HighlightsWe developed a wearable platform (KiD) designed for tracking movements of children We establish the potential of KiD as stand-alone movement measurement platformWe demonstrate successful classification of different types of movements using KiD Accelerations collected using KiD compare well with those of optical MoCap systems
This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Comitato Etico Regionale della Regione (39/2020) the Italian Ministry of Health under Protocol No. 45831.
Bluetooth-enabled wearables can be linked to form synchronized networks to provide insightful and representative data that is exceptionally beneficial in healthcare applications. However, synchronization can be affected by inevitable variations in the component’s performance from their ideal behavior. Here, we report an application-level solution that embeds a Neural network to analyze and overcome these variations. The neural network examines the timing at each wearable node, recognizes time shifts, and fine-tunes a virtual clock to make them operate in unison and thus achieve synchronization. We demonstrate the integration of multiple Kinematics Detectors to provide synchronized motion capture at a high frequency (200 Hz) that could be used for performing spatial and temporal interpolation in movement assessments. The technique presented in this work is general and independent from the physical layer used, and it can be potentially applied to any wireless communication protocol.
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