def test_parse_line_for_blank_line(self):
erroneous_line = ""
erroneous_line = erroneous_line.strip(" ")
erroneous_line = erroneous_line.strip("\r")
parseable_lines = []
lines_with_errors = []
repaired_lines = parse_line(erroneous_line, 68, parseable_lines, lines_with_errors)
self.assertEqual(True, len(lines_with_errors) == 0 and len(parseable_lines) == 0)
def test_parse_line_for_invalid_line(self):
erroneous_line = """J Kanjo, E; Younis, EMG; Sherkat, N Kanjo, Eiman; Younis, Eman M. G.; Sherkat, Nasser Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach INFORMATION FUSION English Article Multi sensor data fusion; Regression analysis sensor data; Multivariable regression; Affective computing; Physiological signals; Machine learning AIR-POLLUTION; NOISE EXPOSURE; HUMAN MOBILITY; GLOBAL BURDEN; FRAMEWORK; NETWORK; DISEASE; QUALITY; SYSTEMS; MODELS Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study 'in the wild' with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated 10.1016/j.scitotenv.2010.03.018; Stansfeld S, 2000, Rev Environ Health, V15, P43; Steinle S, 2015, SCI TOTAL ENVIRON, V508, P383, DOI 10.1016/j.scitotenv.2014.12.003; Steinle S, 2013, SCI TOTAL ENVIRON, V443, P184, DOI 10.1016/j.scitotenv.2012.10.098; Takahashi K, 2004, RO-MAN 2004: 13TH IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, PROCEEDINGS, P95, DOI 10.1109/ROMAN.2004.1374736; Valstar M., 2016, ARXIV160501600; van Kempen EEMM, 2002, ENVIRON HEALTH PERSP, V110, P307, DOI 10.1289/ehp.02110307; Van Poucke S, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0145791; Verberkmoes NJ, 2012, NETH HEART J, V20, P193, DOI 10.1007/s12471-012-0258-x; Wan-Hui W., 2009, COMP SCI INF ENG 200, V4; Wesolowski A, 2012, SCIENCE, V338, P267, DOI 10.1126/science.1223467; Younis E.M., 2015, INT J COMPUT APPL, V112 65 0 0 159 159 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion MAR 2018 40 18 31 10.1016/j.inffus.2017.05.005 14 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Computer Science FJ9AG WOS:000413059200002 gold 2018-02-07 """
erroneous_line = erroneous_line.strip(" ")
erroneous_line = erroneous_line.strip("\r")
parseable_lines = []
lines_with_errors = []
repaired_lines = parse_line(erroneous_line, 68, parseable_lines, lines_with_errors)
self.assertEqual(True, len(lines_with_errors) == 1)
def test_parse_line_for_valid_line(self):
line_to_parse = """J Kanjo, E; Younis, EMG; Sherkat, N Kanjo, Eiman; Younis, Eman M. G.; Sherkat, Nasser Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach INFORMATION FUSION English Article Multi sensor data fusion; Regression analysis sensor data; Multivariable regression; Affective computing; Physiological signals; Machine learning AIR-POLLUTION; NOISE EXPOSURE; HUMAN MOBILITY; GLOBAL BURDEN; FRAMEWORK; NETWORK; DISEASE; QUALITY; SYSTEMS; MODELS Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study 'in the wild' with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate. Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting. (C) 2017 The Authors. Published by Elsevier B.V. [Kanjo, Eiman; Sherkat, Nasser] Nottingham Trent Univ, Comp & Technol, Nottingham, England; [Younis, Eman M. G.] Menia Univ, Fac Comp & Informat, Al Minya, Egypt Kanjo, E (reprint author), Nottingham Trent Univ, Comp & Technol, Nottingham, England. [email protected]; [email protected]; [email protected] Sherkat, Nasser/0000-0003-1488-5682; kanjo, eiman/0000-0002-1720-0661 B11 Research unit at NTU [3452] This work was supported by the B11 Research unit at NTU, Ref 3452. Also, the authors would like to thank Dr. Caroline Langen-siepen for her help with the Ethical Approval Application and Dr. Ahmed Aldabbagh for his help in collecting the data. 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Fusion MAR 2018 40 18 31 10.1016/j.inffus.2017.05.005 14 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Computer Science FJ9AG WOS:000413059200002 gold 2018-02-07 """
line_to_parse = line_to_parse.strip(" ")
line_to_parse = line_to_parse.strip("\r")
parseable_lines = []
lines_with_errors = []
repaired_lines = parse_line(line_to_parse, 68, parseable_lines, lines_with_errors)
self.assertEqual(True, len(parseable_lines) == 1 and repaired_lines == 1)