## 2 Literature Review  
In the last five years, several studies have implemented games for upper limb rehabilitation. For instance, a study [17] proposed a ski driving game which is controlled using hand gripping, balance handle operation, as well as the extension, flexion, pronation, and supination of the shoulder, elbow, and forearm. Electromyography (EMG) signals were used to detect the extension, flexion, pronation, and supination movements, whereas electroencephalography (EEG) signals were used to detect intentions to carry out the hand gripping and balance handle movements. Similarly, [PERSON] et al. [18] created a game for hand rehabilitation in which a ball is controlled using hand movements (e.g., closed hand, open hand, wrist extension, and wrist flexion) to avoid obstacles and drop it into a basket. EMG signals were collected via a Myo armband sensor and processed using convolutional neural networks to detect the hand movements. Another study [19] designed a lightweight and portable hand exoskeleton with a hand-sensing glove to play an archery game for hand rehabilitation. In the same vein, [PERSON] et al. [20] developed a wearable grip sensor to detect finger flexions to play piano melodies in a serious game. [PERSON] et al. [21] created a glove to identify hand movements that were used to control two games for hand rehabilitation. Specifically, the players move a character in the air to collect coins using flexion and extension hand movements in game 1, whereas they grab and squeeze fruits using grip and release hand movements in game 2. Several studies have used a Leap Motion Controller (LMC) sensor to develop games for hand and upper limb rehabilitation [22; 23; 24; 25; 26]. This sensor has infrared cameras that identify X, Y, Z positions of the phalanges, palm, and wrist. A study [27] has assessed the performance of the LMC (i.e., feasibility, accuracy, and repeatability) when used in conjunction with a serious game for upper limb rehabilitation. They conducted experiments using a Yaskawa industrial robot that mimics upper limb motions and is tracked by LMC. They concluded that the LMC has a satisfactory performance regarding tracking accuracy. In the context of using LMC for serious games for upper limb rehabilitation, [PERSON] et al. [22] implemented a game to improve fine motor control in children with epileptic encephalopathy. Specifically, the game simulates the preparation of a pizza using movements such as picking up, grasping, manipulating, and releasing objects with the fingers. These movements were detected using an LMC sensor. [PERSON] et al. [23] implemented three games to improve hand function and grip strength: (i) a \"counting with fingers\" game, in which the player performs finger flexion and extension; (ii) a \"pronation and supination, wrist flexion, and extension\" game, in which the player performs these movements; and (iii) a \"hand grasp\" game, in which the player grasps a cube. All the movements were identified using an LMC. Another study [24] implemented a piano game which was played using finger movements detected via an LMC. [PERSON] et al. [25] implemented a game which is controlled using the flexion, extension, pronation, and supination of the hand. These movements were identified via an LMC and used to control and set an airplane on gates. Another study [26] implemented a game to train shoulder horizontal adduction, in which the player moves a virtual cursor using arm movements to color tiles that define specific trajectories. The arm movements were detected via an LMC. [PERSON] et al. [28] fused data from a force myography sensor, an electromyography sensor, and inertial measurement unit to identify hand movements (e.g., thumb abduction, wrist flexion, wrist dorsiflexion, forearm pronation, and forearm supination) that were employed to play two games: (i) \"find the sheep\", in which the player performs hand movements to find the sheep card; and (ii) \"Best Salesman\", in which the player delivers the food to customers using hand movements.