CB - Center for Biomechatronics, ECIJG

CB - Center for Biomechatronics at ECIJGThe 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Robot for Coaching during Gait Training

with Lokomat: Preliminary Experiment with

a Multiple Sclerosis Patient

Nathalia Céspedes Gómez, Jonathan Casas, Betsy Jaramillo, Catalina

Gómez, Marcela Múnera, Carlos Cifuentes.

Email: nathalia.cespedes@mail.escuelaing.edu.co

Center for Biomechatronics, Colombian School of Engineering Julio

Garavito

Rehabilitation Center Mobility

Sabana University Clinic

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Physical Rehabilitation

• “Around 15% of the world population has some disability” (WHO)

• Causes: neurological diseases such as stroke and spinal cord injuries (WHO).

• Physical Rehabilitation (PR) is a continuos process that seeks to improve the quality of

life and self-reliance of patients.

• PR is focused on : physiological aspects and cognitive aspects

• PR use several methods

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“OMS, Atención médica y rehabilitación”, WHO, 2016

W. H. Organization, “full-text,” vol. 4, Rehabil, 2011. O’Sullivan .S et al, [n.d], “Physical Rehabilitation”, 1505 pages

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Physical Rehabilitation with

Lokomat• Lokomat is the gold standar device in

the robot-assited therapy.

• Enables effective and intensive gait

training and ensures the optimal

exploitation of neuroplasticity.

Increase the muscular tone

Balance improvement

Increase motor control and

muscular strength

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B. Husemann et al, “Effects of Locomotion Training With Assistance of a Robot-Driven Gait Orthosis in Hemiparetic Patients After Stroke: A Randomized Controlled Pilot Study,” Stroke, vol. 38, no. 2,

pp. 349–354, Feb. 2007.

G. Colombo, M. Joerg, R. Schreier, and V. Dietz, “Treadmill training of paraplegic patients using a robotic orthosis.,” J. Rehabil. Res. Dev., vol. 37, no. 6, pp. 693–700.

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. Lokomat hocoma, “Relearning to walk from the beginning”, web, https://www.hocoma.com/solutions/lokomat/

Superior to manual therapy

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Schwartz I et al, The Effectiveness of Locomotor Therapy Using Robotic-Assisted Gait Training in Subacute Stroke Patients: A Randomized Controlled Trial. PM&R 2009, 1: 516-523./

CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Limitations during Phyisical

Rehabilitation

Lack of adherence of the patients to the

programs .

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2 W. H. Organization, “full-text,” vol. 4, Rehabil, 2011.

K. Jack, S. M. McLean, J. K. Moffett, and E. Gardiner, “Barriers to treatment adherence in physiotherapy outpatient clinics: A systematic review,” Man. Ther., vol. 15,

no. 3, pp. 220–228, 2010.

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Economical, social Factors.

Anxiety, depression.

Low level of physical activity or

aerobic capacity, fatigue

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Limitations during Phyisical

Rehabilitation with Lokomat

•Multiple tasks performed by

the therapists during a session.

Examples: Simultaneous

measurments of gait patterns:

ankle kinematics and spinal

posture

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9 Heather E. Douglas, Magdalena Z. Raban, Scott R. Walter, and Johanna I. Westbrook. 2015. Improving our undersatanding of multi-tasking in health

care: Drawing together the cognitive pychology and healthcare literature. Alpplied Ergonomics 59 (2017), 45-55.

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Social Assitive Robotics

In this context, Socially Assitive

Robotics (SAR) could be use as a

potential tool to improve physical

rehabilitation with Lokomat and to

cooperate with thrapists to control

patient’s performance.

• Patien’s positive response in achieving different goals.

• Improvement of the movement’s technical tasks during

upper limb excersises

• Decrease the level of stress

• Usefull tool to engage the patients to excersise.

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Maja J Matáric et al. Socially assistive robotics for post-stroke rehabilitation. Journal of NeuroEngineering and Rehabilitation 4, (2017),5.Hee-Tae Jung et al. Upper limb ecersises for post stroke patients through the direct engagement of an embodied agent. Proceedings of the 6th international conference- HRI. (2011).157

Juan Fasola and Maja J Matáric. Using socially assistive human-robot interaction to motivate physical exercise for older adults. Proc IEEE 100, 8, (2012).

