1. Introduction
Improving health and lives of people is undoubtedly one of the prime goals of healthcare
organizations, policy-makers, and leaders around the world. The need of ageing, disability,
long-term care, and palliative care in our current society pose formidable challenges
for disease burden and healthcare systems that must be addressed [1]. In order to
tackle the leading causes of morbidity and mortality that may result from infections
to chronic conditions especially in older adults and ageing population, the accessibility
and provision of long-term care and palliative care, when and where needed by them,
is crucial. With the continuous challenges and rising demands of the elderly, remote
and home-based care, the technological innovations in the fields of digital health
and health information and communication technologies, such as mobile health, wearable
technologies, telemedicine and personalized medicine have transformed the ways of
practice and delivery of healthcare in the recent decades [2]. Wearable technologies
have been extensively used in the healthcare sector with multi-purpose applications
ranging from patient care to personal health. In clinical and remote care, the applications
of wearable devices/sensors, mobile applications, and tracking technologies are of
immense importance for the diagnosis, prevention, monitoring, and management of chronic
diseases and conditions [3]. The data generated from the wearable devices/sensors
are a cornerstone for healthcare data analytics, especially when it is utilized by
latest technologies, such as Artificial Intelligence (AI), Machine Learning (ML),
Big Data Intelligence, and Internet of Things (IoT) Systems. The literature has many
successful examples of utilization of these data in various branches of medicine,
such as oncology, radiology, surgery, geriatrics, rheumatology, neurology, hematology,
and cardiology. With the regular ongoing updates, the outcomes of data analytics and
their applications are already making a huge impact in transforming and revolutionizing
the healthcare industry.
In this special issue, we aim to provide new insights on research data analytics and
applications of wearable devices/sensors in healthcare by covering wide range of related
topics. This issue represents the latest research that spans across 19 countries,
37 institutions and is covered by a total of 28 articles. To make better understanding
of the research articles, we have arranged them in an order to show various covered
aspects in this field, such as technology integration research, prediction systems,
rehabilitation studies, prototype systems, community health studies, detection systems,
ergonomics studies, technology acceptance studies, monitoring systems, warning systems,
sports studies, clinical systems, feasibility studies, parameters measurement systems,
design studies, location based systems, tracking systems, observational studies, risk
assessment studies, activity recognition systems, impact measurement systems and systematic
review.
2. Summary of Special Issue Papers
In order to provide a basic overview, we will go through and provide brief summary
of all the articles of wearable devices/sensors covered in this issue one by one.
Bayo-Monton et al. [4] provided an implementation of new portable system for remote
management of chronic diseases by presenting and evaluating an embedded and scalable
distributed system using wearable sensors for the connection of cheap health devices
based on prototyping eHealth platforms. The results of their analysis showed that
portable devices (p << 0.01) are suitable for supporting the transmission and analysis
of biometric signals into scalable telemedicine systems. In an observational study,
Thakur et al. [5] presented a supervised ML-based model for predicting the clinical
events during dialysis sessions using data from a non-contact sensor device. The authors
found the findings and performance of the ML model quite encouraging and suggested
the use of non-contact sensors in clinical settings for monitoring patients’ vital
parameters and in early warning solutions for predicting the clinical events. In a
study involving patients that recently had knee replacement surgery, Argent et al.
[6] explored and evaluated the feasibility, usability, impact and user experience
of an exemplar exercise biofeedback system for orthopedic rehabilitation at home.
