{'fraction_non_alphanumeric': 0.011583011583011582, 'fraction_numerical': 0.0045045045045045045, 'mean_word_length': 5.533613445378151, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Playground Social Interaction Analysis using Bespoke Wearable Sensors for Tracking and Motion Capture

Unstructured play1 is considered important for the social, physical and cognitive development of children. Traditional observational research examining play behaviour at playtime (recess) has been hampered by challenges in obtaining reliable data and in processing sufficient quantities of that data to permit credible inferences to be drawn. The emergence of wearable wireless sensor technology makes it possible to study individual differences in childhood social behaviour based on collective movement patterns during playtime. In this work, we introduce a new method to enable simultaneous collection of GNSS/IMU data from a group of children interacting on a playground. We present a detailed description of system development and implementation before going on to explore methods of characterising social groups based on collective movement recording and analysis. A case study was carried out for a class of 7-8 year old children in their school playground during 10 episodes of unstructured play. A further 10 play episodes were monitored in the same space following the introduction of large, loose play materials. This study design allowed us to observe the effect of an environmental intervention on social movement patterns. Sociometric analysis was conducted for comparison and validation. This successful case study demonstrates that sensor based movement data can be used to explore children's social behaviour during naturalistic play.