Published at : 24 Dec 2024
Volume : IJtech
Vol 15, No 6 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i6.7197
Adrian Nacarino | 1. Instituto de Investigación en Ciencias Biomédicas (INICIB), Ricardo Palma University, Av. Alfredo Benavides 5440, Santiago de Surco, 15039, Peru, 2. Professional School of Mechatronics Engineering, |
Anderson La-Rosa | Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440, Santiago de Surco, 15039, Peru |
Yelmo Quispe | Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440 Santiago de Surco, 15039, Peru |
Karl Castro | Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440 Santiago de Surco, 15039, Peru |
Freedy Sotelo Valer | Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440 Santiago de Surco, 15039, Peru |
Jose Cornejo | 1. Instituto de Investigación en Ciencias Biomédicas (INICIB), Ricardo Palma University, Av. Alfredo Benavides 5440, Santiago de Surco, 15039, Peru. 2. Research Group of Advanced Robotics and Mechatro |
Mariela Vargas | Instituto de Investigación en Ciencias Biomédicas (INICIB), Ricardo Palma University, Av. Alfredo Benavides 5440 Santiago de Surco, 15039, Peru |
Robert Castro | Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440 Santiago de Surco, 15039, Peru |
Ricardo Palomares | 1. Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440 Santiago de Surco, 15039, Peru. 2. Research Group of Advanced Robotics and Mechatronics (GI-RO |
Bryan Sanchez | Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440, Santiago de Surco, 15039, Peru |
David Allcca | Professional School of Mechatronics Engineering, Ricardo Palma University, Av. Alfredo Benavides 5440, Santiago de Surco, 15039, Peru |
Gary Nacarino | Department of Maintenance, Infrastructure, Equipment, and General Services Unit, Hospital Nacional Edgardo Rebagliati Martins, EsSalud, Av. Edgardo Rebagliati 490, Jesús María, 15072, Peru |
Jhony A. De La Cruz-Vargas | Instituto de Investigación en Ciencias Biomédicas (INICIB), Ricardo Palma University, Av. Alfredo Benavides 5440, Santiago de Surco, 15039, Peru |
Exoskeletons are crucial for providing intensive and consistent rehabilitation over a longer period and may be able to treat the patient without the presence of the therapist compared to manual therapy. This approach allows for frequent treatment reducing several costs. Therefore, this study aimed to examine the passive elbow rehabilitation of lateral epicondylitis patients, usability, and bioinspired design, to develop a mechatronics system with three rehabilitation positions. Regarding the biomechanical fundamentals of the elbow joint and as an engineering sustain, Computer-Aided Design (CAD) was made, consisting of Finite Element Analysis (FEA), anthropometric, ergonomic, and assembly analysis. The results showed that for the three rehabilitation positions, FEA showed von Mises stress less than the elastic modulus by a 103-factor resulting in no permanent deformation. Position 1, 2, and 3 produced angular displacements of 27°, 16.5° and 31° respectively with a total of 74.5°. An arm exoskeleton for passive rehabilitation of the elbow was developed using a pneumatic cylinder and an AD8832 electromyography (EMG) sensor, capable of detecting the EMG peak point to activate or deactivate the 24 V Arduino relay to flex or extend the elbow based on the positions. A total angular displacement of 74.5° was obtained instead of the simulated version 84.63°, with an error margin of 11.96%. The force during the three rehabilitation positions was 18 N exerted by the air compressor at a 6-bar constant pressure, and due to the use of choke valves.
Arm Exoskeleton; Bio-design; Passive Rehabilitation; Robot-Aided Rehabilitation
The upper limbs are associated with various important roles in daily activities. Therefore, a musculoskeletal and neurological disorder can affect the functions, reducing the patient quality of life, either due to a spinal cord injury, different disorders in motor neurons, or a Cerebrovascular accident (CVA) (Copaci et al., 2019). CVA is one of the main causes of mortality in the world because rehabilitation treatments have a special requirement for one or more therapists due to extended supervision time (Vargas, Cornejo, Correa-López, 2016).
