Search Results (2)

Playful approaches are recommended to enhance wheelchair skills training with young people. Inclusive dance allows participants to discover motor skills and improve social participation. Integrating wheelchair skills training into dance has not been evaluated. This study aimed to explore participants’ experiences in dance while integrating wheelchair skills, and the influence of dance on wheelchair skills and wheelchair use confidence in young people. A convergent mixed-methods design was used during a one-week dance camp. Data collection combined observations, two focus groups (with young dancers who used manual wheelchairs and with professional dancers without disabilities), and evaluation of wheelchair skills and confidence. Data analyses included deductive thematic analysis guided by the Quality Parasport Participation Framework, merged with pre–post comparisons in wheelchair skills and confidence. Three young female dancers were 11, 12 and 15 years of age and three professional female dancers were 22, 27 and 27 years of age. Emergent themes included skill mastery, belongingness, and supportive environments. There were improvements in wheelchair skills and confidence (16.7%, 19.4%, 16.7%; 0.8%, 11.4%, 4.5%, respectively). Participants described overall positive experiences with the dance camp and perceived enhanced skills and confidence. This study advances knowledge about innovative approaches to integrate wheelchair skills training for young people. Future larger-scale controlled studies are needed to determine efficacy. Full article

Manual wheelchair dance is an artistic recreational and sport activity for people with disabilities that is becoming more and more popular. It has been reported that a significant part of the dance is dedicated to propulsion. Furthermore, wheelchair dance professionals such as Gladys Foggea highlight the need for monitoring the quantity and timing of propulsions for assessment and learning. This study addresses these needs by proposing a wearable system based on inertial sensors capable of detecting and characterizing propulsion gestures. We called the system WISP. Within our initial configuration, three inertial sensors were placed on the hands and the back. Two machine learning classifiers were used for online bilateral recognition of basic propulsion gestures (forward, backward, and dance). Then, a conditional block was implemented to rebuild eight specific propulsion gestures. Online paradigm is intended for real-time assessment applications using sliding window method. Thus, we evaluate the accuracy of the classifiers in two configurations: “three-sensor” and “two-sensor”. Results showed that when using “two-sensor” configuration, it was possible to recognize the propulsion gestures with an accuracy of 90.28%. Finally, the system allows to quantify the propulsions and measure their timing in a manual wheelchair dance choreography, showing its possible applications in the teaching of dance. Full article