Title: Augmented feedback to enhance motor and artistic learning during social distancing
Funder: Economic and Social Research Council

Abstract: Social distancing has produced an indefinite shift to remote teaching. This shift poses unaddressed challenges in delivering classes and providing feedback remotely for physical activities such as performing arts or rehabilitation (Leijen et al. 2009; Barton et al. 2015; Osterlie 2016). Digital technologies can facilitate or enhance learning (Henderson et al. 2017) but little is known about remote motor skills teaching, hence there is an urgent need to identify the best practices for remote teaching of physical and artistic disciplines. The lessons learnt would be transferable to any motor skills teaching, such as rehabilitative exercise or practical skills. We propose building on (a) QMUL facilities and expertise including an online teaching platform, experts in psychology, dance medicine, physiotherapy, cognition and learning, artificial intelligence and multimedial signal processing, and (b) collaboration with established dance partners (English National Ballet school, Royal Ballet, Laban Conservatory, London Contemporary Dance School, Central School of Ballet, FloorBarreTM) and Barts Health NHS Trust. We will 1) experimentally validate the efficacy of different remote teaching methods 2) experimentally validate the efficacy of different tools on physical and artistic training including artificial intelligence augmented feedback (Lakhal et al. 2019a, b; Lin et al. 2019) 3) provide results useful to best practice identification to plan motor learning/dance teaching 4) develop tools for remote feedback during the rehabilitation of dancers

Lay abstract: Social distancing has produced an indefinite shift to remote teaching. This shift poses unaddressed challenges in delivering classes and providing feedback remotely for physical activities such as performing arts or rehabilitation. Digital technologies can facilitate or enhance learning but little is known about remote motor skills teaching, hence there is an urgent need to identify the best practices for remote teaching of physical and artistic disciplines. The lessons learnt in this project will be transferable to any motor skills teaching, such as rehabilitative exercise or practical skills. We will build on (a) Queen Mary University of London facilities and expertise including an online teaching platform, experts in psychology, dance medicine, physiotherapy, cognition and learning, artificial intelligence and multimedial signal processing, and (b) a collaboration with established dance partners (e.g. English National Ballet school, Laban Conservatory, FloorBarreTM) and Barts Health NHS Trust. We will validate the efficacy of different remote teaching methods and tools on physical and artistic training including artificial intelligence augmented feedback. By focusing on remote training for dance we will not only shed light not on a specific popular discipline but on the remote training of a broad range of traits that include physical skills (from flexibility, muscular endurance and power, joint range of motion), psychological state (mental health, concentration, motivation and their relation to this pandemic), artistic development (presence, rhythm, creativity). Human expert assessment will provide immediate results useful to plan teaching, training data for machine learning for behavioural tracking for personalised feedback. Project outcomes include best practice identification and new tools to mitigate social distancing effects on physical, psychological, artistic, and cognitive outcomes. This will be applicable to performing artists, patients with injury, and ultimately the general public.

Research activity codes: 5.7 PHYSICAL
Health categories: MUSCULOSKELETAL