Keywords

1 Introduction

1.1 Background

Recently, robotic technology has been applying to rehabilitation such as robot therapy for patients with stroke and brain injury [1]. Some gait training robots, such as LOKOMAT and HAL are successfully developed, which make it possible to recover from lower limbs motor disorder for stroke patients [2, 3].

Moreover, with the development of neuroscience, the reorganization and plasticity of human brain induced by motion skill learning are being revealed gradually [4, 5]. Functional Magnetic Resonance imaging (fMRI), which measures brain activity of a human in real time by detecting changes of blood flow [6], plays an important role in observing brain activation while motion learning is executed by a subject.

However, most of current exoskeleton robots could not be used in MRI environment to measure brain activity because magnetic materials are forbidden in MRI environment. Some researchers developed some gait-like MRI compatible devices to study lower limb movement, however there are also plenty of patients with hand deficit [7]. Hence, a hand robot corresponding to a high magnetic field environment is therefore required.

1.2 Purpose

The purpose of this study is to develop an wearable MRI-compatible hand exoskeleton robot that supports a human moving his fingers in high electromagnetic field voluntarily and involuntarily. This robot executes passive and active movements when a subject wears this robot. The final purpose of this study is to evaluate which kind of motion learning will improve motor function for patients with stroke. We will therefore measure human brain activity in real time by fMRI while this assistive hand robot is used to provide finger motions for a subject.

2 Development of an Wearable MRI-Compatible Hand Exoskeleton Robot

The developed hand robot is shown in Fig. 1. The exoskeleton of the hand robot consists of four exoskeletal fingers excluding the thumb, which is constructed with nonmagnetic materials through 3D printing. This robot is designed for supporting tapping motion while a subject wears the exoskeleton hand. Air pressure provides extension power for each fingers independently by air tubes.

Fig. 1.
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Exoskeleton hand and button box

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2.1 Materials Selection

This hand assistive robot will be applied around an MRI scanner, a high magnetic field. There are plenty of challenges associated with using the device in MRI scanner. Any devices used for performing movements during MRI must be designed particularly for the special environment and fabricated with low magnetic or non-magnetic materials [8]. There are mainly two reasons. Firstly, the robot should not disturb the imaging process and impact the scanner environment. Secondly, the magnetic field should not affect the function of robot [9, 10]. Considering these constrains, we choose pneumatic actuators to drive the joints of the wearable robot. And the exoskeleton is fabricated with nonconductive resin materials through 3D printing, shown in Table 1.

In addition, potentiometers are used to measure the angle of joints. These potentiometers contain low magnetic materials, and the influence is ignorable for MRI machine, because they are located around outside of MRI scanner.

Table 1. Materials selection
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2.2 Exoskeleton Design

The exoskeleton is designed for human left hand that consists of four independent exoskeletal fingers.

Fig. 2.
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Assembly drawing of exoskeleton hand

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Figure 2 refers to an assembly drawing of the exoskeleton hand. Each finger comprises three joints, but only PIP and MP joints are independently driven by air pressure controlled by solenoid valve. In addition, each finger is assembled with a ball joint, which allows each finger moving in three degrees of freedom. Therefore this exoskeleton gives 17 degrees of freedom for movement in totally. Potentiometers are installed in MP and PIP joints of four fingers to measure the angles of the motions.

Fig. 3.
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Structure of middle finger

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Fig. 4.
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Slider structure to flex PIP joint

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Figure 3 shows the structure of middle finger. Each finger is connected with a ball joint, and each joint is designed a stopper in case of hyperextension of finger.

Figure 4 is a slider structure to flex PIP joint all of finger. The air gets in from the tube fitting, and then drives slider to move. An O-ring is used to keep a good air tightness with less friction while a joint moves passively.

