SMU Research Data Repository (RDR)
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Earable & IoT Dataset from: ERICA - Enabling real-time mistake detection & corrective feedback for free-weights exercises

posted on 2020-10-23, 06:05 authored by Meeralakshmi RADHAKRISHNAN, Ramesh Darshana Rathnayake KANATTA GAMAGE, ONG KOON HAN (SMU), Inseok Hwang, Archan MISRAArchan MISRA
Wearables or infrastructure sensors have been widely proposed for automated tracking and analysis of individual-level exercise activities. This dataset is collected as part of building a pervasive, low-cost digital personal trainer system, that supports fine-grained tracking of an individual’s free-weights exercises via a combination of (a) sensors on personal wireless ear-worn devices (‘earables’) and (b) inexpensive IoT sensors attached to exercise equipment (e.g., dumbbells). The dataset is comprised of sensor signals acquired from two 6-axis IMUs and contains a total of 324 samples for 3 different free-weight exercises performed by 27 individuals.


This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative.


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