Dynamic Difficulty Adaptation in Therapeutic Serious Games after Stroke N. Hocine1, A. Gouaïch1, I. Di Loreto1 1
LIRMM (Montpellier 2 University, CNRS), Montpellier, France
Abstract—Therapeutic game adaptation is an important feature allowing the personalization of the patient’s motor re-learning while maintaining her/his engagement and motivation. This article presents a dynamic difficulty adaptation technique dedicated to a family of therapeutic games for upper-limb post-stroke rehabilitation. The proposed technique is based on players’ profile and prior clinical assessment of their physical capabilities. It provides a generic real-time adaptation module to dynamically adapt the game experience to each patient according to her/his observed performances and capabilities. An initial prototype and a pre-pilot experiment on healthy people are presented to demonstrate how this technique has been preliminary implemented and could be tested for poststroke therapeutic games.
adaptation technique dedicated to a family of therapeutic games. The rest of this paper is organized as follows: we present in the third section the proposition’s context as well as our motivations. The fourth section outlines the state of the art of difficulty adaptation in games. The following section aims to describe the proposed adaptation technique. In the sixth section, we describe the pilot experiment. Finally, we conclude this paper by discussing the experiment’s results and describing our outlook concerning the difficulty adaptation issue in the therapeutic game context.
Index Terms—Game difficulty adaptation, therapeutic serious game.
Stroke is a medical emergency that can cause permanent neurological and functional damages. It can cause for instance a hemineglect and an inability to move one or more limbs on the controlateral side of the body. The specific abilities that can be affected by stroke depend on the location, type and size of the lesion. Each patient is then characterized by a specific combination of deficits. In addition, rehabilitation programs are different and should be adapted to patients’ abilities in order to regain as much motor functions as possible. Most rehabilitation techniques are founded on the principles of motor re-learning and skill acquisition established for the healthy nervous system. Several studies suggest that intensive training (many repetitions) while giving feedbacks and motivating patients can have an important impact on patients’ skills recover [2] [12]. During the rehabilitation process therapists face hard difficulties. Often patients progress slowly and recover only a part of their motor abilities. In addition, for patients the rehabilitation is hard and exhausting and their motivation has to be supported during the rehabilitation process. As a matter of fact, the therapist continuously supports the patient by encouraging her/him and adapting the rehabilitation exercises to her/his ongoing health condition. The potential benefit of a training based on a virtual reality or serious game strategy consists in providing an environment in which the training intensity, duration and frequency can be manipulated and enhanced. This manipulation could be used in order to motivate the patient by creating a personalized motor re-learning paradigm [11]. For instance, integrating gaming features in virtual environments for rehabilitation could enhance patient’s motivation [17] which is, in turn, a key to recovery. A person who enjoys what he is doing spends more time developing her/his skills in a given activity.
I. INTRODUCTION Serious game is currently attracting many researchers’ interests as it is considered as a useful training, education and learning tool [21]. This usefulness is due to its approach that combines both fun and serious aspects. Indeed, the serious game has quantifiable objectives in terms of acquiring knowledge and skills, and video game features allowing the creation of a motivating environment. The serious aspect or game goals highly depends on the application domain such as advertisement, education, simulation and health. The pedagogical purpose can be for instance informative, educative and/or training (motor learning and re-learning) We are interested in this field by the health sector and especially by the use of serious game as a training or rehabilitation tool. The serious aspect in our context concerns motor re-learning facets without focusing on cognitive or learning issues. In this case the therapeutic game is used (i) by post-stroke patients in order to help them to regain as much their functional skills as possible and (ii) by trainers or therapists as it can help them to support patient’s motivation, to perform pedagogical training programs and to get quantitative measures about the patient’s performance during her/his rehabilitation sessions. In this paper, we focus on the adaptation of therapeutic games dedicated to upper limb post-stroke rehabilitation. The proposed adaptation technique aims to increase patients' motivation by making the training a personalized experience. In fact, our purpose is to better understand practical issues related to adaptation challenges regarding the usability and effectiveness of serious game-enhanced stroke rehabilitation. The main goal is to develop a generic
II.
