Are nursing home employees ready for the technical evolution? German-wide survey on the status quo of affinity for technology and technology interaction.

Background: Technological devices can support nursing home employees; however, their perspective is not sufficiently studied. Our aims were thus to (a) examine affinity for technology and technology interaction and related sociodemographic confounders, as well as (b) detect possible requirements and boundary conditions relevant for the development and implementation of assistive technologies among nursing home employees. Methods: We conducted an online survey between May and July of 2022 among 200 nursing home employees in Germany. The survey included two questionnaires, that is, Affinity for Technology Interaction (ATI) and Affinity for Technology-Electronic Devices (TA-EG; subscales TA-EG-Enthusiasm, TA-EG-Competence, TA-EG-Positive Consequences, and TA-EG-Negative Consequences), as well as sociodemographic variables, that is, age, gender, professional groups, education/graduation level. We carried out factorial variance and multiple regression analyses. Results: There were differences between age groups in ATI (lower score with increasing age) and between gender, age, and professional group in TA-EG (lower score for females, participants with higher ages, and nursing home managers). Predictors of ATI were age and professional group, predictors of TA-EG, TA-EG-Enthusiasm, and TA-EG-Competence were gender, age, and professional group. Predictors of TA-EG-Positive Consequences were education and professional group. Conclusions: We observed rather high affinity for technology and technology interaction values overall, and particularly for nursing home employees compared to managers. Significant predictors for technology affinity and interaction may have important implications, for example the perspectives of nursing home employees and managers should be considered separately in the technological design, development, and implementation process. Furthermore, an open dialogue between all stakeholders should be encouraged to increase the probability of actual technology use.

Interactive and incremental learning of spatial object relations from human demonstrations.

Humans use semantic concepts such as spatial relations between objects to describe scenes and communicate tasks such as "Put the tea to the right of the cup" or "Move the plate between the fork and the spoon." Just as children, assistive robots must be able to learn the sub-symbolic meaning of such concepts from human demonstrations and instructions. We address the problem of incrementally learning geometric models of spatial relations from few demonstrations collected online during interaction with a human. Such models enable a robot to manipulate objects in order to fulfill desired spatial relations specified by verbal instructions. At the start, we assume the robot has no geometric model of spatial relations. Given a task as above, the robot requests the user to demonstrate the task once in order to create a model from a single demonstration, leveraging cylindrical probability distribution as generative representation of spatial relations. We show how this model can be updated incrementally with each new demonstration without access to past examples in a sample-efficient way using incremental maximum likelihood estimation, and demonstrate the approach on a real humanoid robot.

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review.

Exoskeletons are a promising tool to support individuals with a decreased level of motor performance. Due to their built-in sensors, exoskeletons offer the possibility of continuously recording and assessing user data, for example, related to motor performance. The aim of this article is to provide an overview of studies that rely on using exoskeletons to measure motor performance. Therefore, we conducted a systematic literature review, following the PRISMA Statement guidelines. A total of 49 studies using lower limb exoskeletons for the assessment of human motor performance were included. Of these, 19 studies were validity studies, and six were reliability studies. We found 33 different exoskeletons; seven can be considered stationary, and 26 were mobile exoskeletons. The majority of the studies measured parameters such as range of motion, muscle strength, gait parameters, spasticity, and proprioception. We conclude that exoskeletons can be used to measure a wide range of motor performance parameters through built-in sensors, and seem to be more objective and specific than manual test procedures. However, since these parameters are usually estimated from built-in sensor data, the quality and specificity of an exoskeleton to assess certain motor performance parameters must be examined before an exoskeleton can be used, for example, in a research or clinical setting.

Designing Prosthetic Hands With Embodied Intelligence: The KIT Prosthetic Hands.