A literature review on
exoskeletons and robotic rehabilitation showed significant advancements in
control and torque estimation. A Model Reference Adaptive Control (MMRAC) has
proven effective for controlling soft robotic exoskeletons, enabling both passive
and assistive control without the need for additional sensors, and showing
robustness against uncertainties (Toro-Ossaba et
al., 2024). Additionally, trend analysis showed that all analyzed
exoskeletons use flexion/extension movements, with aluminum and 3D printing, as
well as PID control algorithms and servomotors (Huamanchahua
et al., 2021). The most common applications are assistance and
rehabilitation (Cornejo et al., 2021).
An adaptive controller based on artificial neural networks demonstrated high
accuracy in gesture detection for wheelchair-mounted exoskeletons (Schabron, Desai, and Yihun, 2021). Twisted String
Actuators (TSAs) have proven effective in lightweight, customizable assistive
devices (Hosseini et al., 2020).
Topological optimization of transtibial prosthetic sockets using Finite Element
Analysis (FEA) demonstrated improvements in stress performance and weight
reduction, although it may also contribute to material fatigue over time (Faadhila, 2022). Compared to manual therapy,
exoskeletons have the potential to provide intensive and consistent
rehabilitation over a longer period (Lo and Xie,
2012).
Other studies focused on the
development and evaluation of innovative medical and rehabilitation devices
aimed at enhancing patient outcomes. For instance, a study designed a
Transforaminal Lumbar Interbody Fusion (TLIF) spine cage using reverse engineering,
followed by simulated compression tests, which demonstrated the capability to
withstand the forces typically encountered in spinal fusion surgeries (Norli et al., 2024). Another study
addressed the challenge of maintaining motivation in pediatric rehabilitation,
particularly for children with Developmental Coordination Disorder (DCD). This
study discussed the iterative co-creation of Matti, a pressure-sensitive
Tangible User Interface (TUI) for exergaming, which has shown potential as a
tool to improve patient engagement and the effectiveness of therapy sessions (Ockerman et al., 2024). These devices can
provide autonomous rehabilitation for patients, potentially reducing healthcare
costs and increasing treatment frequency. The main focus is on the bio-mechatronics
design and manufacturing of an arm exoskeleton with an electro-pneumatic
mechanism tailored for passive rehabilitation.
Passive robotic rehabilitation aims to restore
muscle and joint function by assisting the affected limb without requiring
active participation from the patient (Juarez et
al., 2021). Systems using electromyography (EMG) signals for
rehabilitation are typically categorized as active (Tiboni
et al., 2018), where movements from a functional limb are
mirrored by the affected one (Gull, Bai, and Bak, 2020;
Qassim and Hasan, 2020). However, this study focuses on a passive reflex
rehabilitation method, where both arms are simultaneously engaged, enhancing
recovery through synchronized motion. The classification for the device based
on the type of assistance provided is passive reflex (Maciejasz
et al., 2014) because it aims to assist the motion of the elbow
extremity while mirroring the other arm (Khalid et
al., 2023), The proposed exoskeleton assists in elbow movement, a
critical function in daily tasks, and is designed to improve rehabilitation
outcomes by mirroring the movements of the unaffected limb.
Upper limb
exoskeletons are used to rehabilitate patients with various conditions, such as
strokes (Cervantes et al., 2024),
cerebral palsy (Sandoval et al., 2023),
and neuromuscular diseases, helping to regain mobility of the arms, shoulders,
hands, and elbows (Lo and Xie, 2012). The
elbow is a hinge-type synovial joint located between the humerus with the ulna
and radius formed by the humeral trochlea, the spheroidal condyle, the
trochlear incision, as well as the head of the radius that allow flexion and
extension movements at an angle of up to 170° (Rodriguez
et al., 2022). The proximal radioulnar joint allows pronation and
supination of the forearm (Molina et al.,
2023a). Rotation occurs within a ring formed by the annular ligament and
the radial incision of the ulna, and these movements are essential for everyday
tasks (Rahlin, 2024).
