Since the industrial revolution, modern technologies have progressively provided man with more comfort, better health, and higher quality of life. The evidence is clear. Man’s life expectancy has more than doubled during the past few centuries. But new challenges have also risen, from cancer and brain diseases to movement disabilities due to aging populations, inactive lifestyles, and polluted environments. These challenges also come with the expectation of higher performance and more efficiency in operations more closely intertwined with man. In this talk, we will consider the above aspects within the context of robotic rehabilitation and artificial intelligence. In particular, I hope to present some of our soft computing strategies and how the uncertainties and complexities of the rehabilitation process necessitate them.

1. On the Uncertainties and Complexities of
Robotic Rehabilitation
Towards AI technologies for the better good
Mohammad-R. Akbarzadeh-T.
Department of Electrical and Computer Engineering,
Center of Excellence on Soft Computing and Intelligent Information Processing,
Ferdowsi University of Mashhad, Mashhad, Iran
akbazar@um.ac.ir

2. SCIIP - Our center of excellence at FUM

3. Our challenge to you: Identify a national/local crisis
suitable for AI solutions

4. Centers of Excellences
• Serve as a National Reference in
– Producing State of the Art Research
– Solving Critical Issues at National Level
– Promoting/Facilitating Research for the Nation
– Translating Knowledge to Products/Solutions -> Wealth
– Educating the Public
– Building Coalitions - a National/International Network
of Experts
• CoEs are not Research Centers/Laboratories
• Soft Computing as a Strategic Technology
– Appears in ‫کشور‬ ‫علمی‬ ‫انداز‬ ‫چشم‬ ‫سند‬
– Multidisciplinary Field (Cognitive Sciences, Engineering and
Information Technology, Basic Sciences, Medicine,
Humanities…)

5. Centers of Excellences in Iran (153)
Tehran (108), Isfahan (14), Shiraz (9), Mashhad (6),
Tabriz (6), Ahvaz (4),
Kermanshah (2), Others (1)

6. Engineering CoE in Iran (62)
Tehran (56), Others (8- 1 each)

7. Intelligent-related CoE in Iran (4)
Tehran (2-Control and Mechanical Engineering), Yazd
(1-Civil), Mashhad (1-Information)

8. Outline
• Solving the right problems
• Solving problems traditionally, a control systems
perspective
• The intelligent control
• Robotic rehabilitation, as case studies
• Solving the right problems, the keys to success

9. Solving the right problems:
The Pearls of Human Progress
• The world has never
been richer,
• Humans have never lived
longer,
• Literacy has never been
so universal.
• We are more productive
and healthier than ever.

10. Solving the right problems:
The perils: then why do we seem so troubled?
• Increasing and aging world
population (i.e. increasing
ratio of retirees to workers)
• According to WHO, 16% of
this population have
significant disabilities.
• This ratio is expected to rise
further due to the unhealthy
behaviors in the more dormant
and urban modern lifestyle
and aging populations.
Besides the growing aging population, this also means fewer youth available to serve
the elderly: Robotic Rehabilitation is a Necessity, not a Luxury.
Noncommunicable diseases (NCDs), including heart disease, stroke, cancer, diabetes and chronic lung disease, are collectively responsible for 74% of all deaths worldwide.

11. Outline
• Solving the right problems
• Solving problems traditionally, a control systems
perspective
• The intelligent control
• Robotic rehabilitation, as case studies
• Solving the right problems, the keys to success

12. Solving problems traditionally, a
control systems perspective
Inputs Outputs
A dynamical system
We have two general goals:
•To estimate/predict the outputs of given system, by observing its current
and past inputs and outputs. (Modeling: Communication Systems, Time
Series Prediction Problems)
•To change outputs of a given system towards desired path. (Control:
Robotics)
•To change the outputs of a given system based on estimated states/outputs
(Observer-based control)

13. The problem is
Inputs Outputs
A dynamical system
The problem is:
Either the model is too complicated, unknown, and there are
disturbances and measurement noise.

