Affiliation:
1. School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
2. School of Computer Science and Languages, Universidad de Malaga, Malaga, Spain
Abstract
Objective Motor and cognitive development share biological background within the prefrontal cortex and cerebellum. Monitoring motor development is relevant to identify children at risk of developmental delays. However, access to timely assessment is prevented by its availability and cost. Affordable motion capture technology may provide an alternative to human assessment. Methods MotorSense uses this technology to guide and assess children executing age-related developmental motor tasks. It incorporates advanced heuristics informed by pattern recognition principles based on the developmental sequences of motor skills. MotorSense was evaluated with 16 4–6 year-old children from a rural primary school. Results A total of 506 jumps, 2415 steps and 831 hops were analysed. The analysis illustrates MotorSense Accuracy (MA), recognising jump forward (89.96%), jump high (83.34%), jump sideway (85.63%), hop (74.58%) and jog (92.34%), is as good as the sensor's precision. The analysis of the tasks’ execution shows a high level of agreement between human and MotorSense's assessment on jump forward (91%), jump high (99%), jump sideway (93%), hop (94%) and jog (92%). Conclusions MotorSense helps address the shortage of affordable technologies to support the assessment of motor development using graded age-related developmental motor tasks. Furthermore, it could contribute towards the tele-detection of motor developmental delays.
Funder
EU H2020 Marie Skłodowska-Curie Career-FIT
Subject
Health Information Management,Computer Science Applications,Health Informatics,Health Policy
Cited by
2 articles.
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