the negative synergic effect of those [...] Read more.
People with peripheral neuropathy (PN) are at risk of falling. Many people with PN have comorbid cognitive impairment, an independent risk factor of falls, which may further increase the risk of falling in people with PN. However, the negative synergic effect of those factors is yet to be reported. We investigated whether the presence of cognitive impairment exacerbates the risk of falls in people with PN by measuring gait variability during single-task walking and dual-task walking. Forty-four adults with PN were recruited. Based on the Montreal Cognitive Assessment (MoCA) scores, 19 and 25 subjects were cognitively impaired and intact, respectively. We measured coefficients of variation of gait speed, stride length, and stride time using validated body-worn sensors. During single-task walking, no between-group differences were observed (all p > 0.05). During dual-task walking, between-group differences were significant for gait variability for gait speed and stride length (51.4% and 71.1%, respectively; p = 0.014 and 0.011, respectively). MoCA scores were significantly correlated with gait variability for gait speed (r = 0.319, p = 0.035) and stride length (r = 0.367, p = 0.014) during dual-task walking. Our findings suggest that the presence of cognitive impairment exacerbates the risk of falls in people with PN. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Review

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Open AccessReview
Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation
Sensors 2020, 20(18), 5046; https://doi.org/10.3390/s20185046 - 05 Sep 2020
Abstract
Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, [...] Read more.
Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, it is not commonly understood. Widely used clinical scores are neither accurate nor reliable for spasticity assessment and follow up of treatments. Advancement of wearable sensors, signal processing and robotic platforms have enabled new developments and modeling approaches to better quantify spasticity. In this paper, we review quantitative modeling techniques that have been used for evaluating spasticity. These models generate objective measures to assess spasticity and use different approaches, such as purely mechanical modeling, musculoskeletal and neurological modeling, and threshold control-based modeling. We compare their advantages and limitations and discuss the recommendations for future studies. Finally, we discuss the focus on treatment and rehabilitation and the need for further investigation in those directions. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessReview
The Potential Role of Sensors, Wearables and Telehealth in the Remote Management of Diabetes-Related Foot Disease
Sensors 2020, 20(16), 4527; https://doi.org/10.3390/s20164527 - 13 Aug 2020
Cited by 1
Abstract
Diabetes-related foot disease (DFD), which includes foot ulcers, infection and gangrene, is a leading cause of the global disability burden. About half of people who develop DFD experience a recurrence within one year. Long-term medical management to reduce the risk of recurrence is [...] Read more.
Diabetes-related foot disease (DFD), which includes foot ulcers, infection and gangrene, is a leading cause of the global disability burden. About half of people who develop DFD experience a recurrence within one year. Long-term medical management to reduce the risk of recurrence is therefore important to reduce the global DFD burden. This review describes research assessing the value of sensors, wearables and telehealth in preventing DFD. Sensors and wearables have been developed to monitor foot temperature, plantar pressures, glucose, blood pressure and lipids. The monitoring of these risk factors along with telehealth consultations has promise as a method for remotely managing people who are at risk of DFD. This approach can potentially avoid or reduce the need for face-to-face consultations. Home foot temperature monitoring, continuous glucose monitoring and telehealth consultations are the approaches for which the most highly developed and user-friendly technology has been developed. A number of clinical studies in people at risk of DFD have demonstrated benefits when using one of these remote monitoring methods. Further development and evidence are needed for some of the other approaches, such as home plantar pressure and footwear adherence monitoring. As yet, no composite remote management program incorporating remote monitoring and the management of all the key risk factors for DFD has been developed and implemented. Further research assessing the feasibility and value of combining these remote monitoring approaches as a holistic way of preventing DFD is needed. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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