POST-STROKE REHABILITATION BASED ON ADAPTIVE SERIOUS GAME
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Furthermore, the personalization of motor re-learning focuses on using an appropriate game design that must fit accurate, personal, therapeutic goals. The patient’s performance can vary according to her/his situation and health condition influencing her/his capabilities. Thus, if the therapeutic game has not properly assessed the patients’ capabilities, it is highly possible that the patient will not succeed in the proposed activity, decreasing thus her/his motivation to continue the therapy session. To avoid this effect an adaptive approach can be introduced. An adaptive therapeutic game can adjust its behavior on the base of the patient’s ongoing results. It can dynamically adapt the game difficulty according to the patient’s assessment and not only to her/his profile data. Thus, adaptation can fill the gap between the current condition of the patient during the game and the patient’s profile assumed at the beginning of the therapy session. III.
STATE OF THE ART OF DIFFICULTY ADAPTATION IN THERAPEUTIC SERIOUS GAME
The difficulty of an activity (or challenge) can be defined as the degree to which the activity represents a personally demanding situation requiring a considerable amount of cognitive or physical effort. The aim of challenging the player is to develop her/his knowledge and skills while maintaining her/his engagement and motivation [15] [16]. In addition, the adaptation of the difficulty of the player’s activities influences her/his motivation by avoiding her/his non-commitment and frustration, two factors that affect the learning curve and reduce the time to doing tasks [15]. From a game design point of view, the game difficulty level can be designed either statically or dynamically. The static approach consists in an objective determination of the difficulty level. In this case, the player can choose manually the game difficulty level or it is the game that requires her/him to follow a series of difficulty levels according to her/his performance. The dynamic approach focuses on adapting the game to the player’s progresses, status and performance during the game experience [7] [4]. In this in context, we find work on dynamic difficulty adjustment (or DDA) of entertainment games which consider the difficulty as a heuristic function that depends on game metrics [9]. We also find techniques based on the dynamic creation of games narratives and scenarios [19] and the creation of strategies [7] that are often based on player’s profile and her/his situations (for more information about the literature review please see [20]) In the therapeutic game context, there is no single definition of a task’s difficulty. Most therapeutic games proposed in literature are based on static determination of the game difficulty by therapists [1]. For instance, Heuser et al [8] suggest five therapeutic games exercises with the same graphical interfaces. Each exercise involves some difficulty levels towards the main patient’s recuperation steps in therapy. This technique consists in providing real-time performance feedbacks to patients and therapists. Each difficulty level differs from the others by changing the: virtual object’s color, speed and size. The player’s performance depends on her/his finger forces, speed and movement precision. Finally, the difficulty level is designed in a static manner. Thus, when
the patient fails as the difficulty level is beyond her/his capabilities, the simulation stops the exercise. Chen Y. et al [3] propose a framework for media adaptation in task-oriented neuromotor rehabilitation based on biofeedbacks [10]. Scenery images and their clarity, sounds variety and music instruments are used to reflect the arm movement. The adaptation strategy depends on the choice of the musical instrument, tempo variation and task difficulty which is annotated by the therapist. The decision making on adaptation focuses on a dynamic decision network to model the real-time relationship between the media adaptation and the patient’s performance. Finally, the main idea of this work is to select in real-time the most appropriate adaptation strategy according to each patient’s performance. The experiments in this work propose three adaptation factors: special accuracy, hand trajectory and hand velocity, without merging more than a factor in an adaptation scenario. Another interesting example is given by Ma et al, [14]. The authors suggest an adaptive post-stroke rehabilitation system based on a virtual reality game. The game aims to improve functional arm capabilities, visual discrimination and selective attention using three difficulty levels: beginner, intermediate and expert. The difficulty level is determined according to player’s model. The latter includes information about the patient's motor skills by the use of medical metrics such as standard medical motricity index [5]. The decision concerning the difficulty level focuses on a specification matrix that determines the relationship between the patient’s profile parameters and the difficulty levels. As previous works, the task difficulty is related to the difficulty level which is statically determined. Furthermore, the adaptation technique doesn't take into account the in-game player's performance and her/his motivation and engagement during the game session. IV. PROPOSITION The personalization of the therapeutic game experience should be taken into account during the first stages of the player-centered game design. The latter must fit both therapists’ objectives and patients’ training needs. Our research activity results from more than a year of collaborative work with therapists and researchers from the Montpellier rehabilitation laboratory. This work allowed us to define, through participatory design, the most important elements we need in order to design adaptive therapeutic games. These elements concern the virtual-environment, game goals, difficulty, and motivation requirements. We are interested especially in this paper by the two latter. Our proposed technique aims to increase patients' motivation by making the training a personalized experience. In the following sub-sections, we give an introduction to the key elements contributing to the therapeutic game design process, our proposed difficulty adaptation technique as well as the motivation model. A. From actual to virtual rehabilitaion session A virtual rehabilitation session based on therapeutic serious game allows providing an environment in which we consider as significant both patients and therapists. The serious game can be helpful as it consists in a