Hand prostheses should provide functional replacements of lost hands. Yet current prosthetic hands often are not intuitive to control and easy to use by amputees. Commercially available prostheses are usually controlled based on EMG signals triggered by the user to perform grasping tasks. Such EMG-based control requires long training and depends heavily on the robustness of the EMG signals. Our goal is to develop prosthetic hands with semi-autonomous grasping abilities that lead to more intuitive control by the user. In this paper, we present the development of prosthetic hands that enable such abilities as first results toward this goal. The developed prostheses provide intelligent mechatronics including adaptive actuation, multi-modal sensing and on-board computing resources to enable autonomous and intuitive control. The hands are scalable in size and based on an underactuated mechanism which allows the adaptation of grasps to the shape of arbitrary objects. They integrate a multi-modal sensor system including a camera and in the newest version a distance sensor and IMU. A resource-aware embedded system for in-hand processing of sensory data and control is included in the palm of each hand. We describe the design of the new version of the hands, the female hand prosthesis with a weight of 377 g, a grasping force of 40.5 N and closing time of 0.73 s. We evaluate the mechatronics of the hand, its grasping abilities based on the YCB Gripper Assessment Protocol as well as a task-oriented protocol for assessing the hand performance in activities of daily living. Further, we exemplarily show the suitability of the multi-modal sensor system for sensory-based, semi-autonomous grasping in daily life activities. The evaluation demonstrates the merit of the hand concept, its sensor and in-hand computing systems.

An Embedded, Multi-Modal Sensor System for Scalable Robotic and Prosthetic Hand Fingers.

Grasping and manipulation with anthropomorphic robotic and prosthetic hands presents a scientific challenge regarding mechanical design, sensor system, and control. Apart from the mechanical design of such hands, embedding sensors needed for closed-loop control of grasping tasks remains a hard problem due to limited space and required high level of integration of different components. In this paper we present a scalable design model of artificial fingers, which combines mechanical design and embedded electronics with a sophisticated multi-modal sensor system consisting of sensors for sensing normal and shear force, distance, acceleration, temperature, and joint angles. The design is fully parametric, allowing automated scaling of the fingers to arbitrary dimensions in the human hand spectrum. To this end, the electronic parts are composed of interchangeable modules that facilitate the mechanical scaling of the fingers and are fully enclosed by the mechanical parts of the finger. The resulting design model allows deriving freely scalable and multimodally sensorised fingers for robotic and prosthetic hands. Four physical demonstrators are assembled and tested to evaluate the approach.

Does a Passive Unilateral Lower Limb Exoskeleton Affect Human Static and Dynamic Balance Control?

Exoskeletons are wearable devices closely coupled to the human, which can interact with the musculoskeletal system, e. g., to augment physical and functional capabilities. A main prerequisite for the development and application of exoskeletons is to investigate the human-exoskeleton interaction, particularly in terms of potential inferences with human motor control. Therefore, the purpose of the present study was to investigate whether a passive unilateral lower limb exoskeleton has an impact on static and dynamic reactive balance control. Eleven healthy subjects (22.9 ± 2.5 years, five females) volunteered for this study and performed three different balance tasks: bipedal standing, single-leg standing, and platform perturbations in single-leg standing. All the balance tasks were conducted with and without a passive unilateral lower limb exoskeleton, while force plates and a motion capture system were used to capture the center of pressure mean sway velocity and the time to stabilization, respectively. Dependent t-tests were separately run for both static balance tests, and a repeated-measure analysis of variance with factors exoskeleton and direction of perturbation was calculated for the dynamic reactive balance task. The exoskeleton did not significantly influence postural sway in bipedal stance. However, in single-leg stance, the mediolateral mean sway velocity of the center of pressure was significantly shorter for the exoskeleton condition. For the dynamic reactive balance task, the participants tended to regain stability less quickly with the exoskeleton, as indicated by a large effect size and longer time to stabilization for all directions of perturbation. In summary, the study showed that the exoskeleton provided some assistive support under static conditions, which however may disappear when sufficient stability is available (bipedal stance). Besides, the exoskeleton tended to impair dynamic reactive balance, potentially by impeding adequate compensatory adjustments. These are important findings with strong implications for the future design and application of exoskeletons, emphasizing the significance of taking into account the mechanisms of human motor control.