Mechatronic
virtual bio-design and biomechanics are key areas for developing effective arm
exoskeletons for passive elbow rehabilitation (Cornejo,
Cornejo-Aguilar, and Perales-Villarroel, 2019). Virtual bio-design
allows the operation of these devices to be analyzed using computational
models, which has led to significant advances in design (Cornejo, Vargas, and Cornejo-Aguilar, 2020). However,
challenges such as improving comfort, portability, and efficiency remain. Elbow
biomechanics, on the other hand, studies the physical and mechanical principles
of joint movements, providing crucial information for the design of
exoskeletons that adapt to the anatomy and natural movements of the elbow (Molina et al., 2023b). This study
contributes to ongoing efforts by integrating mechatronic virtual bio-design
with elbow biomechanics, creating a low-cost, more effective, and personalized
exoskeleton for passive elbow rehabilitation. The objective is to enhance the
design and performance of exoskeletons through FEA and dynamic simulations,
offering a novel approach to rehabilitation technology.
The
manuscript is organized as follows; Section 2 describes materials and methods
for the bio-mechatronics design of the arm exoskeleton for passive
rehabilitation, including simulation of the control system. Section 3, focuses
on the system manufacturing and integration, while Section 4 describes the test
and results, focusing on the angular displacement and performance of the
system. In Section 5, the manuscript ends with conclusions and further work.
This started by
defining the problem and proposing an innovative solution to the classic
rehabilitation methods, with a robot-aided system intended to be accessible and
easy to use for patients (Cornejo et al.,
2023). To achieve this, the study problem consisted of assessing the
passive elbow rehabilitation of lateral epicondylitis patients and clearly
understanding the symptoms. As a next phase, the project was subjected to
evaluation of the clinical background as well as the usability of bioinspired
design, to develop a mechatronics system with the appropriate materials. The
specific requirements and constraints were then defined to produce a conceptual
design regarding the biomechanical fundamentals of the elbow joint. The
material selection consisted of appropriate elements for the exoskeleton and
the actuators. As an engineering sustain, Computer-Assisted Design (CAD) was
performed, consisting of FEA, anthropometric, and ergonomic, as well as
assembly analysis. Consequently, the mechatronics system design, simulation,
and manufacturing were prepared. This passive elbow system can be used in
rehabilitation and clinical centers, as shown in Figure 1.
2.1. Digital twin
A
digital twin can be described as an integrated multi-physics, multi-scale,
probabilistic simulation of a complex product that uses the best available
physical models and sensor updates to mirror the life of corresponding twin
according to Michael Grieves (Jiang et al.,
2021). Although initially focused on industrial applications, digital
twins have reached the medical sector, for robot-aided rehabilitation, where
the main goal should be relatively simple mechanically. The structure must be
easy to put on and training within the limited space, with an additional
bio-signals tracking system (Falkowski et al.,
2023). Following the digital twin and the design methodology for
rehabilitation robots (Martínez and Z.-Avilés,
2020), the mechanical, electronic, control, and programming system for a
passive elbow rehabilitation exoskeleton station was developed.
Figure 1 Experiment
set-up and apparatus.
