Changed folder for JOSS

inst/paper.bib

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+@article{Kamisalic2018,
+abstract = {Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.},
+author = {Kamiŝali{\'{c}}, Aida and Fister, Iztok and Turkanovi{\'{c}}, Muhamed and Karakati{\^{c}}, Saŝo},
+doi = {10.3390/s18061714},
+issn = {14248220},
+journal = {Sensors (Switzerland)},
+keywords = {Intelligent analysis,Non-invasive,Sensor,Taxonomy,Visualization,Wrist-wearable device},
+number = {6},
+pmid = {29799504},
+title = {{Sensors and functionalities of non-invasive wrist-wearable devices: A review}},
+url = {www.mdpi.com/journal/sensors},
+volume = {18},
+year = {2018}
+}
+
+@misc{IDC2020,
+author = {IDC},
+booktitle = {2020},
+title = {{Shipments of Wearable Devices Leap to 125 Million Units, Up 35.1{\%} in the Third Quarter, According to IDC}},
+url = {https://www.idc.com/getdoc.jsp?containerId=prUS47067820},
+urldate = {2021-03-15},
+year = {2020}
+}
+
+@article{Bayoumy2021,
+author = {Bayoumy, Karim and Gaber, Mohammed and Elshafeey, Abdallah and Mhaimeed, Omar and Dineen, Elizabeth H and Marvel, Francoise A and Martin, Seth S and Muse, Evan D and Turakhia, Mintu P and Tarakji, Khaldoun G and Elshazly, Mohamed B},
+doi = {10.1038/s41569-021-00522-7},
+isbn = {0123456789},
+issn = {1759-5002},
+journal = {Nature Reviews Cardiology},
+month = {mar},
+title = {{Smart wearable devices in cardiovascular care: where we are and how to move forward}},
+url = {www.nature.com/nrcardio http://www.nature.com/articles/s41569-021-00522-7},
+year = {2021}
+}
+
+@article{Mishra2020,
+abstract = {Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81{\%}) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63{\%} of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.},
+author = {Mishra, Tejaswini and Wang, Meng and Metwally, Ahmed A and Bogu, Gireesh K and Brooks, Andrew W and Bahmani, Amir and Alavi, Arash and Celli, Alessandra and Higgs, Emily and Dagan-Rosenfeld, Orit and Fay, Bethany and Kirkpatrick, Susan and Kellogg, Ryan and Gibson, Michelle and Wang, Tao and Hunting, Erika M and Mamic, Petra and Ganz, Ariel B and Rolnik, Benjamin and Li, Xiao and Snyder, Michael P},
+doi = {10.1038/s41551-020-00640-6},
+issn = {2157846X},
+journal = {Nature Biomedical Engineering},
+number = {12},
+pages = {1208--1220},
+pmid = {33208926},
+title = {{Pre-symptomatic detection of COVID-19 from smartwatch data}},
+url = {https://doi.org/10.1038/s41551-020-00640-6},
+volume = {4},
+year = {2020}
+}

inst/paper.md

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+---
+title: 'Devicely: A Python package for reading, timeshifting and writing sensor data'
+tags:
+  - Python
+  - Wearables
+  - Sensors
+authors:
+  - name: Ariane Morassi Sasso^[[email protected]]
+    orcid: 0000-0002-3669-4599
+    affiliation: 1
+ - name: Jost Morgenstern^[[email protected]]
+    orcid: 0000-0000-0000-0000
+    affiliation: 1
+- name: Felix Musmann^[[email protected]]
+  orcid: 0000-0001-5365-0785
+  affiliation: 1
+affiliations:
+ - name: Digital Health Center, Hasso Plattner Institute, University of Potsdam
+   index: 1
+date: 08 March 2021
+bibliography: paper.bib
+---
+
+# Summary
+
+Wearable devices can track a multitude of parameters such as heart rate, body
+temperature, blood oxygen saturation, acceleration, blood glucose and much more
+`[@Kamisalic2018:2018]`. Moreover, they are becoming increasingly popular with a steeping
+increase in market presence in 2020 alone `[@IDC2020:2020]`. Applications for wearable
+devices varies from tracking cardiovascular risks `[@Bayoumy2021:2021]` to identifying
+COVID-19 onset `[@Mishra2020:2020]`. Therefore, there is a great need for scientists to
+easily go through data acquired from different wearables.
+In order to solve this problem and empower scientists working with biosignals,
+we developed the **devicely** package. It represents the data in a science-friendly
+format and lets scientists focus on what they want: the analysis of biosignals.
+
+# Statement of need
+
+Every wearable company has a different data format and reading this data is
+usually a challenge for scientists. Therefore, we developed the **devicely** package
+in order for researchers to read different sensor data in an easy and
+friendly way. We also added two methods to help with data _deidentification_, one
+is called timeshift and the other is a write method. The idea behind them is
+that you can timeshift all your time series to a different time from the one the
+actual experiments occurred and then write this new deidentified dataset back to
+the original data format. This will empower scientists to keep patient privacy
+and hopefully share more data to increase research reproducibility.
+
+# Design
+
+Different wearables come with different data formats which require different preprocessing steps.
+However, it should be easy for scientists to add data from a new wearable to an existing pipeline and easy for developers to add a new wearable to the **devicely** package.
+We achieved both by encapsulating data preparation for each wearable behind commmon methods: reading, deidentifying and writing data.
+
+After reading, the data is accessible through the reader in common formats such as dataframes.
+Deidentification is achieved by timeshifting the data, either by providing a shifting interval or randomly.
+For writing back deidentified data we focused on keeping a format that can be read again using the same reader class.
+In almost all cases, this is the same format as the wearable provides.
+This enables sharing data with the community while keeping patient anonymity. 
+
+# Functionalities
+
+All reader classes support three core functions: reading data created by wearables, timeshifting it and writing it back.
+To _read_ data, initialize the corresponding reader class, providing as a parameter a path to the data created by the wearable.
+If you are unsure how each wearable outputs its data you can find examples in the _Examples_ section of our documentation website. 
+
+After reading, you can access the data through the reader in convenient formats such as dictionaries and dataframes.
+
+After creating a reader object you can call _timeshift_ on it. This assures deidentification by shifting all time-related data points.
+If you would like to control the shifting interval, provide a parameter to _timeshift_.
+If no parameter is provided, the data is shifted by a random time interval to the past.
+
+You can write the timeshifted data back using the _write_ method.
+For all wearables, the written data can be read again using the same reader class.
+
+# Mention
+
+This package was used in the following paper:
+
+Morassi Sasso, A., Datta, S., Jeitler, M., Steckhan, N., Kessler, C. S.,
+Michalsen, A., Arnrich, B., & Böttinger, E. (2020).
+HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive
+Population. In M. Michalowski & R. Moskovitch (Eds.), Artificial Intelligence in
+Medicine. AIME 2020. Lecture Notes in Computer Science, volume 12299 (pp.
+325–335). Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_29
+
+GitHub: https://github.com/arianesasso/aime-2020
+
+Documentation: https://hpi-dhc.github.io/devicely
+
+# Acknowledgements
+
+We acknowledge contributions from Arpita Kappattanavar, Pascal Hecker, Bjarne Pfitzner and Lin
+Zhou during the genesis and testing of this package.
+
+# References