## 2 Literature Review
However, these games have just one difficulty level [17; 18; 19] or fixed difficulty levels [20; 21; 22; 23; 24; 25; 26; 28]. They are not dynamically adjusted to the people's needs. Reviews have been published regarding DDA on videogames [14; 15]. Nevertheless, few studies have implemented DDA in their serious games for upper limb rehabilitation. For instance, [PERSON] et al. [29] implemented a game in which the player pops balloons which are moving in the game scene using pinch hand movement. This hand movement was identified using an LMC. Furthermore, Q-learning was used to modify the balloons' movement time. They evaluated the user experience using the system usability scale (SUS) questionnaire. The SUS scores determined the game as a usable and satisfactory environment for the user. Another study [30] implemented a game, in which a plane is controlled using flexion, extension, ulnar, and radial hand movements. The player collects rewards that are displayed in the game scene. An LMC was used to identify the hand movements. DDA was implemented in the game by setting the rewards at positions that are difficult, moderately difficult, and easy to collect by the player in the game scene. The DDA was performed using fuzzy logic and according to the range of motion of the player's hand. Additionally, the player's engagement was estimated using a GEQ (Game Experience Questionnaire). Another study [31] has implemented three games for arm rehabilitation: (i) target reach, in which the players move their arms to reach a sphere displayed in the game; (ii) line draw, in which the players perform flexion and extension movements to draw straight lines; and (iii) shape draw, in which the players draw shapes indicated by the physiotherapist. Difficulty adjustments were performed according to rules defined by authors. They considered the reaction time and range of motion of the participant to perform the game difficulty adjustment by changing the shape to be drawn, and the time that they had to do the task. Authors applied a system usability scale questionnaire to assess their proposal. The three games reported above average system usability scale scores. Finally, a virtual reality game [32] was implemented for arm rehabilitation. Specifically, this game was a first-person-shooter, in which the player kills zombie enemies. Two strategies were used to adjust the game difficulty: (i) adjust the number of enemies, and (ii) adjust the health of enemies. Support vector machines were used for classifying three levels of players' stress based on electrodermal activity, electrocardiogram, and electromyography signals. A NASA-TLX (NASA Task Load Index) questionnaire was used to assess the participants' affective state. No statistical differences were found between the two strategies for adjusting the game difficulty in terms of the workload induced by the participants. As mentioned previously, a key aspect in continuing with the rehabilitation process is the participant's engagement. The engagement could be measured using questionnaires (e.g., NASA-TLX (NASA Task Load Index) [33], and GEO--Game Experience Questionnaire [34]) or biosignals (e.g., electrodermal activity (EDA) [35], electrocardiogram (ECG) [36], electroencephalography signals (EEG) [37; 38], and electrooculography signals (EOG) [36]). A review on the use of biosignals for assessing players' emotions in games can be found in a study by [PERSON] et al. [35]. Nevertheless, few studies have analyzed engagement during the playing of serious games for upper limb rehabilitation. Specifically, these studies have used questionnaires (i.e., GEO [30; 39], NASA-TLX [32]) and numerical rating scales [40] to determine the players' engagement.