Accelerometer data of individuals with Huntington's disease and healthy controls from in-clinic assessment

Participants with Huntington's Disease (HD) and age-matched healthy controls (HC) were recruited as part of a study designed to evaluate the feasibility, acceptability, and utility of wearable activity monitors in people with early-mid stage HD. Tri-axial accelerometer signals were collected using wrist-worn devices (GENEActiv, Activinsights; 43 × 40 × 13 mm; weight: 16 g; 100 Hz sampling rate) during an in-clinic assessment. Participants completed a set of standardized laboratory-based functional activities, reflecting independent activities of daily living. The in-clinic assessment lasted 60-90 minutes per participant. The in-clinic assessments were video-recorded, and these videos were subsequently used in the labeling procedure of the accelerometer signals. The dataset contains a csv file for each participant with the 3 axis accelerometer recording, as well as labels for walking detection. An anonymized metadata table is also provided with demographic information and clinical scores. Files description:Each csv file is named with a serial number representing a participant. Each file contains 3 axis of accelerometer recordings (i.e., X, Y, Z) that was recorded in a sample rate of 100 Hz.The forth column contains labels for each sample according to the following: 1=walking , 0=non-walking, -1 = cannot be marked (due to the patients being obscured from the camera). Additional metadata file is provided that include the following information regarding each participant in the columns: 

Huntington's Disease patient/healthy control (HD=1, HC=2)

Sex (Male=1, Female=2)

Age

Birthday

Height

Weight

TMS (Total Motor Score)

FA (Functional Assessment)

TFC (Total functional capacity)

Additional relevant information is available in the paper. 

Kindly cite of the following paper when using this data:

D. Schwartz, L. Quinn, N. E. Fritz, L. M. Muratori, J. M. Hausdorff, and R. G. Bachrach, Detecting daily living gait amid huntington’s disease chorea using a foundation deep learning model, 2024. arXiv:2412.11286 [cs.CV]. url: https://arxiv.org/abs/2412.11286.