with Lokomat: Preliminary Experiment with a Multiple Sclerosis - - PowerPoint PPT Presentation

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with Lokomat: Preliminary Experiment with a Multiple Sclerosis - - PowerPoint PPT Presentation

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CB - Center for Biomechatronics, ECIJG Robot for Coaching during Gait Training with Lokomat: Preliminary Experiment with a Multiple Sclerosis Patient Nathalia Cspedes Gmez, Jonathan Casas, Betsy Jaramillo, Catalina Gmez, Marcela Mnera,

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CB - Center for Biomechatronics, ECIJG CB - Center for Biomechatronics at ECIJG

The 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

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

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

PR) is a continuos process that seeks to imp improve rove the the qu quality lity of

  • f

life fe and self lf-relianc eliance of patients.

  • PR is focused on : physio

iologica gical aspects and cognitiv nitive aspects

  • PR use several methods

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1 1 2 3 1 2 3 “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

  • ptimal

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

4 7 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./

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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

Lack of adherence of the patients to the programs .

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8 8 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

  • f

physical activity

  • r

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

  • f

gait patterns: ankle kinematics and spinal posture

9 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

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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.

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

  • f 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

  • f 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.

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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.

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