human machine interfaces
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2022 ◽  
Vol 430 ◽  
pp. 132635
Author(s):  
Wenlong Wu ◽  
Yukun Ren ◽  
Tianyi Jiang ◽  
Likai Hou ◽  
Jian Zhou ◽  
...  

2022 ◽  
Vol 8 (2) ◽  
Author(s):  
Yiming Liu ◽  
Chunki Yiu ◽  
Zhen Song ◽  
Ya Huang ◽  
Kuanming Yao ◽  
...  

The closed-loop HMI system could compliantly interface with human body for teleoperating various robotics with haptic feedback.


Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 505
Author(s):  
Niclas Hoffmann ◽  
Samet Ersoysal ◽  
Gilbert Prokop ◽  
Matthias Hoefer ◽  
Robert Weidner

In modern times, the collaboration between humans and machines increasingly rises, combining their respective benefits. The direct physical support causes interaction forces in human–machine interfaces, whereas their form determines both the effectiveness and comfort of the collaboration. However, their correct detection requires various sensor characteristics and remains challenging. Thus, this paper presents a developed low-cost sensor pad working with a silicone capsule and a piezoresistive pressure sensor. Its measurement accuracy is validated in both an isolated testing environment and a laboratory study with four test subjects (gender-balanced), and an application integrated in interfaces of an active upper-body exoskeleton. In the material-testing machine, it becomes apparent that the sensor pad generally features the capability of reliably determining normal forces on its surface until a certain threshold. This is also proven in the real application, where the measurement data of three sensor pads spatially embedded in the exoskeletal interface are compared to the data of an installed multi-axis load cell and a high-resolution flexible pressure map. Here, the consideration of three sensor pads potentially enables detection of exoskeletal support on the upper arm as well as “poor” fit conditions such as uneven pressure distributions that recommend immediate system adjustments for ergonomic improvements.


2021 ◽  
Vol 5 (12) ◽  
pp. 84
Author(s):  
Yiyuan Wang ◽  
Luke Hespanhol ◽  
Martin Tomitsch

In recent years, researchers and manufacturers have started to investigate ways to enable autonomous vehicles (AVs) to interact with nearby pedestrians in compensation for the absence of human drivers. The majority of these efforts focuses on external human–machine interfaces (eHMIs), using different modalities, such as light patterns or on-road projections, to communicate the AV’s intent and awareness. In this paper, we investigate the potential role of affective interfaces to convey emotions via eHMIs. To date, little is known about the role that affective interfaces can play in supporting AV–pedestrian interaction. However, emotions have been employed in many smaller social robots, from domestic companions to outdoor aerial robots in the form of drones. To develop a foundation for affective AV–pedestrian interfaces, we reviewed the emotional expressions of non-humanoid robots in 25 articles published between 2011 and 2021. Based on findings from the review, we present a set of considerations for designing affective AV–pedestrian interfaces and highlight avenues for investigating these opportunities in future studies.


Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8380
Author(s):  
Mateusz Szumilas ◽  
Michał Władziński ◽  
Krzysztof Wildner

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.


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