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training tool used during: (i) the first months of stroke rehabilitation programs in hospital and (ii) when patients return home, to help them to remotely continue their rehabilitation programs in order to improve their quality of life. In addition, the use of a serious game strategy allows fulfilling pedagogical purpose by the help of a training personalization paradigm and introducing fun aspects to motivate patients. 1) From therapeutic objectives to game goals Despite the variety of functional rehabilitation strategies that depend on therapists’ practices, the rehabilitation program’s objectives are usually similar. Therapists try to help patient to improve their functional skills as movement amplitude, direction and smoothness. In this paper, only the therapy of upper-limb is considered and more specifically, only therapeutic objectives related to the amplitude and range of arms are considered. Therapists organize the rehabilitation programs as a set of exercises to be followed by a patient. These exercises are usually based on point-to-point movement and require to be repeated during the rehabilitation session. Therapeutic objectives are defined to improve continually and progressively the patient’s performance. The objective can be changed during the game according to patient motivation, tiredness and performance. The therapeutic game goals can be defined following a task-oriented design. A game goal for such task (or exercise in an actual rehabilitation session) can be dynamically defined using three parameters: (i) task type, such as reach or grasp a virtual object and get equilibrium, (ii) spatial and temporal task difficulty, for instance about target-hand distance and direction, trajectory precision and timeout (iii) as well as required repletion value. 2) Therapeutic game abstract model As said in the motivation section, rehabilitation needs a variation of game themes and ambiances to avoid monotony and consequently patient’s boringness. However, although the theme and ambiance of the game can be changed, the therapeutic objectives and strategies should be maintained, i.e. they have to be independent from the actual game. Consequently, two levels have been identified: (i) an abstract movement level and (ii) an actual game level. The abstract movement level considers the mechanics of patients' movement and their relationship to therapeutic objectives; the actual game level constructs a context and meaningfulness for the abstract level. For instance, at the abstract movement level the objective can be to reach a target point starting from a source point. This can be translated differently in games such as catch an animated rabbit or a virtual fish in another virtual world. 3) Player’s profile and the assessment step Each rehabilitation session element should be adapted to patients and therapists’ needs. As a matter of fact, introducing an adaptation mechanism is required at first steps of the therapeutic game design process. Like in the actual rehabilitation session, the virtual rehabilitation session has to start with the assessment of the patients’ capabilities. This step is very important as it allows
defining the first elements and tasks’ objectives of the game. Therapists use generally clinical test scores or a kinematic based evaluation in virtual reality or robotic aided therapy. As for serious games and especially in our approach, the assessment step is included in the game. The game proposes to the player to reach some targets placed in different positions of the 2D plan. The player’s profile is also used as a parameter of the game adaptation module. It contains general data about the player such as his age, gender, and right or left handed. It also contains data about stroke such as impaired side, stroke date, fugl-meyer score, and neglect side. Finally, the player’s profile and her/his assessment represent the key indicators used to choose an appropriate adaptation strategy according to the proposed game goal. B. General Difficulty Adaptation Framework The rationale behind the presented approach is presented in Figure 1. Once the therapeutic objective or game goal is set, a therapy session composed of a sequence of task-oriented therapeutic games takes place. When the patient interacts with the therapeutic game, the latter generates a set of observable data that are collected and referred generically as performances. The adaptation module uses this data to adjust the task difficulty. Our approach of motivation is inspired by the adequationist paradigm relating the motivation to the satisfaction of an individual [13]. In this approach, motivation is mainly considered as a regulation process. When the reality (outcome of a therapeutic game) is consistent with the expectations of the patient (perception of her/his own motor skills) then this is considered as a stabilized satisfaction state. The patient attempts to provide efforts to maintain this steady state.
