Extracting repeated unknown maneuvers or patterns preformed by a human operator in a cyber-physical systems can lead to better understanding of the behavior of the human operator who is controlling or sharing tasks with dynamical systems. These repeated maneuvers can be extracted by analyzing the inputs and outputs of the human operator using control theory and data mining tools. In this paper, we introduce geometrical shape-based pattern detection approach for input-output data. A pattern or a maneuver is defined as the maximum repeated behavior in time series trajectory data that is generated from the operator’s inputs and outputs. A two-phase algorithm is developed in this paper, the first phase consists of trajectory segmentation, creating segment fingerprint, clustering, and symbolic representations. The second phase of the proposed algorithm is pattern extraction phase, which is inherited from the motif finding algorithms in time series data and DNA sequences.
M.A.Schumann, D.Drusinsky, J.B.Michael and D.Wijesekera, Modeling human-in-the-loop security analysis and decision-making processes, IEEE Transactions on Software Engineering40(2) (2014), 154–166.
2.
K.Driggs-Campbell, V.Shia and R.Bajcsy, Improved driver modeling for human-in-the-loop vehicular control, in IEEE International Conference on Robotics and Automation, 2015.
3.
S.B.Bortolami, K.R.Duda and N.K.Borer, Markov analysis of human-in-the-loop system performance, in IEEE Aerospace Conference Proceedings, 2010.
4.
J.Li, A.H.Miller, S.Chopra, M.A.Ranzato and J.Weston, Dilaogue Learning With Human -In-The-Loop, International Conference on Learning Representations, 2017.
5.
B.Han, J.Hamm and J.Sim, Personalized video summarization with human in the loop, in IEEE Workshop on Applications of Computer Vision, WACV, 2011.
6.
G.Schirner, D.Erdogmus, K.Chowdhury and T.Padir, The future of human-in-the-loop cyber-physical systems, Computer46(1) (2013), 36–45.
7.
W.Li, D.Sadigh, S.S.Sastry and S.A.Seshia, Synthesis for human-in-the-loop control systems, in Lecture Notes in Computer Science8413 (2014), 470–484.
8.
J.Z.Self, R.K.Vinayagam, J.T.Fry and C.North, Bridging the gap between user intention and model parameters for human-in-the-loop data analytics, in Proceedings of the Workshop on Human-In-the-Loop Data Analytics - HILDA ”16, 2016.
9.
M.A.Zamani and E.Oztop, Simultaneous human-robot adaptation for effective skill transfer, in Proceedings of the 17th International Conference on Advanced Robotics, ICAR, 2015.
10.
A.C.Koenig and R.Riener, The human in the loop, in Neurorehabilitation Technology, Second Edition, 2016.
11.
K.Hirota, Casual communication between robots and humans using fuzzy interruption technology on internet, in 5th International Symposium on Computational Intelligence and Intelligent Informatics (ISCIII), 2011.
12.
R.Nikolaidis and G.Weinberg, Playing with the masters: A model for improvisatory musical interaction between robots and humans, in Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 2010.
13.
H.A.Samani and A.D.Cheok, Probability of love between robots and humans, in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2010.
14.
T.Nomura and K.Kawakami, Relationships between robots’ self-disclosures and humans’ anxiety toward robots, in Proceedings - 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011, 2011.
15.
M.Mistry and S.Schaal, Representation and Control of the Task Space in Humans and Humanoid Robots, in Representation and Control of the Task Space in Humans and Humoid Robots, 2015.
16.
M.Pantic, A.Pentland, A.Nijholt and T.S.Huang, Human computing and machine understanding of human behavior: A survey, in Artifical Intelligence for Human Computing. Springer, (2007), pp. 47–71.
17.
T.-C.Fu, A review on time series data mining, Engineering Applications of Artificial Intelligence24(1) (2011), 164–181.
18.
J.Lin, S.Williamson, K.Borne and D.DeBarr, Pattern recognition in time series, Advances in Machine Learning and Data Mining for Astronomy1 (2012), 617–645.
19.
P.Patel, E.Keogh, J.Lin and S.Lonardi, Mining motifs in massive time series databases, in IEEE International Conference on Data Mining IEEE, (2002), pp. 370–377.
20.
A.Mueen, E.Keogh, Q.Zhu, S.Cash and B.Westover, Exact discovery of time series motifs, in Proceedings of the SIAM International Conference on Data Mining, SIAM, (2009), pp. 473–484.
21.
B.Chiu, E.Keogh and S.Lonardi, Probabilistic discovery of time series motifs, in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining ACM, (2003), pp. 493–498.
22.
Y.Mohammad and T.Nishida, Data Mining for Social Robotics. Springer, 2015.
23.
S.Dodge, R.Weibel and E.Forootan, Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects, Computers, Environment and Urban Systems33(6) (2009), 419–434.
24.
D.J.Berndt, Finding patterns in time series: A dynamic programming approach, Advances in Knowledge Discovery and Data Mining, 1996.
25.
T.Nakamura, K.Taki, H.Nomiya, K.Seki and K.Uehara, A shape-based similarity measure for time series data with ensemble learning, Pattern Analysis and Applications16(4) (2013), 535–548.
26.
E.Keogh, S.Chu, D.Hart and M.Pazzani, Segmenting time series: A survey and novel approach, Data Mining in Time Series Databases57 (2004), 1–22.
27.
Y.Kawahara and M.Sugiyama, Change-point detection in time-series data by direct density-ratio estimation, in Proceedings of the SIAM International Conference on Data Mining, SIAM, (2009), pp. 389–400.
28.
J.V.Braun and H.-G.Muller, Statistical methods for dna sequence segmentation, Statistical Science13(2) (1998), 142–162.
29.
K.-S.Fu and J.Mui, A survey on image segmentation, Pattern Recognition13(1) (1981), 3–16.
30.
H.Yoon and C.Shahabi, Robust time-referenced segmentation of moving object trajectories, in Eighth IEEE International Conference on Data Mining, ICDM' 08, IEEE, (2008), 1121–1126.
J.C.Bezdek, R.Ehrlich and W.Full, Fcm: The fuzzy c-means clustering algorithm, Computers & Geosciences10(2-3) (1984), 191–203.
34.
J.Nayak, B.Naik and H.Behera, Fuzzy c-means (fcm) clustering algorithm: A decade review from 2000 to 2014, in Computational Intelligence in Data Mining-Volume 2. Springer, 2015, pp. 133–149.
35.
J.Lin, E.Keogh, S.Lonardi and B.Chiu, A symbolic representation of time series, with implications for streaming algorithms, in Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. ACM, (2003), pp. 2–11.
36.
J.Lin, E.Keogh, W.Li and S.Lonardi, Experiencing sax: A novel symbolic representation of time series, Data Mining and Knowledge Discovery15(2) (2007), 107.
37.
M.Kumar and A.Kalia, Preprocessing and symbolic representation of stock data, in 2012 Second International Conference on Advanced Computing Communication Technologies, (2012), pp. 83–88.
38.
I.Rigoutsos and A.Floratos, Combinatorial pattern discovery in biological sequences: The teiresias algorithm, Bioinformatics14(1) (1998), 55–67.
