User:FedericaBarberi/sandbox
Another classification of sensor configuration refers to the coordination of information flow between sensors [1]. These mechanisms provide a way to resolve conflicts or disagreements and to allow the development of dynamic sensing strategies. Sensors are in redundant (or competitive) configuration if each node delivers independent measures of the same properties. This configuration can be used in error correction when comparing information from multiple nodes. Redundant strategies are often used with high level fusions in voting procedures. [2] [3] Complementary configuration occurs when multiple information sources supply different information about the same features. This strategy is used for fusing information at raw data level within decision-making algorithms. Complementary features are typically applied in motion recognition tasks with Neural network [4],[5], Hidden Markov model[6] [7], Support-vector machine [8], clustering methods and other techniques [8] [7]. Cooperative sensor fusion uses the information extracted by multiple independent sensors to provide information that would not be available from single sensors. For example, sensors connected to body segments are used for the detection of the angle between them. Cooperative sensor strategy gives information impossible to obtain from single nodes. Cooperative information fusion can be used in motion recognition[9], gait analysis, motion analysis [10],[11],[12].
- ^ Durrant-Whyte, Hugh F. (2016). "Sensor Models and Multisensor Integration". The International Journal of Robotics Research. 7 (6): 97–113. doi:10.1177/027836498800700608. ISSN 0278-3649.
- ^ Li, Wenfeng; Bao, Junrong; Fu, Xiuwen; Fortino, Giancarlo; Galzarano, Stefano (2012). "Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion": 912–917. doi:10.1109/CCGrid.2012.144.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Fortino, Giancarlo; Gravina, Raffaele (2015). "Fall-MobileGuard: a Smart Real-Time Fall Detection System". doi:10.4108/eai.28-9-2015.2261462.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Tao, Shuai; Zhang, Xiaowei; Cai, Huaying; Lv, Zeping; Hu, Caiyou; Xie, Haiqun (2018). "Gait based biometric personal authentication by using MEMS inertial sensors". Journal of Ambient Intelligence and Humanized Computing. 9 (5): 1705–1712. doi:10.1007/s12652-018-0880-6. ISSN 1868-5137.
- ^ Dehzangi, Omid; Taherisadr, Mojtaba; ChangalVala, Raghvendar (2017). "IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion". Sensors. 17 (12): 2735. doi:10.3390/s17122735. ISSN 1424-8220.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Guenterberg, E.; Yang, A.Y.; Ghasemzadeh, H.; Jafari, R.; Bajcsy, R.; Sastry, S.S. (2009). "A Method for Extracting Temporal Parameters Based on Hidden Markov Models in Body Sensor Networks With Inertial Sensors". IEEE Transactions on Information Technology in Biomedicine. 13 (6): 1019–1030. doi:10.1109/TITB.2009.2028421. ISSN 1089-7771.
- ^ a b Parisi, Federico; Ferrari, Gianluigi; Giuberti, Matteo; Contin, Laura; Cimolin, Veronica; Azzaro, Corrado; Albani, Giovanni; Mauro, Alessandro (2016). "Inertial BSN-Based Characterization and Automatic UPDRS Evaluation of the Gait Task of Parkinsonians". IEEE Transactions on Affective Computing. 7 (3): 258–271. doi:10.1109/TAFFC.2016.2549533. ISSN 1949-3045.
- ^ a b Gao, Lei; Bourke, A.K.; Nelson, John (2014). "Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems". Medical Engineering & Physics. 36 (6): 779–785. doi:10.1016/j.medengphy.2014.02.012. ISSN 1350-4533.
- ^ Xu, James Y.; Wang, Yan; Barrett, Mick; Dobkin, Bruce; Pottie, Greg J.; Kaiser, William J. (2016). "Personalized Multilayer Daily Life Profiling Through Context Enabled Activity Classification and Motion Reconstruction: An Integrated System Approach". IEEE Journal of Biomedical and Health Informatics. 20 (1): 177–188. doi:10.1109/JBHI.2014.2385694. ISSN 2168-2194.
- ^ Chia Bejarano, Noelia; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Monticone, Marco; Ferrante, Simona (2015). "A Novel Adaptive, Real-Time Algorithm to Detect Gait Events From Wearable Sensors". IEEE Transactions on Neural Systems and Rehabilitation Engineering. 23 (3): 413–422. doi:10.1109/TNSRE.2014.2337914. ISSN 1534-4320.
- ^ Wang, Zhelong; Qiu, Sen; Cao, Zhongkai; Jiang, Ming (2013). "Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network". Sensor Review. 33 (1): 48–56. doi:10.1108/02602281311294342. ISSN 0260-2288.
- ^ Kong, Weisheng; Wanning, Lauren; Sessa, Salvatore; Zecca, Massimiliano; Magistro, Daniele; Takeuchi, Hikaru; Kawashima, Ryuta; Takanishi, Atsuo (2017). "Step Sequence and Direction Detection of Four Square Step Test". IEEE Robotics and Automation Letters. 2 (4): 2194–2200. doi:10.1109/LRA.2017.2723929. ISSN 2377-3766.