Figure 1.General Framework
When introducing a minor disruption (constructive dissatisfaction), the patient may produce efforts to regulate her/his satisfaction state. However, in the case of an important disruption, the patient could fail in producing efforts to regulate her/his satisfaction state entering her/him in a demotivation situation.
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The difficulty adaptation module is asked to produce constructive dissatisfaction situations to keep the patient engaged in the therapy, increasing thus the rehabilitation volume (I.e total time and number of exercises) As it is shown by several studies [2] [12] the rehabilitation volume increases results toward a better achievement of therapeutic objectives. C. Motivation Model Motivation models proposed in the literature highly depend on the application domain. From post-stroke therapeutic games point of view, motivation depends on several factors such as: success and stimulation, feedback, autonomy, self-esteem, meaningfulness and variety of tasks. In addition, a quantitative approach is required to check the patient’s motivation during the game session. In this paper, the proposed motivation model focuses on the principles of the activation theory [18] since it fulfills most of the previous mentioned factors. This theory states that stimulation is necessary and the use of an activation level is required in order to make an individual sufficiently motivated to perform her/his tasks. This theory seems to be adequate for therapeutic game context where the stimulation can be controlled by adjusting the difficulty level. As a measure of the activation level, we have considered the ratio between the probability of encountered successes and failures for a patient [18]:
Activation(n) =
Pr (success )
log 1 − Prn (success) Prn (success) ≠ 1 n −1 otherwise 1 if
Prn ( success) represents the proportion of successful games from all prior games. The cumulative activation score is simply a sum of all activation scores:
The interaction matrix represents the 2D plan of game where each place D(i , j ) holds a probability that an action should be performed at this location by the patient. Having this information, the game level has to direct game actions towards places with highest probabilities. The advantage of this structure is to offer a common interface to all games. Thus, the game level considers the difficulty adaptation module as a black box producing the interaction matrix. To construct the interaction matrix by taking into account the therapeutic game requirements, it is necessary to collect some information concerning patient’s abilities. This step is called the assessment and produces a matrix where each place A(i , j ) represents a success rate of the patient. The difficulty level is expressed then as a desired success probability d for the patient. This means to let the patient succeeding with the probability d% at least. In this case, the relationship between the two matrices is defined as follows: 1
# {A(x, y ) ≥ d } D(i , j ) = 0
if (A(i , j ) ≥ d )
if (A(i , j ) p d )
Where # denotes the cardinal of a set (number of elements) This states that all places where the patient is supposed to succeed with a ratio of d at least are selected with equal probabilities. All other places are given probability of 0 so the game level ignores them. E. Difficulty Adaptation Process
Figure 2. Difficulty Adaptation Process
δ (n ) = ∑i=1 Activation(i ) n
This measure keeps a memory of all activation scores achieved by the patients. It provides a global trend of the motivation that is not affected by local variations. To represent local variations, we also introduce a derivative measure on activation scores:
δ (n ) = Activation(n ) − Activation(n − 1) This measure indicates the direction of the current motivation score when compared with the previous one. D. Abstract Model for Difficulty Adaptation As mentioned previously, it is important to conceive the difficulty adaptation module at the abstract level in order to reuse it in different games. This is introduced in our adaptation technique by defining a structure called: interaction matrix.
