Implementation of Real Time Control Algorithm for Gait Assistive Exoskeleton

Devices for Stroke Survivors

Jhulan Kumar, Neelesh Kumar, Dinesh Pankaj, Amod Kumar Biomedical Instrumentation Unit,

Central Scientific Instruments Organisation, (CSIR-CSIO), Chandigarh 160030 India

*Email: jhulan.gnit@gmail.com

Abstract- Controlling human gait by wearable assistive

devices is a dynamic and time critical activity and thus

requires a dedicated real time control environment. The

paper discusses an implementation strategy for real time

control algorithm for GaExoD prototype. Control approach

follows gait trajectory using feedback sensors and actuators

for movement control. NI Lab VIEW, Robotics, FPGA and

RT module were used and prove beneficial in shorter

development time. Position control errors were estimated

for standing and sitting functions provided which is

significantly lower for sitting function.

Keywords-Exoskeleton device, Real time control, Gait

phases, LabVIEW

I. INTRODUCTION Exoskeleton Devices (ExoD) are wearable robotic

mechanism used to support and augment the physical

action performed by human body. Earlier

development of these ExoD was envisaged as

mechatronics devices to support lifting & carrying

more weight by soldiers. In the last decade, there are

research evidences which support the effectiveness of

robot assisted rehabilitation. [1, 2] According to an

estimate in USA there are about 700000 people suffer

stroke every year. About 50% of the stroke survivors

required assistance in performing daily activities. [3]

Mobility disorders after stroke is the most common

among stroke survivors. Research activities are going

on for developing external wearable mechanics to

support walking of stroke patients. Literatures

confirm that these robotic devices are able to perform

the gait rehabilitation of stroke patient in much

improved and efficient manner. [4] These devices help

to achieve variable gait patterns and extended range

of activities on Assistive Daily Living (ADL) scale.

Authors at CSIR-CSIO are involved in development

of Gait Assistive Exoskeleton Device (GaExoD). [5]

Human gait is rhythmic activity involving multiple

joint having multiple degrees of freedom and

kinematics. For accurate control it is important to

measure range of motion is important while the

kinetics and physiological activity parameters are

need to be monitored in real time [6]. Realizing a

natural gait with an externally worn mechanism

2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies

978-1-4799-2102-7/14 $31.00 © 2014 IEEE

DOI 10.1109/ICESC.2014.99

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with limited degree of freedom is a challenging task.

For implementing the real time gait control the

controller demands higher processing capabilities.

The sequential controllers like PLC, microcontrollers

etc. will limit the performance and thus there is a

need of a controller and control algorithm which

executes the process in real time. The paper discusses

the algorithm developed for controlling developed

prototype of GaExoD using parallel processing of

input data and implementing it on a FPGA hardware.

II. METHODS AND MATERIALS

A. GaExoD Prototype Development:

Authors developed a prototype of wearable

exoskeleton mechanism which supports the walking

of person recovering from stroke. It has three joint

segments, hip, knee and ankle with 1 degree of

freedom at each respective joint. The gait cycle

movement was achieved by synchronizing all three

joints. The range of joint angle motion was recorded

with 3 axis accelerometers and in-house developed

electrogoniometers. The high torque of selected

actuators can support the walking of subject’s

weighing up-to 100 kg. A body unweighing system

can also be used in conjunction to reduce the torque

requirement.

B. Controller Design:

Gait cycle for a healthy human typically completes in

0.9 second. The gait cycle is divided into swing phase

and stance phase which is further subdivided into

seven gait phases. The smallest gait phase duration is

10% of gait cycle. To control the gait in real time the

controller should respond a programmable control

action in 90ms. The input processing time is critical

and dependent of the type of input signal used. When

the controller has to process the bio-physical signal

like EMG then the design architecture of controller

becomes important. The figure 1 shows the controller

design; the Real time processor handles the logic

element and communicates information with other

devices. The main task of a reconfigurable FPGA is

to process the input information and update the

actuators position. The process control algorithm is

executed by real time processor of 1.33 GHz. It can

be used in network mode with high speed gigabit

communication protocol.

Figure 1: architecture of RT Controller

C. Control Algorithm

The closed loop control algorithm uses feedback

input from sensors mounted on the GaEx GaExoD

prototype. Figure 2 shows the block diagram of the

control.

Figure 2: Block diagram of control

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The gait is rhythmic activity with seven distinguish

phases. These phases have different range of joint

angles for different human joint during a dynamic

gait. Table 1 informs about the various joint angle

ranges computed from OpensimTM Simulation

software. These ranges are also validated by

performing gait experiments in lab on normal healthy

individuals he generated gait range database also acts

as a reference data base to compute the algorithm

error and achieve near normal gait. The control is

generating the gait trajectory with normal walking

speed. It scans the sensor information to know the

dynamic position of each joint and generates a

command to achieve next gait phase position. The

controller generated commands to all six actuators in

real time. The control for sitting and standing

function of GaExoD was also developed. The control

algorithm was developed using NI LabVIEW 2012,

Robotics, FPGA and RT modules.

Gait phaseposition

Hipmovement

(deg.)

Kneemovement

(deg.)

Ankle movement

Initial contact 30 0 Neutral (0)

Loading response 30-35 0 to -15 0 to 15PF

Mid stance 35-0 -15 to 0 15PF to 10DF

Terminal stance 0 to 0 0 to 0 10DF to 0

Pre swing -10 to 0 0 to -35 0to 10PF

Initial swing 0to20 -35 to -60 20PF to 10PF

Mid swing 20 to 30 -60 to -30 10PF - Neutral

Terminal swing no change -30 to 0 Neutral

Table 1: Joint range angles for Hip, Knee & Ankle

Joints during walking over level ground

III. RESULTS

For testing the developed control algorithm is

deployed on the selected RT controller. The GaExoD

prototype has operated for several gait cycles. The

trajectory of knee hip and ankle joint were recorded

and analysed for error estimation. Algorithm was

tested for fault tolerance by creating several possible

events where it can lose its dynamic position and

results in abnormal gait cycle. The role of the

exoskeleton control algorithm is to follow the

trajectory of normal gait cycle. The gait activity to

sub phasic gait level is also recorded to compute

control error. The error was estimated for stand

position and sit position in Fig. 3 & 4 respectively.

The calculated error during stand position was higher

at knee joint and lower at ankle joint. This range of

motion and higher dynamism at stand position were

the contributing factors to these errors. The error

estimation for sit position was significantly lower for

all joints as sit position being the terminal position.

IV. CONCLUSIONS

The real time control algorithm for gait cycle was

successfully implemented using RT controller.

Developed algorithm uses trajectory estimation

approach for control. Algorithms was deployed and

tested on a RT controller for gait cycle, sitting and

standing phase. The NI LabVIEW development

platform and associated modules were used for faster

and efficient algorithm development. The trajectories

for hip, knee and ankle joints of prototype were

recorded for estimating error. The error at standing

position which is also the reference position is higher

than the sitting position which was a terminal

position. The error can be reduced by using adaptive

control approaches in future.

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Figure 3: Estimated error during stand position

Figure 4: Estimated error during sit position

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ACKNOWLEDGEMENTS

The authors gratefully acknowledge the support of

Director, CSIR-CSIO, Chandigarh through BSH PSC

0103-05. The authors acknowledge the support of

Arpan Nath & Ratan Das for help in integration &

trial activities.

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