Catalog of Regulatory Science Tools to Help Assess New Medical Devices
This regulatory science tool is an open-access dataset containing raw inertial measurement unit (IMU) and sensorized insole data from individuals with Parkinson’s Disease (PD) and age-matched controls synchronized to a gait walkway reference system that can serve as a tool to assess novel algorithms deriving gait metrics through wearables and explore measurable connections between gait, PD severity, PD progression, and controls, thus leading to future clinical enhancements and treatments for patients.
Technical Description
This open-access dataset contains raw inertial measurement unit (IMU) and sensorized insole data from individuals with Parkinson’s Disease (PD) and age-matched controls synchronized to a gait walkway reference system. IMU data include 3-degree of freedom (DOF) acceleration, rotational velocity, and magnetic field strength, as well as orientation for each of 13 different sensors placed at various positions on the participant’s body; sensor insole data include absolute pressure from 16 sensors in each insole and 3-DOF acceleration and rotational velocity; walkway data include 2D position and relative pressure for each active sensor during every footfall registered on the walkway. Frame-by-frame annotation of participant actions during gait and balance tasks was incorporated into the dataset using video cameras synchronized to all systems. To expand the utility of this dataset, all data are associated with demographic information and clinical evaluations (e.g., medications, DBS-status, Movement Disorder Society – Unified Parkinson’s Disease Rating Scale [MDS-UPDRS] scores). An example of how these data can be used to compare insole-derived gait metrics to a walkway reference system is shown in Watkinson et al., 2024.
Intended Purpose
This dataset RST can serve as a tool to assess novel algorithms deriving gait metrics through wearables and be used to establish distributions and variability of wearable-derived gait metrics in clinical and control populations. It can also be used to identify digital biomarkers relevant to the PD population for use in clinical trials and/or home monitoring. This dataset, in addition to being a critical tool for evaluating the emerging wearable market, also allows for measurable connections between gait, PD severity, and PD progression, thus leading to future clinical enhancements and treatments for patients.
Testing
A quality assurance process was implemented to ensure consistent and accurate data collection. Specifically, a core experimental protocol was developed and reviewed by each researcher in consultation with clinical collaborators. This protocol detailed the steps involved in system-set-up, participant preparation, data collection, and data processing and export. For those modalities that required more manual processing (i.e., walkway data and video annotation data), a second researcher was tasked with review and verification of the primary processing results to ensure proper footfall identification and conformance with the agreed upon annotation event definitions.
After final files were created (MAT and CSV files), a data quality control process on those files was implemented through a series of automatic and manual checks that involved visual inspection of data streams by a researcher to identify data cleanliness and validity issues. Specifically, this process identified issues related to:
- Processing and inclusion of all tasks in the MAT and CSV files
- Integrity of individual IMU sensor data, including the identification of sensor values outside of expected ranges
- Unexpected missing columns of data
- Unexpected large gaps in data
- Expectations around the variable type for a given data variable (e.g., annotations contain no numeric data, IMU data does not contain any errant non-numeric data)
- Video annotation events and expectations surrounding inclusion of specific events for specific tasks
- Alignment of all data, with a particular focus on walkway pressure and sensor insole force data
If an issue was identified, a second researcher was assigned to review and address the issue. Once an issue was addressed, the relevant files were put through the data quality control process again to confirm that all issues were resolved before the final MAT file and final set of CSV files were generated and included in the dataset.
Limitations
There are several systems from which data were captured to curate this dataset. Synchronization of these systems and accurate data alignment are critical to the overall utility of this tool. While data synchronization between the walkway and IMU system was found to be within 0.02 seconds, alignment between the walkway and sensor insoles was less consistent. To resolve the observed inconsistency, comprehensive testing was conducted to define the synchronization strategy among the systems. This allowed for fine-tuning the alignment of the sensor insole data with that of the walkway. Despite these enhancements, analysts should proceed with caution when interpreting data that depends on the precise alignment of sensor insoles with walkway/IMU systems.
While the raw data output from the walkway enables calculation of any spatiotemporal metric requiring knowledge of timing and position of footfalls, the use of relative pressure as opposed to absolute pressure may limit accuracy of derived kinetic gait metrics.
While tasks were intentionally designed to evoke natural movement within near real-world contexts, it is important to note that data were collected in a controlled lab-based environment. Consequently, the extrapolation of findings from these data to real-world scenarios may be somewhat restricted.
Supporting Documentation
Additional background information, data descriptions and details on data access processes can be found here: https://doi.org/10.7303/syn52540892
Example analysis using data:
Watkinson et al., " Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson’s disease," 2024 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Coronado Springs, Florida, 2024
Contact
Tool Reference
- In addition to citing relevant publications please reference the use of this tool using RST24NO01.1