14. Introducing Feedback
Controller
Observer
Actuator Process S
Disturbance Measurement
Noise
Output
Input
•Feedback moves the output towards the desired by
monitoring the output and making adjustments at the input.
•Helps keep internal stability despite uncertainties and noise.

15. Uncertainty vs. Certainty: A History of Struggle in Control
Open
Loop
Systems
Closed
Loop
Systems
James
Watts:
First
Industrial
Feedback
Controller
for
Steam
Engine
Robust/Multivariable
Control
Optimal/adaptive
Control Nonlinear
Control
More realistic designs towards more practical Value/Need/Modern Applications
Reliable Results/Rigorous Analysis
Hybrid Systems
Impulsive Systems
…
Discrete Event Systems
Increasing Complexity and Ambiguity
Distributed and Multiagent
Systems

16. Uncertainty vs. Certainty: A History of Struggle in Control
Open
Loop
Systems
Closed
Loop
Systems
James
Watts:
First
Industrial
Feedback
Controller
for
Steam
Engine
Robust/Multivariable
Control
Optimal/adaptive
Control Nonlinear
Control
More realistic designs towards more practical Value/Need/Modern Applications
Reliable Results/Rigorous Analysis
Hybrid Systems
Impulsive Systems
…
Discrete Event Systems
Increasing Complexity and Ambiguity
Distributed and Multiagent
Systems
?

17. How do some control engineers treat
COMPLEX Systems?
1. Decomposition – Assumption 1
It is easier to work with less complex systems.
2. Slowly varying variables -Assumption 2
It is easier to replace variables by constants.
3. Linearization – Assumption 3
It is always easier to work with linear systems.
4. Controllable & Observable System -- Assumption 4
It is necessary to do the design.
5. Delays are assumed to be non-existent x(t-t) =x(t) -- Assumption 5
1 2 3
S N
...
t
x
t
x .

18. Outline
• Solving the right problems
• Solving problems traditionally, a control systems
perspective
• The intelligent control
• Robotic rehabilitation, as case studies
• Solving the right problems, the keys to success

19. Open
Loop
Systems
Closed
Loop
Systems
James
Watts:
First
Industrial
Feedback
Controller
for
Steam
Engine
Robust/Multivariable
Control
Optimal/adaptive
Control Nonlinear
Control
Practical Value/Need/Modern Applications
Reliable Results/Rigorous Analysis
Hybrid Systems
Impulsive Systems
…
Discrete Event Systems
Increasing Complexity and Ambiguity
Intelligent
Control
Distributed and Multiagent
Systems
The Intelligent Control

20. Open
Loop
Systems
Closed
Loop
Systems
James
Watts:
First
Industrial
Feedback
Controller
for
Steam
Engine
Robust/Multivariable
Control
Optimal/adaptive
Control Nonlinear
Control
Practical Value/Need/Modern Applications
Reliable Results/Rigorous Analysis
Hybrid Systems
Impulsive Systems
…
Discrete Event Systems
Increasing Complexity and Ambiguity
Intelligent
Control
Socially
Intelligent
Control
Distributed and Multiagent
Systems
The Intelligent Control

21. In other words, with a realistic view,
• Seldom: Both the governing dynamical laws and parameters
are known
– Such as in robot modeling by following the natural laws of physics and where
the parameters can be measured. (White Box)
But even so, they could be mathematically (for analytical work) and numerically
(for real-time work) too complicated.
• Often, either:
– The governing dynamical laws are known, but the parameters are not
(Such as in robot modeling where friction must be estimated. (tools of
identification) (Grey)
– There is no/only partial knowledge on the governing laws and
parameters (Such as in weather forecasting, financial markets, and when
involving human in the loop. (Data-driven techniques, Darker Grey to Black)
But what if the desired is not known?

