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Conference Paper 

Kinematic and Dynamic Analysis of the Human Hand’s 
Articulation for Wearable Soft-Robotic Device Applications

Paschalina-Danai Sarra, Vasiliki Fiska, Konstantinos Mitsopoulos, Diamanto Mylopoulou and Panagiotis D. Bamidis*

Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece.

* Corresponding Author Email: bamidis@auth.gr

ABSTRACT

Robot-assisted therapy, particularly hand exoskeletons, has emerged as a promising approach to address hand function limita-
tions caused by neurological diseases that can significantly impact mobility, balance, and posture, leading to physical, psycho-
logical, and societal challenges. Traditional rigid-body robots, while helpful, have limitations in safety and dexterity, spurring 
research into soft robotics in neurorehabilitation. The research presented in this manuscript focuses on the advancement of a 
Soft Robotic Glove prototype developed for neurorehabilitation, integrated into the NeuroSuitUp Body-Machine Interface. This 
glove, composed of five PneuNet pneumatic actuators and a multi-sensor system, is designed to facilitate natural hand move-
ments. To optimize the glove’s functionality, kinematic and dynamic analyses of the human hand were conducted. Specifically, 
a kinematic model of the hand, with 19 links representing human bones (phalanges) and 24 joints connecting them, was de-
veloped indicating the 24 degrees of freedom of the human hand. By understanding the forces applied to the finger phalanges, 
the movement of the entire finger can be predicted. This knowledge aids in designing personalized exoskeletal hand devices 
tailored to individual patient needs. Further research aims to combine this model with a dynamic model of the actuators and 
investigate the device's effect on hand performance through computer simulations.

Keywords—Soft robotic device, Kinematics, Dynamics.            

Copyright © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
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85 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

INTRODUCTION

Neurological diseases, such as Cerebral Palsy (CP), 
Parkinson’s Disease (PD), and Spinal Cord Injury (SCI) 
affect a great percentage of the world’s population. These 
diseases can significantly affect a person’s mobility, balance, 
and posture, having a significant physical, psychological, 
as well as societal impact.1 In the past few decades, a wide 
range of studies about robot-assisted therapy have been 
developed to help alleviate the effects of these diseases. 
These neurological pathologies usually affect the proper 
physical functions of a patient’s hand and therefore, they 
can create limitations in performing activities of daily 
living. As a result, numerous hand exoskeleton systems 
have been developed aiming to the hand rehabilitation.

This research focuses on the mathematical analysis of 
the human hand’s kinematics and dynamics, for the pur-
pose of developing more efficient rehabilitation devices. 
Through mathematical modeling, the exact motion and 
forces of the interaction between a robot and the human 
body can be determined. More specifically, the degrees 
of freedom, position, and orientation of the end effector, 
as well as the forces that need to be applied for the sys-
tem’s operation, can be defined. This result enables the 
personalization of rehabilitation devices and exercise 
regimens, depending on each patient’s condition and the 
specific system operational parameters.

As an assistance to the aforementioned motor dis-
abilities, ongoing development of soft robotics for neu-
rorehabilitation purposes has been observed in the past 
years. This emerging field uses lightweight, flexible, and 
compliant devices, built from materials with mechanical 
properties similar to those of living organisms. Compared 
to the traditional rigid-body robots, these new types of 
robotics are designed and manufactured in a very in-
novative way in order to secure safety with the patient, 
dexterity, but also high performance.2

A wearable prototype in the shape of a glove has been 
designed and developed for neurorehabilitation purposes, 
as mentioned above. As shown in Figure 1, it consists of 
an actuation system with five PneuNet pneumatic actua-
tors initiating the typical human hand movement, such as 
grasping an object, and a multi-sensor system.3 The device 

is part of the NeuroSuitUp body-machine interface (BMI), 
which is a platform consisting of a wearable robotics 
jacket and glove, along with a serious game application 
for neurorehabilitation purposes.4 In order to understand 
and optimize the soft robotic glove’s future function, the 
proposed research describes the kinematic and dynamic 
analysis of the human hand and fingers, specifically.

METHODS 

The proposed kinematic model of the hand consists of 
19 links, which imitate the corresponding human bones 
(phalanges), and 24 joints, which connect the phalanges/
links of the fingers. Therefore, the hand system is de-
fined as having 24 DoFs. Figure 2 depicts the kinematic 
configuration of the human hand with all the joints J(i,j) 
of the five fingers, where i ={1,2,3,4,5} is the number of 
fingers and j ={1,2,3,4} is the number of joints in each 
finger. The four joints of the fingers, starting from the 
palm to the fingertip, are the Carpometacarpal (CMC), 
Metacarpophalangeal (MCP), Proximal Interphalangeal 
(PIP), and Distal Interphalangeal (DIP) joint.5,6

FIGURE 1. Soft-robotic glove device.3 

FIGURE 2. Configuration of the human hand joints.

