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Virtual reality (VR) is a powerful tool to motivate its participants to active participation, while providing augmented feedback to instruct the subject and improve task performance. This chapter presents the technical prerequisites of different recording, display, and rendering technologies. VR does not replace the real environment, but is a tool for setting up automatic training schedules. VR can be a good solution to train dangerous or difficult tasks. VR is being applied in physiotherapy, occupational therapy to recover limb functionality after disease or accident, and to enhance cognitive learning. VR technologies can also be applied to provide feedback as assistance during activities of daily living—this is then typically called augmented reality (AR). Despite the many technological achievements and positive results in many therapeutic and assistive applications, the field of VR rehabilitation is still in an early phase. VR technologies will continue to grow, gain further mainstream acceptance and, eventually, have a significant positive effect on therapeutic outcome.

Patients with impaired walking function are often dependent on assistive devices to retrain gait and regain independence in life. To provide adequate support, gait rehabilitation devices have to be manually set to the correct support mode or have to recognize the type and starting point of a certain motion automatically. For automated motion type detection, machine learning-based classification algorithms using sensor signals from different body parts can achieve robust performance. However, until today, there is only little work available to detect motion onset. In this paper, we investigate task onset detection of sit-to-stand and stand-to-sit transitions. The focus of the current study is twofold: First, the optimal window size for the online classification algorithm shall be found. Second, the ideal sensor placement in a single sensor-setup, to detect movement onset with shortest detection delays possible is of interest. For our investigations a linear discriminant analysis classifier, basic kinematic features, and a leave-one-subject-out cross validation are used. As a result, an average detection time of 56 milliseconds (SD 111) for sit-to-stand and 48 milliseconds (SD 137) for stand-to-sit were achieved with a window size of 15 and 35 milliseconds respectively at a data rate of 200 hertz. For sit-to-stand transitions, a sensor close to the tenth vertebra and for stand-to-sit transitions close to the posterior pelvis provided the smallest detection times.

Increasing the transparency of robotic systems used for gait rehabilitation can improve training of various daily activities such as walking or balancing. However, until today, there has been very little effort invested in a systematic analysis of occurring unwanted interaction forces between the patient and the robot. In this paper, we have developed such a setup for systematic analysis and quantification of interaction forces between the gait rehabilitation device the FLOAT and the patient. The results of our analysis show that our setup is capable of quantifying small interaction forces and movement effects under different, precisely controllable experimental conditions. Measured interaction forces of the FLOAT were found to be small and in the range of 3.3 N at low body weight support (52 N) to 17.6 N at high body weight support (376 N) when moving with constant velocity. In addition, deviations of the measured forces in the FLOAT ranged from 2 to 5 N. In combination with additional measurements that investigate other robot workspace locations and movement directions these results will help to understand the influence of unwanted interaction forces on patient recovery.

Bouncing a ball with a racket is a hybrid rhythmic-discrete motor task, combining continuous rhythmic racket movements with discrete impact events. Rhythmicity is exceptionally important in motor learning, because it underlies fundamental movements such as walking. Studies suggested that rhythmic and discrete movements are governed by different control mechanisms at different levels of the Central Nervous System. The aim of this study is to evaluate the effect of fixed/fading haptic guidance on learning to bounce a ball to a desired apex in virtual reality with varying gravity. Changing gravity changes dominance of rhythmic versus discrete control: The higher the value of gravity, the more rhythmic the task; lower values reduce the bouncing frequency and increase dwell times, eventually leading to a repetitive discrete task that requires initiation and termination, resembling target-oriented reaching. Although motor learning in the ball-bouncing task with varying gravity has been studied, the effect of haptic guidance on learning such a hybrid rhythmic-discrete motor task has not been addressed. We performed an experiment with thirty healthy subjects and found that the most effective training condition depended on the degree of rhythmicity: Haptic guidance seems to hamper learning of continuous rhythmic tasks, but it seems to promote learning for repetitive tasks that resemble discrete movements.