22. But what if the desired is not known or
is ill-defined?
• This is an uprising problem.
• In human centered systems, the
desired output is itself uncertain.
• For illustration, here we consider
the case study of rehabilitative
robotics.
Control Sensing
Manipulation
Thinking
Control Sensing
Manipulation

23. Process
Inputs Outputs
Modeling and Prediction
Let’s visit our familiar feedback system

24. Process
Inputs Outputs
Control
Learning, Optimization, Robustness
Desired
Error
Error
Controller

25. Process
Inputs Outputs Desired
Error
Error
Controller
Control
Learning, Optimization, Robustness

26. Process
Inputs Outputs Desired
Error
Error
Controller
Control
Learning, Optimization, Robustness

27. Process
Inputs Outputs Desired
Error
Error
Controller
Control
Learning, Optimization, Robustness

28. Process
Inputs Outputs Desired
Error
Error
Controller
Control
Learning, Optimization, Robustness

29. Process
Inputs Outputs Desired
Error
Error
Controller
Control
Learning, Optimization, Robustness

30. Process
Inputs Outputs Desired
Error
Error
Controller
Control
Learning, Optimization, Robustness

31. Process
Inputs Outputs Desired
Error
Error
Controller
Control
Learning, Optimization, Robustness

32. Process
Inputs Outputs Desired
Error
Error
Controller
What is
measurable?
What is the true
objective?
How do we
aggregate the
two
to measure
performance?
What to do?
How to
process
information
to the
human user?
Control
Learning, Optimization, Robustness
Lee Majors

33. Outline
• Solving the right problems
• Solving problems traditionally, a control systems
perspective
• The intelligent control
• Robotic rehabilitation, as case studies
• Solving the right problems, the keys to success

34. The Robotic rehabilitation problem
• Exoskeletons
Physiotherapy of stroke
patients
• Bionic Hands for the
disabled
• Games for Joint Therapy
of Hemophiliac Children

35. The Robotic rehabilitation problem
• Exoskeletons
Physiotherapy of stroke
patients
• Bionic Hands for the
disabled
• Games for Joint Therapy
of Hemophiliac Children

36. The Robotic rehabilitation problem
• Exoskeletons
Physiotherapy of stroke
patients
• Bionic Hands for the
disabled
• Games for Joint Therapy
of Hemophiliac Children

37. FUM’s Exoskeleton
With great thanks to our robotic team at the FUM CARE (Center of Advanced Rehabilitation and
Robotics Research).
3*2 Load
Cells
5*2 Foot
Sensors
3*2*2
Motor
Encoders
2*2*16
EMGs
5 IMUs

38. The deep neural network to estimate human joint angles

40. Exoskeleton Results
Ali Foroutannia, M.-R. Akbarzadeh-T., A. Akbarzadeh, and M. Tahamipour, Adaptive Fuzzy Impedance Control of
Exoskeleton Robots with Electromyography based Convolutional Neural Networks for Human Intended Trajectory
Estimation, Journal of Mechatronics, 2023.

41. 2 Load
Cells
5*2 Foot
Sensors
2*2 Motor
Encoders
FUM’s Hip Exoskeleton (HEXA)
2 EMGs
5 IMUs

45. HEXA Results
Ali Foroutannia, M.-R. Akbarzadeh-T., and A. Akbarzadeh, A deep learning strategy for EMG-based joint position
prediction in hip exoskeleton assistive robots, Journal of Biomedical Signal Processing and Control, 2022.

46. Bionic Hands:
• Intuitive motion should be fast and
accurate.
• It begins with a proper signal along
with intelligent processing.
• Current signaling technologies are
1. EEG: Highly inaccurate and noisy, what about
loose probes
2. Probe embedded EEG: Too invasive
3. EMG: More accurate, but
– Amputated hands lose range of movement and
so less muscle activity
– Loose probes still persist.
4. Embedded magnets: Kineticomyography
– No probes, accurate and fast (+++),
– Needs surgery for implantation (-)
2
1
3
4

47. Tracking Magnet
Embedded
A. Moradi, et. al, Clinical Implementation of a Bionic Hand Controlled with Kineticomyographic Signals,
Scientific Report, Nature Publishing, 2022.

