Spine posture during repetitive lifting is one of the main risk factors for low-back injuries in the occupational sector. It is thus critical to design appropriate intervention strategies for training workers to improve their posture, reducing load on the spine during lifting. The main approach to train safe lifting to workers has been educational; however, systematic reviews and meta-analyses have shown that this approach does not improve lifting movement nor reduces the risk of low back injury. One of the main limitations of this approach lies in the amount, quality and context of practice of the lifting movement. In this article, first we argue for integrating psychologically-grounded perspectives of practice design in the development of training interventions for safe lifting. Principles from deliberate practice and motor learning are combined and integrated. Given the complexity of lifting, a training intervention should occur in the workplace and invite workers to repeatedly practice/perform the lifting movement with the clear goal of improving their lifting-related body posture. Augmented feedback has a central role in creating the suitable condition for achieving such intervention. Second, we focus on spine bending as risk factor and present a pilot study examining the benefits and boundary conditions of different feedback modalities for reducing bending during lifting. The results showed how feedback modalities meet differently key requirements of deliberate practice conditions, i.e., feedback has to be informative, individualized and actionable. Following the proposed approach, psychology will gain an active role in the development of training interventions, contributing to finding solutions for a reduction of risk factors for workers.

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In vibrotactile design, it can be beneficial to communicate with potential users about the desired properties of a product. However, such users' expectations would need to be translated into physical vibration parameters. In everyday life, humans are frequently exposed to seat vibration. Humans have learned to intuitively associate specific labels (e.g., "tingling") with specific vibrations. Thus, the aim of this article is to identify the most common sensory-perceptual attributes and their relationships to vibration parameters. First, we generalized everyday-life seat vibration into sinusoidal, amplitude-modulated sinusoidal, white Gaussian noise and impulse-like vibrations. Subsequently, the (peak) level, (center/carrier) frequency, bandwidth, modulation frequency and exponential decay rate parameters of these vibrations were systematically varied depending on the signal type. A free association task was conducted to reveal the most common sensory-perceptual attributes for each vibration. After aggregating similar attributes, the 21 most frequently occurring attributes were utilized in a second experiment to rate their suitability for describing each vibration stimulus. Principal component analysis guided the selection of six attribute groups, which can be represented by "up and down," "tingling," "weak," "repetitive," "uniform" and "fading." The observed relationships between vibration parameters and attribute ratings are suitable for future model building.

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In today's urban environment inhabitants are permanently exposed to elevated noise levels, which are mostly dominated by traffic noise. The current electrification of vehicles might affect the traffic noise in city centers. The aim of this work was to determine the pedestrian reaction, the annoyance and the warning effect of electric vehicle sounds. For this purpose the differences in the perceived annoyance, warning effect and detection time were investigated with perception studies. Furthermore the sound level of a full speed-scaling of an approaching vehicle starting from 0 km/h at the critical distance is nearly 10 dB below the level of a constant speed of 10 km/h. Therefore variants of electric vehicle sounds were generated, in which a constant level is used below 5 or 10 km/h. The results show that the change of the speed-scaling influences the detection time enormously. In this study, Artificial neural network (ANN) is used as an indexing tool to imitate subjective perceptions, because in some further work the results of artificial neural networks show great correlation with the assessments of subjects in listening tests. Through the use of ANN, a flexible model can be developed which can predict the annoyance or the warning effect of future electric vehicle sounds.