A Dataset of Clinical Gait Signals with Wearable Sensors from Healthy, Neurological, and Orthopedic Cohorts.

Open access, clean, annotated databases are key for future significant advances in gait quantification with inertial sensors. This multi-pathology and clinically annotated dataset provides 1356 gait trials from 260 participants equipped with four inertial measurement units placed on the head, lower back, and dorsal part of each foot. Participants followed a standardized protocol: standing still, walking 10 meters, turning around, walking back 10 meters, and stopping. It results in a large human walking dataset with over 11 hours of gait time series data. The quality is ensured by the documentation and metadata provided. The study population encompasses healthy individuals and patients with neurological (parkinson disease, cerebrovascular accident, radiation-induced leukoencephalopathy and chemotherapy-induced peripheral neuropathy) or orthopedic (hip osteoarthritis, knee osteoarthritis and anterior cruciate ligament injury) conditions. For each pathology, the most relevant clinical or radioclinical score has been calculated to provide insight into the gravity of the disease. This dataset can be used to study kinematic parameters, gait cycles time series, and various indicators for quantifying gait in routine clinical practice.