fall detection

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 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.

A specially designed waist-worn device with accelerometer, gyroscope, and pressure sensor was utilized to collect information about 18 ADLs and 16 fall types. The falls protocol has been performed in our lab to replicate realistic situations that typically affect workers and older people. In contrast to other datasets that are accessible to the public, we included a new task in the falls, syncope, since it has a high mortality rate among the elderly and is linked to falls. As such, we must take it into account and include it in our fall detection system.

This is a dataset for TCS-Fall.
A total of 20 volunteers were invited to take part in the experiment. Each volunteer performed hundreds of falls and non-falls.
All fall data and non-fall data are stored in binary files that can be parsed by Python or matlab.

Current radar fall detection techniques based on deep learning (DL) networks are often too complex for real-time detection. This paper proposes a real-time fall detection approach by reducing the complexity of the DL networks and the UWB radar hardware requirements. A multi-indoor scene behaviour dataset of 40 subjects is established using K-band UWB radar. A sliding window-based dataflow augmentation method is proposed to augment and balance the given datasets.