Claudia Carpineti, Vincenzo Lomonaco, Luca Bedogni, Marco Di Felice, Luciano Bononi
备注：
Pre-print of the accepted version for the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 2018
Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smart-phones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.
Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models
University of California San Diego（加州大学圣地亚哥分校）；McGill University（麦吉尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；University of Pennsylvania（宾夕法尼亚大学）；Duke Kunshan University（杜克昆山大学）
Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images
Nepal Applied Mathematics and Informatics Institute for research (NAAMII)（尼泊尔应用数学与信息学研究所（NAAMII））；Center for Analytical Sciences, Kathmandu Institute of Applied Sciences (KIAS)（加德满都应用科学学院分析科学中心）；Central Department of Chemistry, Tribhuvan University（特里布万大学化学系）
AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones
Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Southern University of Science and Technology Hospital（南方科技大学医院）
