+ Documentation: bibliography

Author: André Böni

docs/_static/Gaitalytics.bib

@@ -0,0 +1,130 @@
+
+@article{zeni_two_2008,
+	title = {Two simple methods for determining gait events during treadmill and overground walking using kinematic data},
+	volume = {27},
+	doi = {10.1016/j.gaitpost.2007.07.007},
+	abstract = {The determination of gait events such as heel strike and toe-off provide the basis for defining stance and swing phases of gait cycles. Two algorithms for determining event times for treadmill and overground walking based solely on kinematic data are presented. Kinematic data from treadmill walking trials lasting 20-45 s were collected from three subject populations (healthy young, n = 7; multiple sclerosis, n = 7; stroke, n = 4). Overground walking trials consisted of approximately eight successful passes over two force plates for a healthy subject population (n = 5). Time of heel strike and toe-off were determined using the two new computational techniques and compared to events detected using vertical ground reaction force (GRF) as a gold standard. The two algorithms determined 94\% of the treadmill events from healthy subjects within one frame (0.0167 s) of the GRF events. In the impaired populations, 89\% of treadmill events were within two frames (0.0334 s) of the GRF events. For overground trials, 98\% of events were within two frames. Automatic event detection from the two kinematic-based algorithms will aid researchers by accurately determining gait events during the analysis of treadmill and overground walking. © 2007 Elsevier B.V. All rights reserved.},
+	journal = {Gait and Posture},
+	author = {Zeni, J. A. and Richards, J. G. and Higginson, J. S.},
+	month = may,
+	year = {2008},
+	pages = {710--714},
+}
+
+@article{martinez_pyomeca_2020,
+	title = {pyomeca: An Open-Source Framework for Biomechanical Analysis},
+	volume = {5},
+	issn = {2475-9066},
+	shorttitle = {pyomeca},
+	doi = {10.21105/joss.02431},
+	language = {en},
+	number = {53},
+	journal = {Journal of Open Source Software},
+	author = {Martinez, Romain and Michaud, Benjamin and Begon, Mickael},
+	month = sep,
+	year = {2020},
+	pages = {2431},
+}
+
+@article{hollman_normative_2011,
+	title = {Normative Spatiotemporal Gait Parameters in Older Adults},
+	volume = {34},
+	issn = {0966-6362},
+	doi = {10.1016/j.gaitpost.2011.03.024},
+	number = {1},
+	journal = {Gait Posture},
+	author = {Hollman, John H. and McDade, Eric M. and Petersen, Ronald C.},
+	month = may,
+	year = {2011},
+	pages = {111--118},
+}
+
+@article{hoyer_xarray_2017,
+	title = {xarray: N-D labeled Arrays and Datasets in Python},
+	volume = {5},
+	copyright = {Copyright (c) 2017 The Author(s)},
+	issn = {2049-9647},
+	shorttitle = {xarray},
+	doi = {10.5334/jors.148},
+	language = {en},
+	number = {1},
+	journal = {Journal of Open Research Software},
+	author = {Hoyer, Stephan and Hamman, Joe},
+	month = apr,
+	year = {2017},
+	pages = {10--10},
+}
+
+@article{michaud_ezc3d_2021,
+	title = {ezc3d: An easy C3D file I/O cross-platform solution for C++, Python and MATLAB},
+	volume = {6},
+	copyright = {http://creativecommons.org/licenses/by/4.0/},
+	issn = {2475-9066},
+	shorttitle = {ezc3d},
+	doi = {10.21105/joss.02911},
+	number = {58},
+	journal = {JOSS},
+	author = {Michaud, Benjamin and Begon, Mickaël},
+	month = feb,
+	year = {2021},
+	pages = {2911},
+}
+
+@article{carter_exploration_2024,
+	title = {An exploration of the agreement, inter- and intra-rater reliability, and reproducibility of three common methods used to measure minimum toe clearance with optical motion capture systems under three shoe conditions},
+	volume = {113},
+	issn = {0966-6362},
+	doi = {10.1016/j.gaitpost.2024.08.006},
+	journal = {Gait \& Posture},
+	author = {Carter, Sylvester},
+	month = sep,
+	year = {2024},
+	pages = {534--542},
+}
+
+@article{schulz_new_2017,
+	title = {A new measure of trip risk integrating minimum foot clearance and dynamic stability across the swing phase of gait},
+	volume = {55},
+	issn = {00219290},
+	doi = {10.1016/j.jbiomech.2017.02.024},
+	language = {en},
+	journal = {Journal of Biomechanics},
+	author = {Schulz, Brian W.},
+	month = apr,
+	year = {2017},
+	pages = {107--112},
+}
+
+@article{muller_interpreting_2016,
+	title = {Interpreting Spatiotemporal Parameters, Symmetry, and Variability in Clinical Gait Analysis},
+	doi = {10.1007/978-3-319-30808-1_35-1},
+	language = {en},
+	urldate = {2024-11-12},
+	journal = {Handbook of Human Motion},
+	author = {Gouelle, Arnaud and Mégrot, Fabrice},
+	collaborator = {Müller, Bertram and Wolf, Sebastian I. and Brueggemann, Gert-Peter and Deng, Zhigang and McIntosh, Andrew and Miller, Freeman and Selbie, William Scott},
+	year = {2016},
+	pages = {1--20},
+}
+
+@article{mckinney_pandas_2011,
+	title = {pandas: a foundational Python library for data analysis and statistics},
+	volume = {14},
+	shorttitle = {pandas},
+	number = {9},
+	journal = {Python for high performance and scientific computing},
+	author = {McKinney, Wes},
+	year = {2011},
+	note = {Publisher: Seattle},
+	pages = {1--9},
+}
+
+@inproceedings{mckinney_data_2010,
+	title = {Data structures for statistical computing in {Python}.},
+	volume = {445},
+	booktitle = {{SciPy}},
+	author = {McKinney, Wes},
+	year = {2010},
+	note = {Issue: 1},
+	pages = {51--56},
+}