In order to maximize the engagement and impact, the study incorporated user-centered
design approaches by incorporating participants’ evaluation during the design of the
system. The findings of the study support the ongoing development and evaluation of
sensor-based biofeedback systems, and authors found the system highly usable and effective
for patient support and engagement. In a community health study, Martinez et al. [7]
developed a new unsupervised exploratory method for characterizing feature extraction
and detecting movement similarity in sleep by using actigraphy signals. The results
of statistical analysis showed the potentials of this method for sleep disorders and
their link with other conditions. The authors suggested the possible application of
proposed approach for the extraction and comparison of sleep movements’ patterns in
the field of medicine. Based on a previous work of a Wearable Heat stroke Detection
Device (WHDD) [8] that was used for heat stroke prediction capability for any activity
or exercise. Lin et al. [9] investigated the detailed information analysis and performed
static and dynamic experiments for verifying the availability and effectiveness of
WHDD experimental subjects. The results of their work demonstrated the superior applicability
of the WHDD for predicting the occurrence of heat stroke effectively and ensuring
the safety of runners. Using recurrent neural networks (RNNs)-based deep learning
models, Luna-Perejon [10] presented a feasibility study of implementing a wearable
system for the detection of falls and its associated risks/hazards in real time through
accelerometer signals. Based on the results of the study, the authors recommended
RNNs models as an effective method for creation of autonomous wearable fall detection
systems in real time. Using a large real-world database of posture data, Stollenwerk
et al. [11] analyzed the postural changes that are induced under postural training
in three different positions, sitting, standing, and hip hinging, and compared the
snapshots of unguided-guided posture pair based on features resulted from 2D spine
curve geometry. The results showed the novelty of the work in the field of wearable-sensor-based
evaluation of spine curves. Vega-Barbas et al. [12] proposed a precise and pervasive
ergonomic platform for accurate assessment of continuous risk and personalized automated
coaching by utilizing in-house developed garments and a mobile application. The results
of the study demonstrating a good usability score proved the acceptable usability
of the platform. The authors expected that wearable technology in the field of ergonomics
can have cost effective risk assessment and economical solutions in the future. The
study from Lin et al. [13] presented the design of a wearable cardiac health monitoring
platform, implemented it as wearable smart clothing system with multi-channel mechanocardiograms
and electrocardiograms measurements, and evaluated the usability of the system using
technology acceptance model. The analysis and the results of the study showed the
positive attitude of subjects for using this wearable system in providing early risk
warnings. Based on deep learning, Lim et al. [14] presented a coaching assistant method
to provide useful information for table tennis practice, and used long short-term
memory (LSTM) recurrent neural networks (RNNs) with deep state space model and probabilistic
inference to support practice. The promising results provided by this method showed
its capability in characterizing high-dimensional time series patterns and providing
useful information with wearable sensors in table tennis coaching. Lu et al. [15]
developed and tested a new method that combined information from heart rate, respiration,
and accelerations measurements to estimate energy expenditure. These data measurements
were taken from wearable sensor system and were integrated by neural network based
model. The results of the proposed method showed improved accuracy over two existing
established methods. The authors suggested that this model along with wearable system
could be utilized in both occupational as well as general health applications. Ejupi
[16] investigated the feasibility of wearable textile-based sensors to accurately
monitor breathing patterns, develop algorithm to detect talking using ML algorithm,
and evaluate the model’s performance with the study participants. The evaluation showed
random forest classifier as the best performer in the dataset. The authors suggested
that this approach could be used to quantify talking through social interaction and
prevent social isolation and loneliness. Using a previously developed inertial measurement
unit device based on three sensor [17], Cesareo et al. [18] presented an automatic
and position-independent algorithm to derive the respiration-induced movement and
determine the respiratory rate accurately. The results showed that principal component
analysis (PCA) fusion method obtained overall highest performance in terms of breathing
frequency estimation, in both supine as well as seated position. The authors suggested
that PAC fusion, as dimension-reduction method, can be used to analyze further data
in the future. Using wearable technology and ML algorithms, Manjarres et al. [19]
developed a smart physical workload tracking system in real time for simultaneous
remote monitoring of people. The established framework was based on the concept of
ergonomics to facilitate the work of health professionals and fitness experts. The
results of two case studies in real time showed good accuracy and reliability of the
system. The authors recommended the future developments by combining ergonomics and
ML to predict the physical effort of activities and for injury prevention environments.
Nam et al. [20] used an inertial measurement unit-based motion capture and analysis
system to access arm movements. The study provided an important database on the dimensions
of workspace and range of motions for arm movements. The validation results showed
high accuracy and reliability of the system and emphasized on the importance of designing
new exoskeletons for neurorehabilitation purposes. Zhang et al. [21] examined the
relevance of different conventional physical activity metrics and complexity in the
assessment of functional change after exercise intervention in younger and older adults.
