In many countries of the world, the life expectancy increases but the population ages so rapidly that it is expected that soon it will be difficult to ensure a good life quality to the elder people when health issues arise. In this paper, we consider this problem from the point of view of the physiotherapy rehabili- tation which nowadays is perceived as costly and inconvenient for the elder patients. In order to lessen these problems, we propose a distributed architecture to allow the physiotherapists to remotely assist their patients while they comfortably do exercises from home. As in other proposals, the Human Pose Recognition is delegated to a computer equipped with MS Kinect and neural networks. Our approach, however, differs from others because it includes a logical framework based on Event Calculus augmented with Expectations which provides a higher-level description of the exercises and a mean to measure how well they were done.

1. A Distributed System
Using MS Kinect and Event Calculus
for Adaptive Physiotherapist Rehabilitation
Stefano Bragaglia
University of Bristol
Stefano Di Monte
University of Bologna
IIBM 2014
Birmingham, 2 July 2014
Paola Mello
University of Bologna

2. Introduction
• Population ageing fast: fewer young people to
support the elderly
• Life expectancy increases, as well as health
issues in older age
• Proven correlation between improperly
treated issues and consequent more serious
health problems
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3. Use Case Scenario
• Physiotherapy rehabilitation for elder people
– Hospitals and LHUs can be source of anxiety
– Very unbalanced patients/doctor ratio
– Exercises: generally low chances of a proper cure
• Frequent visits to costly physiotherapy centres
• Many causes lead to desist from a proper cure…
• or insist with an improper cure!
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4. Our proposal
• Use non-invasive technology to virtually bring
the physiotherapist in the patient’s house
• Distributed system
– LHU’s data server
– Physiotherapist’s application
– Patient’s device
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5. Computer Vision
• Human pose prediction with MS Kinect
– 6 networks, one per limb/body part with Weka
• Multi-Layer Perceptrons *
• Decision Trees
• Logistic Model Trees
• Support Vector Machines *
– Input: the coordinates of the appropriate joints
– Output: a selection of partial frontal poses
– For each frame and network:
the ID of the predicted pose and its likelihood
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6. An Overlying Logic Framework
• Provides
– a convenient high-level way to describe exercises
– a low-level operational way to review them
• Based on
– Event Calculus: streamlined and resilient formalism to
reason about actions and their effects on a domain
– Expectations: formalism to declaratively describe
expected and/or undesired workflows within a
domain
• Implemented as forward rules with Drools
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Example
• Example: sit-to-stand
– Event: FrameAcquired (parametric)
– Fluents: leftLegPose, leftLegPoseScore, …
– Workflow: sit, stand, compute score
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Example: Sit-to-Stand(1/4)
on FrameAcquired( ... ,
leftLegPose, leftLegPoseScore,
rightLegPose, rightLegPoseScore, ... ) ... ,
set LeftLegPose to leftLegPose,
set LeftLegPoseScore to leftLegPoseScore, ... .
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9. 1001 0101 0111 1001 1001 0101 0111 1001 1001 0101 0111 1001 1001 0101 0111 1001
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Example: Sit-to-Stand(2/4)
on FrameAcquired
if SittingScore < 0
expect LeftLegPose == 1 and RightLegPose == 1
when fulfilled
set SittingScore
to LeftLegPoseScore * RightLegPoseScore
when violated
set SittingScore
to min(LeftLegPoseScore, RightLegPoseScore).
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10. 1001 0101 0111 1001 1001 0101 0111 1001 1001 0101 0111 1001 1001 0101 0111 1001
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Example: Sit-to-Stand(1/4)
on FrameAcquired
if SittingScore >= 0 and StandingScore < 0
expect LeftLegPose == 0 and RightLegPose == 0
before 2s
when fulfilled
set StandingScore
to LeftLegPoseScore * RightLegPoseScore
when violated
set StandingScore
to min(LeftLegPoseScore, RightLegPoseScore).
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11. 1001 0101 0111 1001 1001 0101 0111 1001 1001 0101 0111 1001 1001 0101 0111 1001
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Example: Sit-to-Stand(4/4)
on FrameAcquired
if SittingScore >= 0 and StandingScore >= 0
and ExerciseScore < 0
set ExerciseScore
to SittingScore * StandingScore.
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12. Conclusions
• Contributions
– Distributed architecture to assist elder people in
physiotherapy rehabilitation
– Combines CV techniques with a powerful logic
framework
– Proof-of-concept, not trained with proper data
• Future works
– With the approval of an ethical committee,
creation of a postural dataset for data mining
2 July 2014 IIBM 2014 12