=Paper=
{{Paper
|id=Vol-2404/paper02
|storemode=property
|title=Towards a Logic-Based Approach for Multi-Modal Fusion and Decision Making During Motor Rehabilitation Sessions
|pdfUrl=https://ceur-ws.org/Vol-2404/paper02.pdf
|volume=Vol-2404
|authors=Fabio Aurelio D’Asaro,Antonio Origlia,Silvia Rossi
|dblpUrl=https://dblp.org/rec/conf/woa/DAsaroOR19
}}
==Towards a Logic-Based Approach for Multi-Modal Fusion and Decision Making During Motor Rehabilitation Sessions==
https://ceur-ws.org/Vol-2404/paper02.pdf
Workshop "From Objects to Agents" (WOA 2019)
Towards a Logic-Based Approach for Multi-Modal
Fusion and Decision Making during
Motor Rehabilitation Sessions
Fabio Aurelio D’Asaro Antonio Origlia Silvia Rossi
CRDC Tecnologie URBANECO DIETI
University of Naples Federico II University of Naples Federico II University of Naples Federico II
Napoli, Italy Napoli, Italy Napoli, Italy
antonio.origlia@unina.it silvia.rossi@unina.it
Abstract—We introduce a general approach which aims at performed. The resulting user model can then be used e.g. to
combining machine learning and logic-based techniques in order monitor the user’s performance and decide what is the most
to model its user’s cognitive and motor abilities. In the context appropriate rehabilitation strategy.
of motor rehabilitation, hybrid systems are a convenient option
as they allow both for the representation of formal constraints However, handling data coming from different sources
needed to implement a clinically valid exercise, and for the requires a complex system able to integrate them and take
statistical modelling of intrinsically noisy data sources. Moreover, decisions accordingly, i.e. a multimodal system [4]. Moreover,
logic-based systems offer a transparent way to look at the in existing rehabilitation games, the patient motivational state
decisions taken by an automated system. This is particularly has been considered to evaluate the game effectiveness [5],
useful when an AI system needs to interact with a therapist in
order to assist therapeutic intervention, e.g. by explaining why without providing the possibility of taking decisions with
a given decision is sound. This methodology is currently being respect to the online adaptation of the rehabilitation exercise.
developed within the context of the AVATEA project. Alongside machine learning techniques, we also intend to
Index Terms—Multimodal Fusion, Epistemic Probabilistic employ a logic-based system. Indeed, in a complex domain
Event Calculus, Motor Rehabilitation such as that of AVATEA, much of the experts’ knowledge
would have to be re-learnt from scratch (thus requiring a
I. INTRODUCTION considerable amount of data) if we were to use machine learn-
In this work, we introduce some of the ongoing activities in ing techniques exclusively. Logic-based systems, on the other
the context of the AVATEA project (Advanced Virtual Adap- hand, are progressively becoming able to handle uncertain
tive Technologies e-hEAlth). The project aims at developing knowledge (e.g., using Probability Theory or Fuzzy Logic).
an intelligent system to support the rehabilitation process This provides the opportunity to retain important parts of this
of children with neuro-motor disorders. More specifically, knowledge even when it comes with a degree of uncertainty.
AVATEA aims at creating an integrated system consisting of: Moreover, logic-based systems offer a transparent way to look
(i) an adjustable seat, (ii) different types of sensors, and (iii) an at the information in AI systems. For example, a therapist
interactive visual interface to perform rehabilitation exercises might want not only to see what decisions were taken by the
in the form of games. Such games/exercises are going to system but also why they were taken. A logic-based system
be specifically targeted at supporting therapeutic sessions for is able to reconstruct the rationale behind the decisions taken
Development Coordination Disorders (DCD). by considering the chain of rules that were applied starting
Although a significant amount of work has been done in from the facts in the knowledge base. Such an advantage does
the general area of motor rehabilitation with promising results not also apply to machine learning algorithms, that generally
[1], there is still a need for developing personalised therapeutic cannot provide explanations in human-readable terms. It is also
scenarios. Adaptation techniques typically only focus on max- worth noting that these systems can be used by an expert to
imising effort during the rehabilitation session [2]. However, sketch the causal relationships of a domain, and then use other
it is also necessary to take into account parameters such as the techniques to learn the appropriate parameters when they are
individual subject’s capabilities [3] and the child’s emotional not available to the expert.
response, e.g. in terms of motivation and engagement [2].
