Datasets for Bilateral Lower-Limb Neuromechanical Signals in Able-Bodied and Impaired Individuals with Wearable and Ambient Sensors (BLISS)

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data archive 13703 disk0/00/00/14/25 2025-04-28 14:59:02 2026-04-17 11:12:31 2025-04-28 14:59:02 data_collection show Ahmed Samer sa2930@bath.ac.uk 0000-0002-5721-8866 University of Bath TRUE Mohanna Mohamed 19P6218@eng.asu.edu.eg Ain Shams University FALSE Martinez Hernandez Uriel umh21@bath.ac.uk 0000-0002-9922-7912 University of Bath; University of Sheffield FALSE Awad Mohammed mohammed.awad@eng.asu.edu.eg 0000-0002-0367-0187 Ain Shams University FALSE Mansour Alia alia.hassan@med.asu.edu.eg 0000-0002-8806-9429 Ain Shams University FALSE DataCollector Ahmed Samer sa2930@bath.ac.uk 0000-0002-5721-8866 University of Bath DataCollector Mohanna Mohamed 19P6218@eng.asu.edu.eg Ain Shams University Supervisor Martinez Hernandez Uriel umh21@bath.ac.uk 0000-0002-9922-7912 University of Bath; University of Sheffield Datasets for Bilateral Lower-Limb Neuromechanical Signals in Able-Bodied and Impaired Individuals with Wearable and Ambient Sensors (BLISS) FB0010 GM0130 GS0020 dept_elec_eng gait, locomotion, biomechanics, motion capture, electromyography, foot drop, impairment, benchmark To address the challenges in human activity recognition (HAR) and advance research in lower-limb assistive devices and machine learning (ML) for HAR, we introduce the BLISS dataset (Bilateral Lower-Limb Neuromechanical Signals). This benchmark dataset includes bilateral EMG and limb kinematics from wearable sensors for 21 subjects, encompassing both healthy individuals and those with mild to severe gait abnormalities. The selected abnormalities, which are clinically prevalent, include reduced knee flexion due to stiffness and/or overweight conditions, and dorsiflexor weakness resulting from stroke, multiple sclerosis and lumbar radiculopathy. Subjects perform free ground-level walking in an uncontrolled environment across multiple trials, each representing a complete gait bout. Additionally, marker-based motion capture and force plates provide ground truth estimates of limb positions and ground reaction forces. Comprehensive analyses estimate angular accelerations, velocities, positions, and torques at individual joints. The dataset is fully annotated for gait cycle phases: loading response (LR), mid-stance (MST), terminal stance (TS), pre-swing (PSW), and swing (SW),. This dataset complements existing benchmarks by offering detailed guidelines for sensor modality selection, analysis, and annotation, and balancing data between healthy and impaired subjects. 2025-04-28 University of Bath public RightsHolder University of Bath Self-funded cent_cbio The subject's weight and height were measured using analog devices. The motion capture cameras and force plates were calibrated prior to the experiment. 15 wearable sensors (inertial measurement (IMU) plus electromyography (EMG) integrated units) were attached to the participants' lower limbs. 26 passive reflective markers were attached non-invasively on top of skeletal landmarks according to the IOR lower body automated identification of markers (AIM) model. The attachment of sensors and markers is visually illustrated in the additional metadata files associated with the dataset. The walking procedure involved moving forward for about 5 meters. Each subject performed this trial approximately 50 times, with administrated breaks to prevent fatigue. The number of gait bouts in each direction was roughly equal, with each bout covering nearly 5 meters. Subjects walked at their self-selected speeds, and trials with pauses or trips within the force plate area were discarded. Data collection took about 3 hours for healthy subjects and extended to 4-5 hours for those with severe impairments. The dataset was annotated using a visual3D. EMG and IMU signals were filtered thoroughly according to literature recommendations. Visual3D software analyzed raw Qualysis signals, assigning joint reference frames and calculating kinematic and dynamic data. The processed data was later segmented into analysis windows to create additional feature-based datasets. Each window's features were computed, including 780 features from 30 sensors. MATLAB was used for gait analysis, comparing joint kinematic/kinetic data to normative data and processing EMG envelopes and gait cycle data. Data from the test subjects was completely anonymized using unique codes. Qualysis software (Qualysis, Göteborg, Sweden) 2023; Visual3D software (HAS-motion; Kingston, Ontario) 2023; MATLAB software (Mathworks, Natick, Massachusetts, United States) 2022; 15 Trigno Avanti™ Sensors; Delsys, Natick, MA, USA. The motion capture system used (Qualysis, Göteborg, Sweden) includes 14 cameras (2 Miqus and 12 Arqus cameras) to track passive markers. Ground reaction forces are recorded using four force plates (BMS400600; Amti, Watertown, MA, USA) fixed to the lab floor. en 3 10.15125/BATH-01425 https://doi.org/10.1109/TSMC.2025.3601893 pub open Dataset