The mhealthx open source software was originally developed to extract low-level features from voice, accelerometry, screen tapping, and other tasks performed on the mPower Parkinson app (see below). The above schematic diagram shows how flexible and modular the nipype-based pipeline environment is, running different software packages to extract and store features from different sensor data for statistical analysis.
Extraction and analysis of high-dimensional feature sets to characterize vocal production, speech patterns, and speech content is a promising direction for biomarker identification. Features characterizing vocal production are independent of speech content itself, and can provide objective measures of motor difficulties as well as objective means of assessing relevant psychiatric states, such as mood and anxiety. Features related to speech patterns and content provide additional opportunities to characterize more complex emotional and cognitive states, as well as issues related to processing information and expressing thoughts. For voice feature extraction, mhealthx currently makes primary use of the openSMILE package.
We have not been actively maintaining mhealthx for some time, but intend to resume development efforts to update and expand its feature extraction capabilities for use with MindLogger and wearable sensor data we will be collecting from future projects.