Implementation of Real Time Control Algorithm for Gait Assistive Exoskeleton Devices for Stroke Surv

Advances in Biomedical Engineering Research (ABER) Volume 3, 2015 doi: 10.14355/aber.2015.03.001

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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 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

Introduction Exoskeleton Devices (ExoD) are wearable robotic mechanism used to support and augment the physical action performed by human body [1-5]. Earlier development of these ExoD was envisaged as mechatronics devices to support lifting & carrying more weight by soldiers [6]. In the last decade, there were research evidences which support the effectiveness of robot assisted rehabilitation. [7- 10] According to an estimate in USA, there are about 700000 people suffering stroke every year [11]. About 50% of the stroke survivors required assistance in performing daily activities. [12] 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 [13]. 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 [14] (GaExoD). 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 while the kinetics and physiological activity parameters are need to be monitored in real time [15]. Realizing a natural gait with an externally worn mechanism 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 FPGA hardware.

Methods and Materials 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 (ADXL) and in-house developed electrogoniometers. The high torque of selected actuators has Max force-12000 N, Max self-locking- 800 N, Stroke- 100 mm, Max speed 12 mm/Sec. It can

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Advances in Biomedical Engineering Research (ABER) Volume 3, 2015

support the walking of a subject’s weighing up-to 120 kg. A body unweighting system can also be used in conjunction to reduce the torque requirement.

Controller Design The total elapsed time for one gait cycle, also known as stride time, is for a healthy human typically completing in 0.9 second [16]. The gait cycle is divided into swing phase and stance phase which is further subdivided into seven gait phases [17-20]. 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 [21, 22]. 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

Control Algorithm The present research implements, an improvement of previously developed algorithms was designed for gait control of indigenously developed prototype. It received an input command from sensors and decided to enter into next cycle. The nonlinearities of the feedback sensors causes to fluctuate input sensor data which are required to remain stable for optimum control. To overcome this, in present algorithm, the sensor output was taken as a range of data instead of point data. This solved the control errors due to which the controller did not enter into the next gait phase in turn reduced the failure rate. The earlier developed prototype had the knee angle range of 100 ◦ that was not sufficient for ascend / descend on stair. The control was suitably modified to work in the required extended range.

The closed loop control algorithm uses feedback input from two different sensors ADXL & electrogoniometers which are mounted on the GaExoD prototype. Electrogoniometers measure the rotation of the each joint. Its sensitivity is 19mv/˚ and range is ±120˚. ADXL measures the tilt along X, Y & Z axis and calibrates to measure roll & pitch angle. Its range is ±180˚ with resolution of 1˚. Both sensors were putted on subject’s hip, knee and ankle position. Both sensors outputs data of subjects were recorded in normal walking mode for different cycle and joints angle range were tabulated after study & evaluation. Purposed algorithm deployed on cRIO-9014 controller has a clock time 400 MHz and respond time is in ns. It is implemented using continuous proportional control algorithm.

Figure 2 shows the block diagram of the control. The generated gait range database also act as a reference database to compute the algorithm error and also to achieve near normal gait cycle. Gait experiment for measuring the range of motion and spatiotemporal parameters to sub-phasic level was done on two subjects with prior verbal consent (weight 58 ± 2 kg, height 156 ± 4.5cm and age 28 ±1.5 years). They were asked to walk with self-selected normal walking speed, over level ground, stair walking, and sit-stand function. The data were analysed offline. The joints angle range is formulated for sit function for a fixed chair height and also for ascent & descent to the stairs which are shown in table 2, 3 & 4 respectively.

Joints angle are considered to be zero at the human stand position. It is considered positive when a joint moves in anticlockwise direction or goes away and negative when moves in clockwise direction or goes behind.

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FIGURE 2 BLOCK DIAGRAM OF CONTROL

The control generates the gait trajectory with a normal walking speed. It scans the sensor information to know the dynamic position of each joint and generates control output to perform the human activity for a given user command input. The controller generated commands to all six actuators in real time. The output of controller has low current capacity. A Polulo driver was used to amplify the controller low current output signal. This driver has much better feature than switching relay. It has more switching speed, bidirectional control, 5 A to 25 A current outputs, small size, and adjustable pulse width modulation (PWM) frequency from1khz to 22 kHz which can interface with an actuator having a speed of four times higher than a switching relay allow. The control for sitting and standing on a chair was also developed and tested. The control algorithm was developed using NI Lab VIEW 2012, Robotics, FPGA and RT modules.

TABLE 1 JOINTS ANGLE RANGE FOR WALKING

Gait phase position

Hip movement (deg.)

Knee movement (deg.)

Ankle movement

Initial contact

30

0

Loading response

30-35

0 to -15

Neutral (0) 0 to 15PF

Mid stance

35-0

-15 to 0

15PF to 10DF 10DF to 0

Terminal stance

0 to 0

0 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 2 JOINTS ANGLE RANGE FOR SIT DOWN & STAND UP MOTION

Gait phase position

Hip movement (deg.)