In this direction, AVATEA aims to assist the activity of a II. BACKGROUND AND RELATED WORK
therapist through the use of data acquired from its sensors. Gamification strategies have proven to be extremely suc-
Machine learning techniques can process this data to profile cessful to engage young children in diagnostic or therapeu-
the user’s motor abilities, his/her psychophysiological state, tic exercises, even before the advent of digital gaming. By
and to monitor the child’s response to the exercise being leveraging on Self-Determination Theory [6], the concept of
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Workshop "From Objects to Agents" (WOA 2019)
intrinsic motivation has been applied to engage children in and ProbLog [21] to perform event recognition from security
activities designed to provide therapists with reports about cameras. In the proposed case study, the logical part of
their competence levels in either cognitive or physical tasks. the architecture receives time-stamped events as inputs and
While games to test cognitive capabilities (see e.g. [7]) do processes them in order to detect complex long-term activities
not form a sharply defined class, games designed to test and (e.g., detect that two people are fighting from the fact that they
improve motor skills are usually referred to as exergames. have been close to each other and moving abruptly during the
The effects of exergames have been found to be generally last few seconds). Given their semi-probabilistic nature, these
positive [8]. Therefore, combining Computerised Adaptive frameworks are able to handle uncertainty in the input events
Tests (CATs) with gamification strategies results in systems (ProbEC) or in the causal rules linking events and fluents
that are able to engage young users in playful activities, (MLN-EC). We envisage that similar systems, especially the
while adapting the current challenge according to level of Event Calculus-based ones, could be employed as a way to
user competence. Furthermore, sessions are typically logged perform fusion between different modalities.
in order to provide detailed feedback to therapists. On the
cognitive side, these adaptive systems have been designed to III. THE AVATEA ARCHITECTURE
evaluate subjective well-being [9] and phonological acquisition The proposed architecture is essentially a multimodal sys-
[10] among others. Adaptive exergames have been used e.g. tem. These were first formally defined in [4] as systems that
in the context of children with spinal impairments [11], “[. . . ] process two or more combined user input modes such as
and to test gross motor skills [12]. These works, however, speech, pen, touch, manual gestures, gaze, and head and body
typically use a single modality to implement CATs and do not movements in a coordinated manner with multimedia system
consider social feedback as a part of the adaptation process to output”. The possibility to handle multiple communication
recover engagement. Considering the challenge posed both by channels is expected to simplify interaction with the user and
multimodal fusion and by adaptation strategies, an integrated to result in a more natural way to control an automated system.
system for both fusion and decision making represents an Available modalities may be used in an exclusive or concurrent
ambitious goal, with potentially broad impact on the field way, with no integration between them [22]. However, it is
of adaptive diagnosis and treatment of both cognitive and more often the case that a multimodal system processes mul-
physical impairments. tiple channels in a parallel and integrated way [23]. Moreover,
Moreover, in the specific case of CATs, the domain knowl- whether it is more advantageous to adopt an early or late
edge is known to the developers, as the experimental procedure model for data fusion strongly depends on the amount of
must remain safe and informative. It is necessary to keep available knowledge about the domain. From a system design
records of the decisions taken by the system, in order to perspective, it is better to develop separate, more specialised,
reconstruct and explain how the session was managed by the approaches to analyse each single data source and then fuse the
system. In this specific situation, statistical modelling alone is results using a subsequent layer. However, when the domain
not advantageous because: (i) it would require a lot of data knowledge is limited, key interactions among input modalities
to discover elements that are already known, and (ii) it would may be overlooked: in this case, early fusion is more adequate.
make it difficult to provide human-readable feedback to the Generally, the problem of deciding when to apply fusion is one
therapists. of the main issues when designing multimodal systems (see
In this context, hybrid systems are a convenient option as e.g. [24]). In our domain, the amount of available knowledge
they allow both for the representation of formal constraints about training exercises supports the adoption of a late fusion
needed to implement a clinically valid exercise, and for the approach.