In our approach, the difficulty of a task is related to its probability of success. Higher success probability indicates low difficulty. Conversely, lower success probability indicates higher difficulty. The difficulty adaptation module takes one of the following three decisions according to the patient’s profile and
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motivation: increase, decrease or maintain the current difficulty level. Three criteria are taken into account in order to adjust the difficulty level. The first two criteria S + local (n), S −local (n) measures the local instability of the motivation in both direction increase and decrease respectively. The third criterion measures the overall trend of motivation. These criteria are calculated as follow: S − local (n) =
# {δ (i ) p 0, i ∈ [n − ω , n]}
ω
pω
+
local
# {δ (i ) ≥ 0, i ∈ [n − ω , n]}
p ω − local ω Tglobal (n) = Trend (σ (1), σ (n) ≥ ωtrend
S + local (n) =
ω is a parameter that determines the size of the window to calculate the local fluctuation of motivation. ω − local and ω + local determines thresholds of success and failure ratios in the local window. Tglobal represents overall trend of motivation. A least squares method is used to calculate this trend on the cumulative motivation. With these elements the algorithm making the following decisions: S − local (n) ∧ ¬Tglobal (n) : decrease the difficulty. Indeed, this is interpreted as a local degradation of motivation and a global trend indicating demotivation. S + local (n) ∧ Tglobal (n) : increase the difficulty. This is interpreted as a local and global increase of motivation: the patient is succeeding too easily. The difficulty is increased to keep an acceptable level of challenge. - In other cases, do not change the difficulty.
To experiment with the presented approach we have conducted a pilot study on healthy players. In fact, before experimenting with patients and disturb their classical rehabilitation program (planned for five weeks at least), it was necessary to experiment with healthy individuals. Nonetheless, the experiment scheme with healthy players has to simulate difficulty conditions - based on movement coordination on a 2D plan- similar to the ones patients could encounter during their rehabilitation program. The proposed game is structured as follow: given a flat 2D plan that can be lifted by the player and a starting point the goal is to stabilize a ball on a target point within a limited time. Depending on the location of the target the player find it more or less difficult to stabilize the ball on the target (see figure 3) The experiment follows an independent-measures design with two independent groups. Group 1 used a naive strategy of difficulty adaptation that increases the difficulty level in case of success. Group 2 used our proposed difficulty adaptation technique. The experiment was conducted in a similar way for both groups. An initial assessment allows to collect prior data about the player’s equilibrium capabilities. The player’s profile was then constructed using the functional measurement deduced from this evaluation test. This initial assessment also produces the difficulty matrix for the first round of the game. In addition, at the end of each round, players' performances allows the system to generate the task difficulty for the next round. Besides, at the end of each round, the player reports on his own perceived difficulty using the DP-15 scale[6]. B. Participants Each group was composed by 4 individuals with the following characteristics: TABLE I.
In addition, the abilities assessment is used as the initialization step for the adaptation module which will decide on game parameters such as the initial difficulty distribution matrix values and the initial target starting point. Once a round of the game is played, the module makes decision about the difficulty using the prior difficulty matrix and motivation model (see figure 2) V.
PILOT EXPERIMENT
A. Protocol Figure 3. Balance game
PILOT EXPRIMENT PARTICIPANTS Group 1 2
Gender 3M 1F 1M 3F
Age (25.75 ± 1.7) (25.75 ± 3.3)
Dominant hand 4 right 4 right
C. Hypothesis The following H0 hypotheses are stated: - A.H0 There is no difference between Group 1 and Group 2 concerning the level of perceived difficulty (With the following parameters: ω =5, ωtrend = 0.25 , ω + local = 0.6 , ω − local = 0.6 ) -
B.H0 There is no difference between Group 1 and Group 2 concerning the success rates.
