Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

Doing whitefella research in blackfella communities in Australia : decolonizing method in sports related research

There is much for non-Indigenous researchers to consider when researching in Indigenous contexts. This paper is a story of discovery for two researchers working on a project with the Indigenous Sports Program section of the Australian Sports Commission. It documents the slow, meticulous and sometimes clumsy steps taken to gain access to communities and conduct research guided by a social justice ethic. The research was successful in that eventually it was possible to develop the trust of individuals and some of the Indigenous communities more broadly, so that information could be gathered and given within the context of shared understandings and mutual interest. However, it is the turbulent journey, filled as it is, with latent tendencies, privileged assumptions and eventually reflexive readings of the data, which remains the focus of this paper. Tentative recommendations are offered to those wishing to advance this politically and epistemologically challenging approach to culturally based research

Perceiving the interactive body in dance: Enhancing kinesthetic empathy through art objects

Through a consideration of audience experience of embodiment in contemporary dance performance, this project used kinesthetic empathy as a theoretical construct to inform choreographic decision-making. The research outcome challenged the traditional performer/audience relationship through an interactive dance performance work entitled Planets. This acted as a platform that allowed both audience and performer to collaboratively listen to, process and form movement in a shared kinesthetic state. This connection was enabled through the distribution of interactive art objects, which responded to the shifting proximity between performer and audience. The performance was thus experienced through following a shared goal as instigated by the interactive technology. Through practice-led research, knowledge from kinesthetic empathy, embodied cognition and the mirror neuron system were used to develop the project’s aim in encouraging interactive audiences to engage in movement. This aim influenced studio explorations of movement through an enquiry into the kinesthetic self in dance. Investigations used movement quality, tension, mobility and acceleration to access a familiar movement vocabulary appropriate for a broad interactive audience. This informed the role of the researcher as performer. Planets was developed as a collaborative project between Michael Smith and interactive visual designer Andy Bates and performed over three nights at the Ars Electronica Festival 2014 in Linz, Austria. Supported by documented footage from Planets and audience responses to the performances, this paper draws together the theoretical underpinnings behind the development of the work and includes the experiential perspective of the performer.

Feminism and art in 2013 : a panel discussion

Brisbane-based artist and Founding Co-Director of LEVEL artist run initiative Courtney Coombs reports on a one day forum about Feminism and Art held at LEVEL on International Womens Day 2013. LEVEL is focused on providing opportunities for female visual artists and generating dialogue around gender and arts practice. To listen to podcasts from the event visit http://www.ciprecinct.qut.edu.au/archive/2013/feminism-art.jsp