typo

Author: philerooski

paper/paper.md

@@ -69,7 +69,7 @@ For more information, including how to use custom preprocessing functions as par
 Default feature sets are provided for both time and frequency domains. For a full list of features and their definitions, see the _Feature Definitions_ vignette.
 
 # Comparison with Related Packages
-There exist a number of other packages that were built to work with data collected from sensors in a digital health research setting. HeartPy [@paul_van_gent_2019] is a Python package focused on extracting features from PPG data collected through PPG or camera sensors. GaitPy [@czech_2019] is a Python package designed to extract gait features collected by an accelerometer sensor on the lower back. It includes functionality to identify gait bouts (for measurements collected during free-living conditions), implementations of a number of published algorithms for deriving gait features, and a convenience function to visualize gait events. PDkit [@saez-pons_2019] is the package with the most overlap in functionality with mhealthtools. Focused on Parkinson's disease, PDkit aims to provide a broad suite of tools, ranging from convenience functions for loading data from popular Parkinson's digital health datasets, feature extraction on measurements collected during commonly performed tasks in Parkinson's studies, and even functions which attempt to map sensor measurements to Unified Parkinson's Disease Rating Scale (UPDRS) Part III scores, a clinically validated metric for measuring Parkinsons's disease progression.
+There exist a number of other packages that were built to work with data collected from sensors in a digital health research setting. HeartPy [@paul_van_gent_2019] is a Python package focused on extracting features from PPG data collected through PPG or camera sensors. GaitPy [@czech_2019] is a Python package designed to extract gait features collected by an accelerometer sensor on the lower back. It includes functionality to identify gait bouts (for measurements collected during free-living conditions), implementations of a number of published algorithms for deriving gait features, and a convenience function to visualize gait events. PDkit [@saez-pons_2019] is the package with the most overlap in functionality with mhealthtools. Focused on Parkinson's disease, PDkit aims to provide a broad suite of tools, ranging from convenience functions for loading data from popular Parkinson's digital health datasets, feature extraction on measurements collected during commonly performed tasks in Parkinson's studies, and even functions which attempt to map sensor measurements to Unified Parkinson's Disease Rating Scale (UPDRS) Part III scores, a clinically validated metric for measuring Parkinson's disease progression.
 
 In comparison to these packages, mhealthtools is focused solely on the feature extraction process and tries to offer a transparent and generalizable framework that makes minimal assumptions about how the sensor data was collected and how it should be processed. This is reflected in a function design that caters to both ease of use and extensibility. mhealthtools adopts robust, functional programming practices, such as predictable error handling, function interfaces, and output formats, making the package a good fit for ETL workloads and streaming data. The default features for IMU sensors were chosen to work well in both clinical and free-living contexts, but are otherwise signal agnostic and not chosen with one particular phenotype or activity type in mind (task specific feature extraction functions, such as `get_walk_features`, may make use of default argument values to these otherwise task agnostic feature sets). For these reasons, mhealthtools is useful for both quickly generating feature sets for exploratory analysis as well as coordinating the extraction of features across multiple packages for more advanced data pipelines.