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A Two-Bit Binary Control System for an Orthotic, Hand-

Assistive ExoskeletonDonald J. Bucci, Sana F. Fathima, Brett F. BuSha

The College of New Jersey: Department of Electrical and Computer Engineering2000 Pennington Road, Ewing, NJ, 08628

Abstract

157

A combined 450,000 Americans suffer from multiple sclerosis, chronic carpal tunnel

syndrome, and muscular dystrophy, debilitative disorders that result in a loss of muscle

strength and dexterity within the afflicted individual. A controlled orthotic exoskeleton

presents a solution by amplifying a user’s residual strength to restore precision pinch

and/or power grasp motions. In order to optimize system response time and control

accuracy, a two-bit binary state control algorithm was designed using LabVIEW v8.5. Thestates of the control system were refined using positional and object-sensing information

supplied via negative feedback from Hall effect angular sensors and force sensing

resistors. Forearm EMG was recorded and used to characterize hand strength with and

without the mechanical assistance generated from the hand-assistive exoskeleton. The

control system was tested using simulated sensor feedback data and has proven to be a

robust design. The controller will be integrated with a glove-like, hand-based exoskeleton.

The complete system was designed to restore hand functionality through the amplification

of precision pinch and/or power grasp.

Background

Problem:

A combined 450,000 Americans suffer from debilitative disorders, such as multiple

sclerosis and muscular dystrophy, resulting in the loss of muscle strength and dexterity

in the hand.

Solution:Orthotic hand exoskeletons may be used to amplify the remaining muscular control in

the hand and facilitate pinching and grasping movements. The restoration of dexterityand the achievement of synergistic motion lie primarily within the device’s control

algorithm.

Current Designs and Limitations:•Binary Control Algorithms -->The controller reads the input signal, compares it

against a predefined threshold, and sets the output state accordingly. Binary control

usually exhibits faster object interactions through increased system response times,

but at a cost of a decrease in control precision [1].

•Variable Control Algorithms --> System output signals are related proportionally

and linearly to input control signals. Saturation of the output occurs if the input

signal falls outside of a predefined range. Variable control systems exhibit arelatively greater level of control precision for more delicate objects; however this

was contrasted with a slower response time when compared to other binary

algorithms [1].

Design Objective:

Our objective was to design a dynamic digital control system with the

benefits of binary and variable control architectures: one that would exhibit

the necessary balance of control accuracy and response that would more

accurately match hand physiology.

Figure 3. Proposed dynamic state control system flowchart. Flowchart represents the logic for a

single motor driven output (capability for the independent control of four motors).

Proposed Control System Design

Input Signal Quantification:

Based on the notion that a binary control system acts as a state controller, a two-bit binary

control algorithm was realized (four total states).The input control signals were divided into

four areas based off of the maximum expected value of the incoming signal (Fig. 2). Each

area was associated with a different state for each motor driven output.

Figure 2. Two-Bit binary control example given a generic input control signal (red). Based on where the

instantaneous value of the signal is located with respect to the ranges (blue, green, and yellow), a specific

output state will be selected.

Dynamic State System:

• Joint angles --> Continuously measured by the control system. If a single joint angle

existed outside of its predefined range, then all system output states would be forced

to zero, preventing any output that could result in phalangeal hyperextension.

• Palmar Resistance --> Quantified to detect the presence of an object. These

resistance measurements were grouped based on their applicability to either the

precision pinch or the power grasp. If a resistance measurement surpassed a pre-

defined threshold, then the flexion output states would be reduced accordingly to

allow for more accurate control and delicate motions.

Figure 1. An orthotic hand exoskeleton for the amplification of a precision pinch and power grasp.

Hardware and Feedback

Digital Control System:• Designed in LabVIEW v8.5 on a laptop computer with an external Data Acquistion

Unit controlled via USB (Fig. 4).

Instrumentation:• Force sensing resistors (FSRs) used to obtain flexion and extension data, in addition

to palmar resistance measurements (Fig. 5)

• Hall Effect sensors used to obtain joint angles (Fig. 5)

Figure 4. Front panel of designed digital control system. Developed in LabVIEW v8.5.

Figure 5. Instrumentation placement diagram. Force Sensing Resistors for flexion/extension (green circles),

Force Sensing Resistors for palmar resistance feedback (light green circles), and Hall Effect sensors (green

squares) shown respectively.

Conclusion

We designed and implemented a dynamic state control system for an orthotic assistivehand exoskeleton that is able to more closely mimic physiological movement. Futureevaluations of control system functionality include:

• Quantification of grasping strength improvement

• Quantification of dexterity and control precision

References and Acknowledgments[1] Lucas, Lenny. Yoky Matsuoka, and Matthew DiCicco. "An EMG-Controlled Hand Exoskeleton for Natural

Pinching." Journal of Robotics and Mechatronics. 2004. 482-8.

We would like to thank The College of New Jersey School of Engineering and Dean Steven Schreiner for funding

support. We would also like to acknowledge the contribution of Dr. Marvin Kurland towards the design of the control

algorithm.

Resulting Control System Flowchart: