This paper presents a system to deliver automated, noninvasive, and effective fine motor rehabilitation through a rhythm-based game using a Leap Motion Controller. The system is a rhythm game where hand gestures are used as input and must match the rhythm and gestures shown on screen, thus allowing a physical therapist to represent an exercise session involving the user's hand and finger joints as a series of patterns. Fine motor rehabilitation plays an important role in the recovery and improvement of the effects of stroke, Parkinson's disease, multiple sclerosis, and more. Individuals with these conditions possess a wide range of impairment in terms of fine motor movement. The serious game developed takes this into account and is designed to work with individuals with different levels of impairment. In a pilot study, under partnership with South West Advanced Neurological Rehabilitation (SWAN Rehab) in Phoenix, Arizona, we compared the performance of individuals with fine motor impairment to individuals without this impairment to determine whether a human-centered approach and adapting to an user's range of motion can allow an individual with fine motor impairment to perform at a similar level as a non-impaired user.

In order to regain functional use of affected limbs, stroke patients must undergo intense, repetitive, and sustained exercises. For this reason, it is a common occurrence for the recovery of stroke patients to suffer as a result of mental fatigue and boredom. For this reason, serious games aimed at reproducing the movements patients practice during rehabilitation sessions, present a promising solution to mitigating patient psychological exhaustion. This paper presents a system developed at the Center for Cognitive Ubiquitous Computing (CubiC) at Arizona State University which provides a platform for the development of serious games for stroke rehabilitation. The system consists of a network of nodes called Smart Cubes based on the Raspberry Pi (model B) computer which have an array of sensors and actuators as well as communication modules that are used in-game. The Smart Cubes are modular, taking advantage of the Raspberry Pi's General Purpose Input/Output header, and can be augmented with additional sensors or actuators in response to the desires of game developers and stroke rehabilitation therapists. Smart Cubes present advantages over traditional exercises such as having the capacity to provide many different forms of feedback and allowing for dynamically adapting games. Smart Cubes also present advantages over modern serious gaming platforms in the form of their modularity, flexibility resulting from their wireless network topology, and their independence of a monitor. Our contribution is a prototype of a Smart Cube network, a programmable computing platform, and a software framework specifically designed for the creation of serious games for stroke rehabilitation.

In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer.

In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.