Saito, T., T. Shibata, K. Wada, and K. Tanie, Relationship between interaction with the mental commit robot and change of stress reaction of the elderly. Computational Intelligence in Robotics and

Automation, 2003. Proceedings. 2003 IEEE International Symposium on, 2003. 1.

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Human–Robot Interface Development

• Structure based on:

• Physical parameters : Heart rate

Cervical and thoracic posture.

• Cognitive parameters: Motivational feedback, fatigue perception (Borg Scale).

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Freedson. S and Miller .K, Objective Monitoring of Physical Activity Using MotionSensors and Heart Rate, (2015).Oulette. M et al, High-Intensity Resistance Training Improves Muscle Strength, Self-Reported Function, and Disability in Long-Term Stroke Survivors, (2004)

Lunenburguer,Clinical assessment performed during robotic rehabilitation by the gait training with Lokomat, (2005). Borg, G. (1998). Borg's perceived exertion and pain scales. Champaign, IL, US

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Human-Robot interface

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Robot Behavior

Behaviors When? Rutine

Cervical Posture

Feedback

Bad Posture

(10°-15° over 0°)

“your head is tilted this

way, please correct it”

Thoracic posture

Feedback

Bad Posture

(10°-15° over 0°)

“Straighten your back”

Heart Rate alert HR >(206.9-

(0.67*age))

“Therapist, your

patient has a elevated

heart rate”

Borg scale alert BS>15 “Are you tired”

Motivational

Feedback

Good posture

Randomly

“You are doing great”

“You can do it”

Table 1. Robot Behaviors during a therapy

CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Preliminar Study Design

• A male patient was randomly

chosen (Height : 1.83 m, Weight:

60 Kg, Age : 62 years).

• Diagnosis: Multiple Sclerosis

• Lokomat features :

• Speed: 1.5 m/s

• 29.2% of body weight support

• Therapy Time : 30 min

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Results

Figure 1. Cervical posture registered by pitch, yaw and roll angles during 30 min

of Lokomat session

Start

“your head is tilted this way, please correct it”“Congratulations!, You are doing well”“You can do it !”

End

Random motivational

Feedback

Posture Feedback

Motivational Feedback

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Results

Figure 2. Thoracic posture registered by pitch, yaw and roll angles during 30 min

of Lokomat session

Start

“Straighten your back”“Congratulations!, You are doing well”

“You can do it !”

Posture Feedback

Motivational Feedback

Random motivational

Feedback

End

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Results

Figure 3. Borg scale and heart rate during Lokomat

session

Start

Cool Down Phase

“According to the Borg

scale, how tired are you?”

“10”

Borg Scale Request

Manual Borg scale

register

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Results

Figure 4. Main Events during 30 min of Lokomat

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Conclusions

• The functionality and the usability of the system for this therapy was

appropriate, showing reliable measurements

• The robot gives different feedback corresponding to the variables and motivate

the patient with randomly verbal phrases, allowing the interaction with the

patient.

• This study shows initially the potential of SAR in physical rehabilitation with

Lokomat for coaching in terms of support the patient and accompany the

therapist’s task.

• Regarding the observations made in the preliminary pilot study, patients have

a well-received behavior and a positive impact to SAR.

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CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.14

CB - Center for Biomechatronics, ECIJG

The 13th Annual ACM/IEEE International Conference on Human Robot Interaction

Chicago, IL, USA from March 5–8, 2018.

Current Work

Patient Bad Cervical

Posture

Control session

(Time)

Bad Thoracic

Posture

Control session

(Time)

Heart

Rate

Mean

Control session

(bpm)

Bad Cervical

Posture

Robot session

(Time)

Bad Thoracic

Posture

Robot session

(Time)

Heart

Rate

Mean

Robot session

(bpm)

Patient 1 17.2 min 5.07 min 92.01 bpm 9.04 min 2.4 min 92.2 bpm

Patient 2 7.63 min 6.84 min 80.4 bpm 6.04 min 1.5 min 95.2 bpm

Patient 3 18 min 9.3 min 82.3 bpm 3.5 min 1 min 85.04 bpm

Patient 4 9.9 min 1.2 min 96.4 2.32 min 0.9 min 98.4 bpm

Table 2. Initially results with 4 patients during two lokomat sessions