docs/_static/tables/features.csv

@@ -17,18 +17,18 @@ PhaseTimeSeriesFeatures,stand_min,"deg, Nmm, N, V*"
 ,swing_median,"deg, Nmm, N, V*"
 ,swing_amplitude,"deg, Nmm, N, V*"
 SpatialFeatures,step_length,"cm, mm✝"
-,stride_length,"cm, mm✝"
-,step_length,"cm, mm✝"
-,step_width,"cm, mm✝"
-,minimal_toe_clearance,"cm, mm✝"
+,stride_length\ :footcite:p:`hollman_normative_2011`,"cm, mm✝"
+,step_length\ :footcite:p:`hollman_normative_2011`,"cm, mm✝"
+,step_width\ :footcite:p:`hollman_normative_2011`,"cm, mm✝"
+,minimal_toe_clearance\ :footcite:p:`schulz_new_2017`,"cm, mm✝"
 ,AP_margin_of_stability,"cm, mm✝☨"
 ,ML_margin_of_stability,"cm, mm✝☨"
 TemporalFeatures,cycle_duration,sec
-,double_support,%
-,single_support,%
+,double_support\ :footcite:`muller_interpreting_2016`,%
+,single_support\ :footcite:`muller_interpreting_2016`,%
 ,foot_off,%
 ,opposite_foot_off,%
 ,opposite_foot_contact,%
 ,stride_time,sec
 ,step_time,sec
-,cadence,steps/min
+,cadence\ :footcite:p:`hollman_normative_2011`,steps/min

docs/conf.py

@@ -19,7 +19,13 @@
 
 extensions = ['sphinx.ext.autodoc',
               'sphinx_codeautolink',
-              'sphinxcontrib.images',]
+              'sphinxcontrib.images',
+              'sphinxcontrib.bibtex',]
+
+bibtex_bibfiles = ['_static/Gaitalytics.bib']
+
+bibtex_encoding = 'utf-8'
+bibtex_reference_style = "author_year"
 
 templates_path = ['_templates']
 exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']

docs/usage/data_loading.rst

@@ -129,7 +129,7 @@ Following code snipped illustrates how to load a c3d file with the corresponding
 
 ..
 
