Sakorn Mekruksavanich1, Narit Hnoohom2, Anuchit Jitpattanakul3,4,*
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2669-2686, 2023, DOI:10.32604/iasc.2023.038549
Abstract Recognition of human activity is one of the most exciting aspects of time-series classification, with substantial practical and theoretical implications. Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments. Consequently, researchers have demonstrated considerable passion for developing cutting-edge deep learning systems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts. This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called SenPyramidNet and motion information from wearable… More >
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