Fig. 5.
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Lever crank mechanism

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Figure 5 refers to a lever crank mechanism of MP joints of middle finger and ring finger. Potentiometers are connected with the cran link, so we can measure joints angles when moving. To describe the calculation process of the angle of potentiometer, we use some characters to instead of angles and length. We assume that \(\alpha \) is the angle of potentiometer, \(\beta \) is the angle between b and the parallel line of d, \(\theta \) is the angle of straight line of a and d.

$$\begin{aligned} a\cos \theta +b\cos \beta =c\cos \alpha +d \end{aligned}$$
(1)
$$\begin{aligned} a\sin \theta =b\sin \beta +d\sin \alpha \end{aligned}$$
(2)
$$\begin{aligned} b^{2}\cos ^{2}\beta =(-a\cos \theta +c\cos \alpha +d)^{2} \end{aligned}$$
(3)
$$\begin{aligned} b^{2}\sin ^{2}\beta =(a\sin \theta -c\sin \alpha )^{2} \end{aligned}$$
(4)

If we suppose A = d + \(a \cos \alpha \), B = \(-c \sin \alpha \),

$$\begin{aligned} 2Ba\sin \theta -2Aa\cos \theta =b^{2}-a^{2}-A^{2}-B^{2}=\sqrt{4A^{2}a^{2}+4B^{2}a^{2}}\sin (\theta +\gamma ) \end{aligned}$$
(5)
$$\begin{aligned} b^{2}\sin ^{2}\beta =(a\sin \theta -c\sin \alpha )^{2} \end{aligned}$$
(6)
$$\begin{aligned} \sin (\theta +\gamma )= \frac{b^{2}-a^{2}-A^{2}-B^{2}}{\sqrt{4A^{2}a^{2}+4B^{2}a^{2}}} \end{aligned}$$
(7)
$$\begin{aligned} \theta =\sin ^{-1}{\frac{b^{2}-a^{2}-A^{2}-B^{2}}{2a\sqrt{A^{2}+B^{2}}}}-\tan ^{-1}{\frac{A}{B}} \end{aligned}$$
(8)

Hence, we can calculate the angle of movement by \(\Delta \theta \) according to the parameters listed in Table 2.

Table 2. Parameters of joints
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2.3 Control Method

Figure 6 refers to the block diagram for control. This robot supports four fingers excluding a thumb of a human hand moving independently by corresponding to pneumatic actuators.

The power for finger flexion is provided by an air compressor, which is able to export stable air pressure for motion. Eight five-meter air tubes are used to transfer power for each joints, because the compressor and the control box are not permitted taking into MRI room. The output of air is controlled by solenoid valves. Rubber bands provide power for finger extension. A microcomputer based on linux is used to control solenoid valves, record angles of joint motion and tapping time.

Fig. 6.
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Block diagram for control

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3 Performance Evaluation

3.1 Flexion Range of Fingers

A potentiometer installed in each joint is used to measure the angle of the joint. Although these potentiometers contain low magnetic materials, the influence is ignorable for MRI machine.

According to the result of measurements, the flexion range of each joint is shown in Table 3. The exoskeleton covers 70% of rang of motion of the human fingers. Although the movable angle of fingers are smaller than human hands, it is enough to support passive tapping motions in our experiments.

Table 3. Flexion range of fingers
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3.2 Time Delay

This wearable robot will be applied in an MRI environment directly. The air compressor and control box have some magnetic components, therefore they are not permitted taking into MRI room. So we need to prepare multiple length of air tubes to provide air pressure for exoskeletal hand movement in MRI. Increasing length of air tubes will enlarge the motion delay of hand robot. The Table 4 shows the time delay of the robot in different conditions. According to result, the time delay is less than 0.15 s, which indicates it is acceptable.

Table 4. Time delay
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3.3 Fingertip Force

For clarifying the supporting force for finger motion, we use a high accuracy electronic scale to measure fingertip force of each finger. The air pressure is 90 psi and the air tube is 5 m. According to test results, all fingers generate near 2N fingertip force, which is enough to assist exoskeleton finger moving involuntarily. The result is shown in Fig. 7.

Fig. 7.
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Fingertip force

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4 Conclusion and Future Work

This study described an MRI compatible tapping assistive robot. Pneumatic actuators were therefore used to drive the joints of the wearable robot. We then tested flexion range of each joint and fingertip force to ensure that the exoskeleton was able to support a subject moving his hand involuntarily. In the future, we will assess the performance of the robot in an MRI environment by measurement experiments of a subject’s brain activities. Finally, the compatibility of the assistive robot in MRI environment will be confirmed.