D. Results Results are reported as (Mean ± Standard Deviation) All statistical analysis was performed using R (http://www.r-project.org) version 2.12.0. A tailed
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independent measure t-test was used to reject A.H0 and a Chi-square test for independence was used to reject B.H0. TABLE II. PERCEIVED DIFFICULTY Group 1 2
Perceived difficulty (8.45 ± 3.44) (5.68 ± 2.90)
Group 2 which used the difficulty adaptation strategy reported less perceived difficulty (M=8.75, SD=1.50) than group 1 (M=6, SD=1.41) which used a progressive difficulty adaptation strategy. This difference was significant, t(5.97)=-2.6679, p=0.01.
TABLE III.
SUCCESS AND FAILURE RATE
Group 1 2
Success 36 49
Failure 50 27
Group 2 which used the difficulty adaptation strategy has succeeded significantly more that group 1 using a progressive difficulty adaptation strategy. χ 2 = (1, n = 162) = 7.3907 , p = 0.006 E. Discussion According to the experiment results we can deduce that: The A.H0 hypothesis stating that there is no difference concerning the level of perceived difficulty between the two groups is false. We deduce that the adaptation strategy changes the perceived difficulty by changing the active component of the general framework of the proposed difficulty adaptation technique.
session and decreasing the level of her/his perceived difficulty.
VI. CONCLUSION AND FUTURE WORKS Therapeutic serious game in post-stroke rehabilitation field is considered as a promising research domain as it allows creating a personalized motor re-learning. The usefulness of such rehabilitation tool consists in providing a virtual-environment in which the training intensity, challenge and motivation are adapted to patient’s capabilities. We focused in this paper on the adaptation of therapeutic game difficulty. We proposed a generic difficulty adaptation technique dedicated to therapeutic games for point-to-point movement based upper limb stroke rehabilitation. The proposed technique parameters are mainly the player's profile and on a prior assessment of her/his physical capabilities to dynamically adapt the game experience. Actually, as stroke patients' failure rate is usually high in their daily rehabilitation session, our adaptation technique could show its potential on decreasing the perceived difficulty of tasks and thus maintaining the patient’s motivation. We conducted a pilot experiment on healthy players using two independent groups. Group 1 used a naive strategy of difficulty adaptation that increases the difficulty level in case of success. Group 2 used our proposed difficulty adaptation technique. Results shown that group 2 reported less perceived difficulty and a higher success rate than group using the naive approach. Despite the large effect size of 79%, the results of this experiment have to be carefully handled due to the small sample size (n=8) Further experiments with stroke patients have to confirm these initial findings.
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The B.H0 hypothesis stating that there is no difference concerning the success rate between the two groups is false. This means that there is a significantly difference between the two groups. The player’s performance is better in the group using the dynamic difficulty adaptation.
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The results of the pilot experiment shows that the dynamic game difficulty adaptation play an important role on motivating players as it (i) influence their perceived difficulty and (ii) therefore keep them involved in the game. In contrary to statically difficulty adaptation techniques used in the literature, our proposed technique has proven an intersting way to simulate the task difficulty in therapeutic games. In fact, the proposed technique allows training and challenging the player without frustrating or boring her/him. This can be carried out by maintaining an appropriate success level rate in the game
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AUTHORS N. Hocine, The Montpellier Laboratory of Informatics Robotics and Microelectronics –LIRMM (Montpellier 2 University - CNRS). 161 rue Ada 34095 MontpellierFrance (e-mail: hocine@ lirmm.fr) A. Gouaïch, The Montpellier Laboratory of Informatics Robotics and Microelectronics –LIRMM (Montpellier 2 University - CNRS). 161 rue Ada 34095 MontpellierFrance (e-mail: gouaich@ lirmm.fr) I. Di Loreto, The Montpellier Laboratory of Informatics Robotics and Microelectronics –LIRMM (Montpellier 2 University - CNRS), 161 rue Ada, 34095 Montpellier-France (e-mail: diloreto@ lirmm.fr)
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