48. FUM Bionic Hand
A. Moradi, et. al, Clinical Implementation of a Bionic Hand Controlled with Kineticomyographic Signals,
Scientific Report, Nature Publishing, 2022.

49. A. Moradi, et. al, Clinical Implementation of a Bionic Hand Controlled with Kineticomyographic Signals,
Scientific Report, Nature Publishing, 2022.

50. Virtual fist and ball game to keep
exercising the muscles
A. Moradi, et. al, Clinical Implementation of a Bionic Hand Controlled with Kineticomyographic Signals,
Scientific Report, Nature Publishing, 2022.

51. Numerical results
A. Moradi, et. al, Clinical Implementation of a Bionic Hand Controlled with Kineticomyographic Signals,
Scientific Report, Nature Publishing, 2022.

52. Learning Games for Physiotherapy of
Hemophiliac Children
• Ankle
• Knee
• Elbow

53. Learning Games for Physiotherapy of
Hemophiliac Children – H. Jabarouti
H. Jabarouti, Intelligent and adaptive control of rehabilitation by a graphical game, M.S. Thesis, Ferdowsi
University of Mashhad, 2018.

55. Outline
• Solving the right problems
• Solving problems traditionally, a control systems
perspective
• The intelligent control
• Robotic rehabilitation, as case studies
• Solving the right problems, the keys to success

56. Lessons Learned and Future to Come
• Fast:
– Better processors
– Fewer computation
• Accurate:
– Better uncertainty
paradigms
– Information fusion and
data stratification
– Learning and
optimization

57. Process
Inputs Outputs Desired
Error
Error
Controller
What is
measurable?
What is the true
objective?
How do we
aggregate the
two
to measure
performance?
What to do?
How to
process
information
to the
human user?
Control
Learning, Optimization, Robustness
Lee Majors
Lessons Learned and Future to Come

58. Process
Inputs Outputs Desired
Error
Error
Controller
What is
measurable?
What is the true
objective?
How do we
aggregate the
two
to measure
performance?
What to do?
How to
process
information
to the
human user?
Control
Learning, Optimization, Robustness
Solving the right problems:
Lessons Learned and Future to Come

59. • Fast:
– Better processors
– Fewer computation
• Accurate:
– Better uncertainty
paradigms
– Information fusion and data
stratification
– Learning and optimization
The usual is:
Solving the right problems:
Lessons Learned and Future to Come

60. • Fast:
– Better processors
– Fewer computation
• Accurate:
– Better uncertainty
paradigms
– Information fusion and data
stratification
– Learning and optimization
• But accuracy and
speed must be
redefined –
transparent and
rehabilitative
engagement is
desired, i.e. the
human factor!
The usual is:
Solving the right problems:
Lessons Learned and Future to Come

61. • Easy to use:
– Easy and lightweight
technology
• Proper sensing
technologies
• Human Factor:
– Performance rather than
accuracy
• Rehabilitation is the ultimate
goal
• Intuitive and fast motion
• Keep it interesting and engaging
– Games and Cognitive Science
• Fast:
– Better processors
– Fewer computation
• Accurate:
– Better uncertainty
paradigms
– Information fusion and data
stratification
– Learning and optimization
• Low cost solutions and
Mass customization
Solving the right problems:
Lessons Learned and Future to Come

62. Announcing
…
Journal of Intelligent and Cognitive Computing
in collaboration with ISSSI and KGUT
…
and
…
AI for Good Award
in the memory of CCI2020

63. Hope you can visit us in Mashhad
The first snow in Mashhad, 14th of Azar 1401