statistical modelling of intrinsically noisy data sources. Re- Figure 1 shows the envisaged architecture for the AVATEA
cently, logic-based approaches have been successfully applied project which main modules we will discuss in the following
to several fields of Artificial Intelligence, including (but not paragraphs.
limited to): event recognition from security cameras [13], [14],
robot location estimation [15], understanding of tenses [16] A. Sensors
and natural language processing [17]. Due to the increasing We are going to use different types of sensors, including:
relevance of Machine Learning and Probability Theory in AI, (i) pressure sensors, (ii) 2D and 3D cameras, (iii) motion
these frameworks and languages have gradually started em- detectors, and (iv) an EEG sensor. Pressure sensors, motion
ploying probabilistic semantics (see e.g. [18]) to incorporate detectors and cameras are going to be used to make sure that
and deal with uncertainty. This has given birth to the field of exercises are being executed correctly by detecting front and
Probabilistic Logic Programming (see e.g. [19]). For example back posture, head-pose, movement speed, balance and feet
[15], which is based on the Situation Calculus ontology, can relative position. In some cases, the cameras may be used
model imperfect sensors and effectors. The Situation Calculus’ to let the user interact with the system, e.g. by pointing at
branching structure makes these frameworks mostly suitable the screen. Moreover, data from cameras and EEG data are
for planning under partial states of information. On the other going to help checking the user’s current level of stress and
hand, MLN-EC and ProbEC [13], [14] extend the semantics engagement and e.g. decide whether the difficulty level of the
of the Event Calculus using Markov Logic Networks [20] exercise should be changed.
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Workshop "From Objects to Agents" (WOA 2019)
Fig. 1. The AVATEA architecture
B. Input Trackers during the exercise, as well as his/her emotional state and
engagement. The Modalities Recognisers classify the features
Adaptive games require the ability to dynamically track
extracted from the sensor data, and create a list of possible
children movements. For example, movements must be taken
interpretations (N-Hypothesis) for the AI engine.
into account if we want the system to automatically adapt
the game speed to the children’ physical capabilities. To this
D. User Models
aim, pressure sensors, head pose and skeleton data from video
images will be processed and then given on input to the game. We will use personalised machine learning techniques to
Particular emphasis will be put on tracking children’ posture: learn a model of the children abilities and interaction prefer-
for instance, the head-pose will be a triggering event for some ences [27]. In turn, these profiles will make the system able to
games. We are going to employ one of the several available recognise anomalies with respect to such model [28] so that it
skeleton detection algorithms. Existing methods include those is possible to track improvements in the user’s performance.
using R-GBD or 2D cameras (e.g. the OpenPose library [25]) Moreover, user’s performance over time will be correlated with
which can identify various positions, even those of ambigu- how they felt about the exercise (or similar exercises) in the
ous interpretation (sitting, three-quarter backward perspective, past. This can be used to create a personalised exercise plan
etc..). (e.g., exercise type, modality of execution of the exercise, etc).
C. Classifiers E. AI Engine
In AVATEA, the focus is on human activities. These are The system we envisage implies the use of noisy data
very difficult to classify due to the diversity of individual con- sources coming from multiple sensors and trained classifiers.
ditions. Leveraging on the expressive power of deep networks These drive a decisions layer conducting adaptive rehabili-
as feature extractors, and by exploiting features modelling tation exercises. If, on one hand, this makes it necessary to
techniques of the human body, we will research and design handle classification estimates with probabilistic reasoning,
novel algorithms for Social Signal Processing [26]. Video and on the other hand, one needs to keep a rule-based structure
EEG data will be used to monitor the attention of the children to ensure clinical effectiveness and human-readable session
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Workshop "From Objects to Agents" (WOA 2019)
summarisation. Hybrid systems, typically consisting of prob- • the o-proposition
abilistic rules, combine the best of the two approaches by
A occurs-at I with-prob P if-holds θ
allowing the definition of strict rules. These rules can be used
to model the structure of the clinical procedure, and adapt states that action A is known to be occurring at instant I
to the information from the classifiers (i.e. confidence and with probability P , but only if its preconditions encoded
probability distributions over classes). A user model based in θ are satisfied.
