bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown
Опубликована: Окт. 29, 2023
Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data serve as labels for supervised model training. We thus propose using existing self-supervised (SSL) leverage IMU datasets pre-train models, which can improve the accuracy and efficiency IMU-based GRF estimation.
Язык: Английский
Frontiers in Surgery, Год журнала: 2024, Номер 10
Опубликована: Янв. 11, 2024
Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), sensors are shaping field orthopaedic surgery on all levels, from patient care to research facilitation logistic processes. Especially COVID-19 pandemic, with associated contact restrictions was an accelerator for development introduction telemedical applications alternatives classical in-person care. Digital already used include support, online video consultations, monitoring patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, forms medical image processing, three-dimensional (3D)-modelling, simulations. In addition that immersive technologies virtual, augmented, mixed reality increasingly training but also rehabilitative settings. advances can therefore increase accessibility, efficiency capabilities services facilitate more data-driven, personalized care, strengthening self-responsibility supporting interdisciplinary healthcare providers offer optimal their patients.
Язык: Английский
Процитировано
8
Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 275 - 290
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1
Frontiers in Bioengineering and Biotechnology, Год журнала: 2024, Номер 12
Опубликована: Апрель 2, 2024
Portable
measurement
systems
using
inertial
sensors
enable
motion
capture
outside
the
lab,
facilitating
longitudinal
and
large-scale
studies
in
natural
environments.
However,
estimating
3D
kinematics
kinetics
from
data
for
a
comprehensive
biomechanical
movement
analysis
is
still
challenging.
Machine
learning
models
or
stepwise
approaches
performing
Kalman
filtering,
inverse
kinematics,
dynamics
can
lead
to
inconsistencies
between
kinetics.
We
investigated
reconstruction
of
arbitrary
running
motions
sensor
optimal
control
simulations
full-body
musculoskeletal
models.
To
evaluate
feasibility
proposed
method,
we
used
marker
tracking
created
optical
as
reference
computing
virtual
such
that
desired
solution
was
known
exactly.
generated
by
formulating
problems
tracked
acceleration
angular
velocity
while
minimizing
effort
without
requiring
task
constraint
an
initial
state.
approach,
reconstructed
three
trials
each
straight
running,
curved
v-cut
10
participants.
compared
estimated
signals
variables
simulations.
The
closely,
resulting
low
mean
root
squared
deviations
pelvis
translation
(≤20.2
mm),
angles
(≤1.8
deg),
ground
reaction
forces
(≤1.1
BW%),
joint
moments
(≤0.1
BWBH%),
muscle
(≤5.4
BW%)
high
coefficients
multiple
correlation
all
Язык: Английский
Процитировано
6
Salud Ciencia y Tecnología, Год журнала: 2024, Номер 4, С. 951 - 951
Опубликована: Янв. 1, 2024
Athletes' inability to return and pursue their athletics is primarily motivated by fear of re-injury. Sports injuries have been recognized as a significant deterrent further physical exercise. This study aims evaluate evidence-based strategies interventions for preventing sports-related injuries, including pre-participation screenings, suitable training programs, equipment modifications, injury prevention programs. A systematic review meta-analysis (PRISMA) approach was used gather, choose, analyze publications on sports injuries. Scopus, Web Science (WoS), ProQuest, Springer Link were databases the study. The inclusion exclusion criteria apply study.Adequate treatment aids in recovery injured parts body future Athletes, coaches, medicine specialists can collaborate reduce frequency severity encouraging safer longer-lasting activity participation. Policies that likelihood players sustain be achieved implementing these into competition protocols
Язык: Английский
Процитировано
5
Arthroscopy The Journal of Arthroscopic and Related Surgery, Год журнала: 2024, Номер 40(7), С. 2135 - 2151.e2
Опубликована: Янв. 11, 2024
Язык: Английский
Процитировано
4
IFMBE proceedings, Год журнала: 2025, Номер unknown, С. 153 - 168
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Hans Journal of Biomedicine, Год журнала: 2025, Номер 15(01), С. 244 - 257
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Sensors & Diagnostics, Год журнала: 2023, Номер 2(6), С. 1414 - 1436
Опубликована: Янв. 1, 2023
This paper discusses the development of wearable flexible textile-based strain sensors for monitoring multiple human motions.
Язык: Английский
Процитировано
9
IEEE Transactions on Biomedical Engineering, Год журнала: 2024, Номер 71(7), С. 2095 - 2104
Опубликована: Фев. 5, 2024
Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data serve as labels for supervised model training. We thus propose using existing self-supervised (SSL) leverage IMU datasets pre-train models, which can improve the accuracy and efficiency IMU-based GRF estimation.
Язык: Английский
Процитировано
3
Chinese Journal of Chemical Engineering, Год журнала: 2024, Номер 72, С. 1 - 9
Опубликована: Апрель 9, 2024
Язык: Английский
Процитировано
1