-| The load_trial function will return a Trial object which contains all the information of the c3d file. Internally Gaitalytics uses the pyomeca [1]_, ezc3d [2]_ and xarray [3]_ libraries to load and store the data.
+| The load_trial function will return a Trial object which contains all the information of the c3d file. Internally Gaitalytics uses the pyomeca\ :footcite:p:`martinez_pyomeca_2020`, ezc3d\ :footcite:p:`michaud_ezc3d_2021` and xarray\ :footcite:p:`hoyer_xarray_2017` libraries to load and store the data.
 | The object returned is a :class:`gaitalytics.model.Trial`. It contains three types of data.
 
     1. Markers -> time series form the Point section of the c3d
@@ -139,15 +139,7 @@ Following code snipped illustrates how to load a c3d file with the corresponding
 Additionally, the object contains the events which are stored in the c3d file.
 
 .. rubric:: References
-
-.. [1] R. Martinez, B. Michaud, and M. Begon, “`pyomeca`: An Open-Source Framework for Biomechanical Analysis,” Journal of Open Source Software, vol. 5, no. 53, p. 2431, Sep. 2020, doi: 10.21105/joss.02431.
-
-
-.. [2] B. Michaud and M. Begon, “ezc3d: An easy C3D file I/O cross-platform solution for C++, Python and MATLAB,” JOSS, vol. 6, no. 58, p. 2911, Feb. 2021, doi: 10.21105/joss.02911.
-
-
-.. [3] S. Hoyer and J. Hamman, “xarray: N-D labeled Arrays and Datasets in Python,” Journal of Open Research Software, vol. 5, no. 1, Art. no. 1, Apr. 2017, doi: 10.5334/jors.148.
-
+.. footbibliography::
 
 
 

docs/usage/event_detection.rst

@@ -5,7 +5,7 @@
 Event Detection
 ===============
 
-To detect gait events Gaitalytics currently only supports a marker based function based on Zeni et al. 2008 [1]_ paper.
+To detect gait events Gaitalytics currently only supports a marker based function based on :footcite:t:`zeni_two_2008` paper.
 Further it provides helping functions to find errors in the detected gait events and store the events into an existing c3d file.
 
 Detection
@@ -32,7 +32,7 @@ Following snipped illustrates the usage of the detection function:
 
 ..
 
-The function returns a pandas [2]_ DataFrame with the following columns:
+The function returns a pandas\ :footcite:p:`mckinney_pandas_2011` DataFrame with the following columns:
 
 .. csv-table::
     :file: ../_static/tables/event_table.csv
@@ -96,10 +96,7 @@ Alternatively the function can be used to store the events into the existing c3d
 
 
 .. rubric:: References
-.. [1] J. A. Zeni, J. G. Richards, and J. S. Higginson, “Two simple methods for determining gait events during treadmill and overground walking using kinematic data,” Gait and Posture, vol. 27, pp. 710–714, May 2008, doi: 10.1016/j.gaitpost.2007.07.007.
-.. [2] W. McKinney, “pandas: a foundational Python library for data analysis and statistics,” Python for high performance and scientific computing, vol. 14, no. 9, pp. 1–9, 2011.
-
-
+.. footbibliography::
 
 
 

docs/usage/features.rst

@@ -38,6 +38,10 @@ Following features can be extracted from the segmented data:
 | ✝ Depending on the unit of the configured entity in the c3d file.
 | ☨ Feature only calculated if a center of mass marker is present in the c3d file and configured in the config file.
 
+.. rubric:: References
+.. footbibliography::
+
+