The paper proposes a free dataset, available at the following link1, named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, Depth videos and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject’s performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise.

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

In this paper, a self-contained procedure to estimate the vertical, medial-lateral, and anterior-posterior displacement of a single sacrum-worn inertial measurement unit (IMU) is presented, which can be related to the human body center of mass (CoM) displacement during treadmill walking through an adaptation of the sacral marker method. Further, a magnetometer-free custom sensor-fusion algorithm for orientation estimation is proposed alongside a practical alignment procedure to refer relative IMU orientation estimation to a ground-fixed reference frame. Twelve healthy subjects performed two trials of treadmill walking at 3, 4 and 5 km/h for 150 s, with a sacrum-worn IMU. Orientation and displacement estimations were then compared with those obtained from an optoelectronic measurement system. Roll, pitch and yaw angles showed root mean square errors (RMSE) lower than 2 deg for walking trials at 3, 4 and 5 km/h, with Pearson’s correlation coefficient higher than 0.90 for each angle. Displacement accuracy was evaluated in terms of peak-to-trough distances and RMSE. Mean errors resulted lower than 1 mm for each axis of interest and for each gait speed, with RMSE not higher than 2.5 mm. The proposed off-line algorithm can be used in low-budget and infrastructure-free environments, to achieve reliable CoM displacement estimation during cyclic activities such as treadmill walking.