Although sensory input is continuous, information must be combined over time to guide action and cognition, leading to the proposal of temporal sampling windows. A number of studies have suggested that a 10-Hz sampling window might be involved in the “frame rate” of visual processing. To investigate this, we tested the ability of participants to localize and enumerate 1 or 2 visual flashes presented either at near-threshold or full-contrast intensities, while recording magnetoencephalography. The inter-stimulus interval (ISI) between the 2 flashes was varied across trials. Performance in distinguishing between 1 and 2 flashes was linked to the alpha frequency, both at the individual level and trial-by-trial. Participants with a higher resting-state alpha peak frequency showed the greatest improvement in performance as a function of ISI within a 100-ms time window, while those with slower alpha improved more when ISI exceeded 100 ms. On each trial, correct enumeration (1 vs. 2) performance was paired with faster pre-stimulus instantaneous alpha frequency. Our results suggest that visual sampling/processing speed, linked to peak alpha frequency, is both an individual trait and can vary in a state-dependent manner.

Numerous technological solutions have been proposed to promote piano learning and teaching, but very few with market success. We are convinced that users’ needs should be the starting point for an effective and transdisciplinary development process of piano-related Tactile Internet with Human-in-the-Loop (TaHIL) applications. Thus, we propose to include end users in the initial stage of technology development. We gathered insights from adult piano teachers and students through an online survey and digital interviews. Three potential literature-based solutions have been visualized as scenarios to inspire participants throughout the interviews. Our main findings indicate that potential end users consider posture and body movements, teacher–student communication, and self-practice as crucial aspects of piano education. Further insights resulted in so-called acceptance requirements for each scenario, such as enabling meaningful communication in distance teaching, providing advanced data on a performer’s body movement for increased well-being, and improving students’ motivation for self-practice, all while allowing or even promoting artistic freedom of expression and having an assisting instead of judging character. By putting the users in the center of the fuzzy front end of technology development, we have gone a step further toward concretizing TaHIL applications that may contribute to the routines of piano teaching and learning.

In this article, we present a comprehensive scheme for the quality assessment of compressed vibrotactile signals with human assessors. Inspired by the multiple stimulus test with hidden reference and anchors (MUSHRA) from the audio domain, we designed a method in which each compressed signal is compared to its original signal and rated on a numerical scale. For each signal tested, the hidden reference and two anchor signals are used to validate the results and provide assessor screening criteria. Differing from previous approaches, our method is hierarchically structured and strictly timed in a sequential manner to avoid experimental confounds and provide precise psychophysical assessments. We validated our method in an experiment with 20 human participants in which we compared two state-of-the-art lossy codecs. The results show that, with our approach, the performance of different codecs can be compared effectively. Furthermore, the method also provides a measure of subjective quality at different data compression rates. The proposed procedure can be easily adapted to evaluate other vibrotactile codecs.

The Tactile Internet aims to advance human-human and human-machine interactions that also utilize hand movements in real, digitized, and remote environments. Attention to elderly generations is necessary to make the Tactile Internet age inclusive. We present the first age-representative kinematic database consisting of various hand gesturing and grasping movements at individualized paces, thus capturing naturalistic movements. We make this comprehensive database of kinematic hand movements across the adult lifespan (CeTI-Age-Kinematic-Hand) publicly available to facilitate a deeper understanding of intra-individual–focusing especially on age-related differences–and inter-individual variability in hand kinematics. The core of the database contains participants’ hand kinematics recorded with wearable resistive bend sensors, individual static 3D hand models, and all instructional videos used during the data acquisition. Sixty-three participants ranging from age 20 to 80 years performed six repetitions of 40 different naturalistic hand movements at individual paces. This unique database with data recorded from an adult lifespan sample can be used to advance machine-learning approaches in hand kinematic modeling and movement prediction for age-inclusive applications.

Kinematic data is a valuable source of movement information that provides insights into the health status, mental state, and motor skills of individuals. Additionally, kinematic data can serve as biometric data, enabling the identification of personal characteristics such as height, weight, and sex. In CeTI-Locomotion, four types of walking tasks and the 5 times sit-to-stand test (5RSTST) were recorded from 50 young adults wearing motion capture (mocap) suits equipped with Inertia-Measurement-Units (IMU). Our dataset is unique in that it allows the study of both intra- and inter-participant variability with high quality kinematic motion data for different motion tasks. Along with the raw kinematic data, we provide the source code for phase segmentation and the processed data, which has been segmented into a total of 4672 individual motion repetitions. To validate the data, we conducted visual inspection as well as machine-learning based identity and action recognition tests, achieving 97% and 84% accuracy, respectively. The data can serve as a normative reference of gait and sit-to-stand movements in healthy young adults and as training data for biometric recognition.

This dataset (named CeTI-Age-Kinematics) fills the gap in existing motion capture (MoCap) data by recording kinematics of full-body movements during daily tasks in an age-comparative sample with 32 participants in two groups: older adults (66–75 years) and younger adults (19–28 years). The data were recorded using sensor suits and gloves with inertial measurement units (IMUs). The dataset features 30 common elemental daily tasks that are grouped into nine categories, including simulated interactions with imaginary objects. Kinematic data were recorded under well-controlled conditions, with repetitions and well-documented task procedures and variations. It also entails anthropometric body measurements and spatial measurements of the experimental setups to enhance the interpretation of IMU MoCap data in relation to body characteristics and situational surroundings. This dataset can contribute to advancing machine learning, virtual reality, and medical applications by enabling detailed analyses and modeling of naturalistic motions and their variability across a wide age range. Such technologies are essential for developing adaptive systems for applications in tele-diagnostics, rehabilitation, and robotic motion planning that aim to serve broad populations.