Molecular Communications (MC), transferring information via chemical signals, holds promise for transformative healthcare applications within the Internet of Bio-Nano Things (IoBNT) framework. Despite promising advances toward practical MC systems, progress has been constrained by experimental testbeds that are costly, difficult to customize, and require labor-intensive fabrication.
This dataset comprises high-resolution 3-axis accelerometer recordings collected from human participants performing distinct hand gestures, intended for training gesture-based assistive interfaces. Each participant’s raw motion signals are individually organized, enabling both user-specific and generalizable model development. The dataset includes time-series accelerometer data, along with a feature-augmented version containing extracted statistical and temporal descriptors such as RMS, Jerk, Entropy, and SMA.
1.Dataset overview
This dataset is designed to support the HAR task of this study. Covered by (a) walking, (b) running, (c) going upstairs, (d) going downstairs, (e) high leg lifting, (f) skipping rope, and (g) rhombic extension Seven types of human movement data.
The files D.xlsx, E.xlsx, H.xlsx, R.xlsx, S.xlsx, U.xlsx, and W.xlsx are one-dimensional time series data of seven sports, with a data length of 400, and the number of data categories of 7.
The CSV data files in the ZIP archive are analytical datasets extracted and processed from the RUG-EGO-FALL dataset, intended to support fall detection research using wearable first-person perspective devices. The data includes visual motion information for each video frame, calculated using the ORB (Oriented FAST and Rotated BRIEF) feature point algorithm in combination with the Lucas-Kanade optical flow method.
This dataset contains data collected from 10 participants using a liquid metal data glove equipped with 12 sensors. The data is primarily aimed at facilitating research in the fields of human - computer interaction, motion tracking, and related areas. The use of a liquid metal data glove offers unique advantages in terms of flexibility and sensitivity, providing rich and detailed information about finger movements.
This dataset contains heartbeat and electromyography (EMG) signals recorded from the brachioradialis muscle under different conditions: rest and induced fatigue. It is intended for research in biomechanics, fatigue detection, and physiological signal processing. The data provide insights into muscle activity and heart rate variations, making it valuable for applications in biomedical engineering and human performance analysis.
PassengerEEG is a brain-signal dataset designed to study how human passengers perceive and cognitively respond to potential traffic hazards in highly automated vehicles (AVs). As AVs increasingly replace human drivers, understanding passenger cognition becomes essential for improving vehicle safety and adaptive decision-making.
This dataset is derived from the RUG-EGO-FALL dataset and has been processed for feature extraction to support fall detection research. We applied Oriented FAST and Rotated BRIEF (ORB) for keypoint extraction and used optical flow methods to compute motion features, including per-frame X and Y pixel displacement values, representing movement speed and direction.
Endurance running is a popular activity due to its accessibility. However, participation is sometimes prevented by individuals experiencing respiratory problems. Monitoring breathing with body area networks can tackle these issues by tracking respiration during exercise and providing immediate, guiding feedback. Common breathing guidance systems rely on observational data from past breath cycles and consequently inherit disruptively lagging guidance interventions if breathing pattern suddenly change.
Monitoring sweat rate provides valuable insights into an individual’s risk of dehydration, thermoregulation efficiency, and electrolyte balance, particularly relevant for workers in hot environments, athletes, and individuals with certain metabolic conditions. Traditional methods for measuring sweat rates, such as gravimetric techniques, are labor-intensive and unsuitable for real-time monitoring.
