Fatigue-Aware gaming system for motor rehabilitation using biocybernetic loops.

Esta tesis tiene como objetivo proponer una terapia de rehabilitación complementaria basada en paradigmas de interacción humano-computadora (HCI) que exploran i) Técnicas de rehabilitación virtual, integrando tecnologías de realidad virtual (VR) sofisticadas y (hoy en día) accesibles, ii) sensores fisiológicos de bajo costo, a saber, electromiografía de superficie (sEMG) y iii)sistema inteligente, a través de adaptación biocibernética, para proporcionar una nueva técnica de rehabilitación virtual..

Muscle activation during Exergame playing

Exergames may provide low-cost solutions for playing, training and rehabilitation. Exergame user research (EUR), studies the interaction between an Exergame and users, in order to provide feedback for game developers and safe and meaningful gameplay. Detailed evaluations and a coding system based on muscle activation levels are necessary to characterize Exergames. This is important when it comes to use exergames in purposes other than fun. The purpose of this chapter was to characterize the muscle activation during a swimming Exergame as an example and to compare the level of activation during different conditions. Healthy subjects played bouts of Exergame using Xbox360 and Kinect. Muscle activation was monitored for desired muscles on dominant upper limb using wireless electromyographic system. Preliminary resutls showed that upper trapezius was the most active muscle in all techniques. An investigation of muscular coordination was also conducted to provide an activation sequences of studied muscles. Results can provide insights for practitioners to have a baseline on application of exergames in their routines

User training for machine learning controlled upper limb prostheses:a serious game approach

BACKGROUND: Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling multiple DoFs although it requires separable and consistent electromyogram (EMG) patterns. Whereas user training can improve EMG pattern quality, conventional training methods might limit user potential. Training with serious games might lead to higher quality EMG patterns and better functional outcomes. In this explorative study we compare outcomes of serious game training with conventional training, and machine learning control with the users' own one DoF prosthesis. METHODS: Participants with upper limb absence participated in 7 training sessions where they learned to control a 3 DoF prosthesis with two grips which was fitted. Participants received either game training or conventional training. Conventional training was based on coaching, as described in the literature. Game-based training was conducted using two games that trained EMG pattern separability and functional use. Both groups also trained functional use with the prosthesis donned. The prosthesis system was controlled using a neural network regressor. Outcome measures were EMG metrics, number of DoFs used, the spherical subset of the Southampton Hand Assessment Procedure and the Clothespin Relocation Test. RESULTS: Eight participants were recruited and four completed the study. Training did not lead to consistent improvements in EMG pattern quality or functional use, but some participants improved in some metrics. No differences were observed between the groups. Participants achieved consistently better results using their own prosthesis than the machine-learning controlled prosthesis used in this study. CONCLUSION: Our explorative study showed in a small group of participants that serious game training seems to achieve similar results as conventional training. No consistent improvements were found in either group in terms of EMG metrics or functional use, which might be due to insufficient training. This study highlights the need for more research in user training for machine learning controlled prosthetics. In addition, this study contributes with more data comparing machine learning controlled prosthetics with Direct Controlled prosthetics

The Effect of Spinal Cord Stimulation and Video Games Training on Body-machine Interface Control

Damage to the spinal cord causes long-lasting loss of motor and sensory function, and currently, there is no ‘cure’ for paralysis. However, even people with severe spinal cord injuries (SCI) have some residual mobility. Studies have shown that transcutaneous electrical spinal cord stimulation (tSCS) combined with functional training targeting residual mobility can further improve the motor function of individuals with SCI. In this study, we present a technical framework that aims to enhance rehabilitation outcomes by targeting residual mobility through a motor training-based approach. Our technical framework centers around a non-invasive body-machine interface (BoMI) that relies on the use of several inertial measurement units (IMUs) to capture the residual mobility of the participant’s body and translate it into the ability to control a two-dimensional (2D) cursor on a computer screen. Participants can manipulate this 2D computer cursor by using their residual body movements to complete a series of self-developed tasks for functional motor training, such as center-out reaching tasks and 2D video games. Additionally, tSCS electrodes were placed at designated spinal segments during the motor training and attempted to produce neuromodulatory effects that facilitate leg and trunk movement and performance of BoMI control. Subsequently, our work aimed to investigate the effect of using non-invasive tSCS and immersive 2D video games on participants’ performance of motor control and learning rate through the above training framework. Participants\u27 performance was recorded and quantified using four assessment metrics based on different center-out reaching tasks. Therefore, a multi-day experiment recruiting both unimpaired control participants and people with SCI was conducted to investigate the effect of training with tSCS and 2D video games on the performance of center-out reaching tasks. Our findings revealed that the BoMI performance of the unimpaired control group improved after training with center-out reaching tasks, and the final performance and learning rate were unrelated to the application of tSCS. However, the effect of tSCS on individuals with SCI varied from person to person. Specifically, we found that tSCS had a clear facilitation effect on the BoMI performance, resulting in a better final performance and a significant learning rate for SCI participant BMS002 but not for SCI participant BMS001. Moreover, our results showed that training with reaching tasks and video games resulted in similar final BoMI performance within the unimpaired control group, but training with reaching tasks generated a better learning rate. Regarding participants with SCI, training with video games led to a significant learning rate in BMS001 and a non-significant learning rate in BMS002. In addition, we observed that there was no significant difference between the final performance after training with reaching task and video games in both unimpaired control and SCI participants. In conclusion, our results suggest that functional training with tSCS could be an effective approach to enhancing motor function and learning rate for individuals with SCI. Also, video games could be considered as a promising training strategy, equivalent to traditional center-out reaching tasks