Knee movement (deg.)

Ankle movement

Phase 0

0

0

0

Phase 1

45

45

12

Phase 2

90

90

-12

Phase 3

45

45

12

Phase 4

90

90

-12

TABLE 3 JOINT RANGE ANGLES FOR STAIR ASCEND

Gait phase position

Hip movement (deg.)

Knee movement (deg.)

Initial contact

53.5

60.1

6

Loading

43.5

75

15

Ankle movement

Mid Swing

23.5

110

5

Terminal & Pre-swing

-2

135

-25

Initial & mid swing

53

50

5

Terminal swing

48

60

3

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TABLE 4 JOINT RANGE ANGLES FOR HIP, KNEE & ANKLE FOR STAIR DESCEND

Gait phase position

Hip movement (deg.)

Knee movement (deg.)

Ankle movement

Initial contact

4.8

8.3

7.1

Loading response

10

26.4

4.5

Mid stance

13.2

25

5

Terminal stance & pre-swing

20

-60

6

Initial & mid swing

15

30

-10

Results For testing, the developed control algorithm is deployed on the selected RT controller. The GaExoD prototype has been operated for several gait cycles. The trajectory of knee hip and ankle joint were recorded for various cycle in figure 3 and analysed for error estimation shown in figure 4 & 5.

FIGURE 3 HIP, KNEE & ANKLE TRAJECTORY FOR VARIOUS CYCLES

FIGURE 4 TRAJECTORY OF KNEE RECORDED USING GONIOMETER (POT) & ADXL SENSOR

FIGURE 5 TRAJECTORY OF HIP JOINT RECORDED USING GONIOMETER (POT) & ADXL SENSOR

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Hip & knee trajectory recorded using both goniometer & ADXL sensor separately to check the validity of the system shown in figure 4 &. 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. 6 & 7 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.

FIGURE 6 ESTIMATED ABSOLUTE ERROR DURING STAND POSITION

FIGURE 7 ESTIMATED ABSOLUTE ERROR DURING SIT POSITION

Conclusions The real time control algorithm for gait cycle was successfully implemented using RT controller. Developed algorithm uses trajectory estimation approach for control. Algorithms were 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 device is in initial phase of testing and it is tested in laboratory on healthy individuals. Initial results shows, that it is useful for rehabilitation of gait disabled.

Rehabilitation is performed at slow speed as compared to normal walking of the individuals. The controller is able to control the gait phases according to the speed of normal individual making this implementation useful for gait rehabilitation. 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 is in a terminal position. The error can be reduced by using adaptive control approaches in future.

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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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Jhulan Kumar received his BE in Electronics & Instrumentation Engineering from GNIT Kolkata in 2011 and M.Tech in Mechatronics & Robotics from Bengal Engineering & Science University, West Bengal in 2013. He is working as a Senior Project Fellow in the department of Optical Devices and Systems at CSIR-Central Scientific Instruments Organisation, Chandigarh, India. He has authored 3 publications in international & national conferences.

Neelesh Kumar received his BE in Electronics & Telecommunication Engineering from DAVV Indore in 2000; ME in Instrumentation & Control in 2005 from Panjab University and Ph.D. in Engineering & Technology from Panjab University in 2012. He has about 13 years of experience in in Bio-Medical Instrumentation Research and Development. He has worked on project of national importance like LINAC, FES system and Intelligent Prosthetic Device Development. He has 60 publications in Journal and conference proceedings.

Dinesh Pankaj received the B.E. degree in Electronics and Communication from Delhi Institute of Technology, Delhi University, Delhi, in 1991. Since 1995, he has been Scientist at different levels, at Biomedical Instrumentation Division in Central Scientific Instruments Organisation, Chandigarh. His areas of work are electronic system design and motion control systems for biomedical applications. His main achievements involves as a senior team member in development and completion of projects like 4MeV and 6MeV Medical Linear Accelerator for Radiotherapy, Portal Imaging System for Radiotherapy, Sodium-Potassium Analyser, Fully Automatic Clinical Batch Chemistry Analyser etc. He is currently working on the project Automation of Surgical Processes.

Amod Kumar received his B.E. (Hons.) in Electrical and Electronics Engineering from Birla Institute of Technology & Science, Pilani (Raj.) in 1979; M.E. in Electronics from Punjab University, Chandigarh and Ph.D. from IIT Delhi. He has about 30 years’ experience in Research and Development of different instruments in the areas of Process Control, biomedical engineering and prosthetics. He is currently working as chief scientist in Central Scientific Instruments Organisation, Chandigarh. He has 13 publications in reputed journals. He visited Germany under DAAD fellowship in 1987-88. His areas of interest are Microcontroller based design, Digital Signal Processing and Soft Computing.

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