The findings of the study demonstrated the potential and usefulness of physical activity
complexity metrics as compared to conventional metrics in assessment of functional
changes for younger and older adults, and recommended them for the feasibility and
effectiveness of risk identification and interventions. Hsu et al. [22] proposed a
wearable 12-lead electrocardiogram monitoring system to measure the electrocardiogram
(ECG) signals of patients with myocardial ischemia and arrhythmia. The experimental
results of the study provided a good ECG signal quality even while walking and detected
ECG features of the mentioned patients. The authors suggested the possible usefulness
of the proposed system in future mobile ECG monitoring applications. Jayasinghe et
al. [23] investigated and quantified the data received from sensors in different types
of clothing in order to characterize the activities as compared to the body worn sensors’
data. The case study analysis indicated that clothing sensors data correlated well
with the body worn sensors data, and classification results from clothing sensors
were also promising compared to body-worn sensors. The results of the study showed
potentials of this approach in daily monitoring. Allahbakhshi et al. [24] examined
the role of Global Positioning System (GPS) sensors data for detection of physical
activity in semi-structured and real-life protocols using participants with wearable
devices in a study. The results provided insights in assisting physical activity for
future study designs and guidance related to detection of posture and transport related
motion activities. Cheung et al. [25] proposed a novel quantile coarsening analysis
(QCA) for reducing the dimension of data from wearable devices and demonstrated the
feasibility of this approach in a small cohort of relatively healthy individuals.
Because of the versatility of the QCA approach, the authors suggested that it can
provide useful analytical tools for data in multi-modal monitoring. By explaining
the role of actigraphs in personalized health, fitness monitoring and Internet of
Medical Things (IoMT) paradigm, Athavale [26] presented a study utilizing wearable
devices to capture and analyze physiological data at home-based health monitoring
in an IoMT environment, and proposed a low level encoding scheme to improve actigraphy
analysis. In order to ensure that there was no loss of information in encoding process,
ML approach was used for the study validation. Based on the dataset Personal RIsk
DEtection (PRIDE) [27], a study by Trejo [28] first explored the impact of using dimension
reduction techniques and frequency domain features for personal risk detection through
correlation matrix and principal component analysis, and then efficiently accelerated
the training and classification process of a given classifier for mobile devices.
The results of the study were encouraging for timely detection of risk prone situations
that can threaten a person’s physical integrity. Yurtman et al. [29] proposed a methodology
to transform the recorded motion sensor sequences to sensor unit orientation unchangeably
and incorporated it in pre-processing stage of the standard activity recognition scheme.
The results from comparative evaluation of proposed method with the existing state-of-the-art
classifiers showed its substantially better output in classifying stationary activities
and hence its possible application in various wearable systems. Dutta et al. [30]
used a novel framework to classify and model the physical activities performed by
different participants in a supervised lab-based protocol and then utilized it to
identify the physical activities in a free-living setting using the data from wrist
worn accelerometers. The positive results of the study demonstrated its application
for estimating physical activities in future cohort or intervention studies. In a
study, Rosati et al. [31] compared two different feature sets for real-time human
activity recognition (HAR) applications; one comprising time, frequency, and time-frequency
related parameters used in the literature and the other containing only time-related
variables linked with biomechanical meaning of acquired signals. The results showed
that both set of features can reach high accuracy with support vector machine (SVM)
classifier, but the new proposed variables can be easily interpreted and employed
for better understanding of the alterations of biomechanical behavior in complex situations.
In a study focusing on healthy subjects having normal heart activity, Morelli et al.
[32] investigated the effects of interpolation on time and duration with increasing
missing values to assess the interpolation strategy for better results during the
estimation of heart rate variability (HRV) features. The results concluded that interpolation
in time is the most favorable method for producing better HRV features estimation
as compared to interpolation on duration. Fortin-Cote et al. [33] presented a graphical
software for the visualization and preprocessing of raw data received from accelerometer
for human posture tracking and assessment. This tool was aimed to provide support
for calibration of orientation estimate of inertial measurement units (IMUs) that
are used for joint angle measurement. Two case studies were used to demonstrate the
usefulness of this open source software. Broadley et al. [34] presented a systematic
review to assess existing methods of evaluating fall detection systems, identify their
limitations, and propose improved evaluation methods in the literature. The search
results of articles that met the inclusion criteria identified few issues, such as
use of small population datasets and inconsistency for performance quantification
for these systems. Sensitivity, precision, and F-measures were derived as the most
appropriate and robust measures for their realistic performance evaluation.