The CMT1A-BioStampNPoint2023 dataset provides data from a wearable sensor accelerometry study conducted for studying gait, balance, and activity in 15 individuals with Charcot-Marie-Tooth disease Type 1A (CMT1A). In addition to individuals with CMT1A, the dataset also includes data for 15 controls that also went through the same in-clinic study protocol as the CMT1A participants with a substantial fraction (9) of the controls also participating in the in-home study protocol. For the CMT1A participants, data is provided for 15 participants for the baseline visit and associated home recording duration and, additionally, for a subset of 12 of these participants data is also provided for a 12-month longitudinal visit and associated home recording duration. For controls, no longitudinal data is provided as none was recorded. The data were acquired using lightweight MC 10 BioStamp NPoint sensors (MC 10 Inc, Lexington, MA), three of which were attached to each participant for gathering data over a roughly one day interval. For additional details, see the description in the "README.md" included with the dataset.

The dataset contains data from wearable sensors and clinical data. The wearable sensor data was acquired using wearable sensors and the clinical data was extracted from the clinical record. The sensor data has not been processed per-se but the start of the recording time has been anonymized to comply with HIPPA requirements. Both the sensor data and the clinical data passed through a Python program for the aforementioned time anonymization and for standard formatting. Additional details of the time anonymization are provided in the file "README.md" included with the dataset.

A program for unzipping files is required to unzip the files provided in zip format. There are numerous programs for this purpose and the functionality is also provided by several operating systems. 

Once unzipped the files are in text format and can be opened with any program/editor capable of reading text files. Most of the text files have comma-separated-values (CSV) which can be opened in any spreadsheet program such as LibreOffice Calc. The Python programming language commonly used for data science and machine learning applications also provides support for reading in CSV files.