Abstract
The proliferation of low power and low cost continuous sensing technology is enabling new and innovative applications in wearables and Internet of Things (IoT). At the same time, new applications are creating challenges to maintain real-time response in a resource-constrained device, while maintaining an acceptable performance. In this paper, we describe an IMU (Inertial Measurement Unit) sensor-based generalized hand gesture recognition system, its applications, and the challenges involved in implementing the algorithm in a resource-constrained device. We have implemented a simple algorithm for gesture spotting that substantially reduces the false positives. The gesture recognition model was built using the data collected from 52 unique subjects. The model was mapped onto Intel® QuarkTM SE Pattern Matching Engine, and field-tested using 8 additional subjects achieving 92% performance.
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Iyer, D. et al. (2016). Generalized Hand Gesture Recognition for Wearable Devices in IoT: Application and Implementation Challenges. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_26
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DOI: https://doi.org/10.1007/978-3-319-41920-6_26
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