2.2. Free
body diagram
Initially, the system was
encharged to transmit force to replicate the flexion and extension of the
elbow, which naturally has a range from 0° to 150° (Martin
and Sanchez, 2013). The functional range of movement is from 30° to 130°
(Felstead and Ricketts, 2017), hence, a
smaller range was considered to not damage the elbow joint, from 30° to 90°. To
reach this point, three rehabilitation positions were considered due to the
limitation of the pneumatic cylinder at 100 mm axial displacement. Each
position produced a 30° angular displacement taking as a reference the position
of the joint. As an initial phase, the free body diagram of the system was
developed to understand how the system will behave as well as the kinematic and
dynamic interactions. Equation (1) describes the geometrical restrictions:
Where represent the lengths of
the link that transmit the force to the revolute joint, represent the
angle formed with a vertical plane as shown in Figure 2. a., by modelling the
dynamics of the system using the second law of Newton (Rojas-Moreno,
2001). The linear and rotational motion between the first link and the
pneumatic coupling (Figure 2. b.) as well as between the elbow pad and the
second link is described in equations (2), (3), and (4) while the variables are
presented in Table 1:
Table 1 Description of Variables and Model
Parameters
Figure 2 Free body diagram of the system to be developed: a) Geometrical
relationships between the links that transmit force, b) Forces interaction on
the link and the coupling of the pneumatic cylinder, c) Forces interaction
between the link and the revolute joint
2.3. Mechanical design considerations
The
mechanical design was carried out using ISO 7250-3:2015 (ISO, 2015), which standardizes the dimensions of the elbow to
wrist (267 mm), and shoulder to elbow (340 mm). Half of the arm was taken to
make the elbow pads considering ergonomics comfort (Schiele
and Helm, 2006), with one in the arm of 170 mm, and the other in the
forearm of 134 mm. After several simulations, the appropriate link distance was
110 mm to achieve the three rehabilitation positions. For the table support,
the proper distance was 50 mm, and when the user feels uncomfortable,
regulating the chair height can solve this inconvenience. For the actuator,
pneumatic cylinders were considered by offering a high force-to-weight ratio,
being able to be stopped at any time without causing injuries, and requiring
less maintenance, which is desirable for a potentially mobile rehabilitative
exoskeletal system (Karanth P, and Desai, 2022; Burns
et al., 2020; Carvalho, Gopura et
al., 2016; Zhang et al., 2008). As shown in Figure 3, the
hole system can be assembled by the patient putting the table support stable
and then connecting the elbow pad, the link mechanism to the pad, and pneumatic
cylinder. The parts come with a tolerance of 0.15 mm, allowing a correct
coupling, which needs only two ?” screws or the link mechanism. At the same
time, the pneumatic cylinder can be introduced by pressure. To make the system
accessible, polylactic acid (PLA) was considered, as 3D printing technology can
be used to sustain the patient needs in short periods (Demeco
et al., 2023), by customizing the designs and maximizing the
times of fabrication.
Figure 3 System developed
for passive elbow rehabilitation: a) Isometric view, b) Exploded view
2.4. Finite element analysis
In
the analysis, two groups were considered, one for the elbow pads, and the other
for the rehabilitation station. A previous study developed human arm parameters
(Speich, Shao, and Goldfarb, 2005), to
simulate the weight distribution of the forearm and arm using FEA. As a
reference point for the studies, the base of each elbow pad was considered a
fixed geometry. A normal force of 150 N was applied to the face in contact with
the arm in the elbow pads, by taking a median of 10 kg for the human arm mass,
and a scale factor of 1.5 was used for the analysis. Furthermore, the material
selected was ABS from the SolidWorks standard library, with an elastic module
of 2 GPa. The maximum von Mises stress for elbow pad 1 was 56.234 N/m2,
and for elbow pad 2, it was 331.985 N/m2. Under the material elastic
limit, there is no permanent deformation.
The second group of FEM studies was
made to the table support and the linkages. Figure 4a shows that l1
receives a von Mises stress of 135 MPa in the bolted joint, hence, a ?” screw
was considered as the maximum shear stress of 145 MPa according to the
‘Structural Design of Stainless Steel’ (BSSA, 2001).
For the table support, Figure 4b shows a maximum von Mises stress of 8.567 MPa
for the elbow joint, while Figure 4c shows a maximum of 6.287 MPa for the bolted
joint in the pneumatic cylinder coupling. Figure 4d shows a maximum von Miss
stress of 20 MPa in the joint to the elbow pad. However, these values are less
than the elastic module by a 103-factor.