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 86

Figure 3 presents the open-chain kinematic configu-
ration for one of the index, middle, ring, and little finger. 
The joints represented are the CMC, MCP, PIP, and DIP. As 
shown, the MCP joint consists of 2 DoFs, since the one is 
for the flexion-extension movement and the second one 
is for the adduction-abduction movement of the finger. All 
the other joints perform the flexion-extension movement. 
Each joint is represented by its own frame of origin with 
regard to the wrist reference frame R0. 

The aforementioned configurations are used to calcu-
late the Direct Kinematics equations in order to define the 
position and orientation of the end-effector (fingertip) 
as functions of the joint variables. In this modeling, the 
Denavit-Hartenberg (DH) method is used and the param-
eters are shown in Table 1.7 

The general form of the Transformation Matrix Ti, 
based on the DH parameters, is the following: 

Equation 1 shows the final Direct Kinematics Model-
ing of one finger i:

where Ti is a matrix representing the final position and 
orientation of the fingertip;   is a geometrical trans-
formation matrix from the (j−1) reference frame of the 
i-finger to its j-reference frame;   is a geometrical 
transformation matrix representing the final position of 
the fingertip regarding the 5th reference frame.

After the development of the kinematic model of each 
finger, the Dynamics equations can be calculated using 
the Euler-Lagrange method. In this case, it applies on one 
of the four fingers (index, middle, ring, middle) and it is 
considered to have the Metacarpophalangeal joint fixed 
for simplification purposes. 

The dynamic configuration of the index finger is pre-
sented in Figure 4, and consists of the three MCP, PIP, and 
DIP joints. Each joint has its own reference frame, while 
the R3 is the base reference frame. It is assumed that the 
center of mass of each link is located as shown in Figure 3 
and has a position vector Gj. As a result, the three generic 
position vectors of the three links with respect to the base 
frame R3 are calculated and are the following6:

FIGURE 3. Kinematic configuration of the index finger. 

TABLE 1. DH parameters for the Direct Kinematics. 

Joint aj αj dj θj

CMC 1 0 π/2 0 θCMC

MCP(ab/ad) 2 L01 −π/2 0 θMCPa/a

MCP(f/e) 3 0 π/2 0 θMCPf/e
PIP 4 L11 0 0 θPIP
DIP 5 L21 0 0 θDIP

(1)

(2)

FIGURE 4. Dynamic configuration of the index finger. 

(3)

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87 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

where φ4 = θMCP + θPIP and φ5 = θMCP + θPIP + θDIP.

The Lagrange-Euler equation is the following:

where L=K−P. K is the kinetic energy of the system, P the 
potential energy of the system and Fgen the generalized 
external forces applying on the upper side of the finger 
phalanges, while q is the generalized coordinate, which 
in this case is the angle θj. The term of Fgen is not being 
described thoroughly at the present time, but will be 
estimated in future research.

The kinetic energy of the center of mass of each finger 
joint is obtained through the following equation: 

where mj is the average mass of each joint j, Jvi is the linear 
velocity Jacobian, Jωj is the angular velocity of the joint, 
Ιj is the moment of inertia of the joint and θ̇ the angular 
velocity.

The dynamic energy of the center of mass, which in-
cludes the gravitational term, is obtained: 

DISCUSSIONS 

Further research in the future will aim to combine both 
the aforementioned model and the dynamic model of the 
actuators, as well as the way the exoskeletal device affects 
the performance of the patient’s human hand. Moreover, 
executing computer simulations is proposed, in order to 
validate the results of the above research.

CONCLUSION

The emerging progress of the soft-robotics field has 
led to the development of numerous exoskeletal soft 
robotic devices aiming at neurorehabilitation. The above 
research describes the kinematic and dynamic model of 
the human finger, in order to solve the direct dynamics 

of the finger. Therefore, given the forces applied on the 
phalanges of the finger, the movement of the whole finger 
can be calculated and a suitable personalized exoskeletal 
hand device can be designed.

ACKNOWLEDGMENTS 

This work has been supported by the NeuroSuitUp 
and HEROES project, in the Medical Physics Laboratory, 
School of Medicine, Faculty of Health Sciences, Aristotle 
University of Thessaloniki, Greece. Special thanks to Dr. 
Alkinoos Athanasiou and Kostas Nizamis, University of 
Twente.

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