on probabilistic estimates can be used by a rule system to • the p-proposition
estimate the best course of action using expert knowledge
A performed-at I if-believes (θ, P̄ )
encoded in a rule system. This user model can be then further
processed in order to customise the therapeutic intervention, states that action A is performed by the agent at instant
and therefore to raise the quality of the children experience, I if its state of belief in formula θ at instant I falls in
by also taking into account the behaviour of the child during the (open, half-open or closed) interval P̄ .
the rehabilitation process. Indeed, the visual interface will be A domain description in EPEC is a collection of these
used to offer children the exercises as part of recreational propositions satisfying some integrity constraints (e.g. exactly
activities that make use of detected social signals. This will one i-proposition must belong to any domain description). We
offer a rehabilitation process based on games whose behaviour are not going to describe these constraints formally here, but
automatically adapts to the child. the interested reader can find them in [29], [30].
1) EPEC: We propose the use of language EPEC (short for EPEC has a possible-worlds semantics where each world
Epistemic Probabilistic Event Calculus) as a foundation for represents a possible evolution of the world from the ini-
our methodology. Similarly to MLN-EC and ProbEC, EPEC tial state and is weighted according to the propositions in
is a language in the style of the Event Calculus for reasoning the domain descriptions. Four implementations of EPEC are
about actions, but goes beyond these languages in that it allows available and/or under active development, and can answer
for the modelling of noisy sensors. Its foundations were laid queries regarding what is true (and with what probability)
in [29], and it has since then been extended in [30] to also in a given domain. Two of them are optimised for the non-
include sensing actions and propositions conditioned on belief. epistemic fragments of EPEC, called PEC+. While the exact
We briefly introduce its syntax in the following. implementation (written in clingo [31]) exhaustively works
In the tradition of reasoning about action languages, out all the possible worlds and their associated weights,
EPEC models a given domain using fluents (which represent the approximate implementation (written in the probabilistic
properties of the world), instants (which represent time points programming language Anglican [32]) samples a user-defined
at which events may occur) and actions (which represent number of worlds using Anglican’s built-in Markov Chain
actions under the control of the agent being modelled or the Monte Carlo sampling capabilities, and uses the obtained sam-
environment itself). The causal interactions between fluents ple to approximate the probability of a query. Similarly, there
and actions are captured by the specialised propositions below: are two implementations of EPEC (including the epistemic
fragment) to deal with exact and approximate inference.
• the v-proposition 2) Knowledge Base: The following simple domain
description demonstrates some features of EPEC:
F takes-values hV1 , . . . , Vn i
Engagement takes-values hfalse, truei (1)
states that fluent F can take values V1 , . . . , Vn .
• the i-proposition
initially-one-of {({Engagement}, 1)} (2)
EEG senses Engagement (3)
initially-one-of {(ψ1 , P1 ), . . . , (ψn , Pn )} 0.7 0.3
with-accuracies
states that the environment is initially in one of the states 0.4 0.6
ψ1 , . . . , ψn with probabilities P1 , . . . , Pn . Watching senses Engagement
(4)
0.8 0.2
• the c-proposition with-accuracies
0.1 0.9
θ causes-one-of {(ψ1 , P1 ), . . . , (ψn , Pn )} Cutscene causes-one-of {({Engagement}, 0.9), (∅, 0.1)} (5)
states that θ, a formula encoding one or more actions ∀I, EEG performed-at I (6)
and some fluent preconditions, has the effect of causing ∀I, Watching performed-at I (7)
exactly one of the fluent conjunctions ψ1 , . . . , ψn with if-believes (Engagement, [0, 0.7])
probabilities P1 , . . . , Pn respectively.
∀I, Cutscene performed-at I (8)
• the s-proposition
if-believes (Engagement, [0, 0.5])
θ senses F with-accuracies M
These propositions aim at describing an automated system
states that θ has the effect of sensing fluent F with an used to detect the degree of engagement of the patient, which
accuracy given by the confusion matrix M . can sound a dedicated alarm to raise the patient’s level of
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Workshop "From Objects to Agents" (WOA 2019)
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