Temporal Context Influences the Perceived Duration of Everyday Actions: Assessing the Ecological Validity of Lab-Based Timing Phenomena.

Timing is key to accurate performance, for example when learning a new complex sequence by mimicry. However, most timing research utilizes artificial tasks and simple stimuli with clearly marked onset and offset cues. Here we address the question whether existing interval timing findings generalize to real-world timing tasks. In this study, animated video clips of a person performing different everyday actions were presented and participants had to reproduce the main action's duration. Although reproduced durations are more variable then observed in laboratory studies, the data adheres to two interval timing laws: Relative timing sensitivity is constant across durations (scalar property), and the subjective duration of a previous action influenced the current action's perceived duration (temporal context effect). Taken together, this demonstrates that laboratory findings generalize, and paves the way for studying interval timing as a component of complex, everyday cognitive performance.

Self-aligning exoskeleton hip joint: Kinematic design with five revolute, three prismatic and one ball joint.

Kinematic compatibility is of paramount importance in wearable robotic and exoskeleton design. Misalignments between exoskeletons and anatomical joints of the human body result in interaction forces which make wearing the exoskeleton uncomfortable and even dangerous for the human. In this paper we present a kinematically compatible design of an exoskeleton hip to reduce kinematic incompatibilities, so called macro- and micro-misalignments, between the human's and exoskeleton's joint axes, which are caused by inter-subject variability and articulation. The resulting design consists of five revolute, three prismatic and one ball joint. Design parameters such as range of motion and joint velocities are calculated based on the analysis of human motion data acquired by motion capture systems. We show that the resulting design is capable of self-aligning to the human hip joint in all three anatomical planes during operation and can be adapted along the dorsoventral and mediolateral axis prior to operation. Calculation of the forward kinematics and FEM-simulation considering kinematic and musculoskeletal constraints proved sufficient mobility and stiffness of the system regarding the range of motion, angular velocity and torque admissibility needed to provide 50 % assistance for an 80 kg person.

A whole-body support pose taxonomy for multi-contact humanoid robot motions.

A taxonomy of whole-body support poses promotes representation, recognition, and generation of multi-contact humanoid robot motions.

The KIT Motion-Language Dataset.

Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input. However, although there have been years of research in this area, no standardized and openly available data set exists to support the development and evaluation of such systems. We, therefore, propose the Karlsruhe Institute of Technology (KIT) Motion-Language Dataset, which is large, open, and extensible. We aggregate data from multiple motion capture databases and include them in our data set using a unified representation that is independent of the capture system or marker set, making it easy to work with the data regardless of its origin. To obtain motion annotations in natural language, we apply a crowd-sourcing approach and a web-based tool that was specifically build for this purpose, the Motion Annotation Tool. We thoroughly document the annotation process itself and discuss gamification methods that we used to keep annotators motivated. We further propose a novel method, perplexity-based selection, which systematically selects motions for further annotation that are either under-represented in our data set or that have erroneous annotations. We show that our method mitigates the two aforementioned problems and ensures a systematic annotation process. We provide an in-depth analysis of the structure and contents of our resulting data set, which, as of October 10, 2016, contains 3911 motions with a total duration of 11.23 hours and 6278 annotations in natural language that contain 52,903 words. We believe this makes our data set an excellent choice that enables more transparent and comparable research in this important area.

Towards open-source, low-cost haptics for surgery simulation.

In minimally invasive surgery (MIS), virtual reality (VR) training systems have become a promising education tool. However, the adoption of these systems in research and clinical settings is still limited by the high costs of dedicated haptics hardware for MIS. In this paper, we present ongoing research towards an open-source, low-cost haptic interface for MIS simulation. We demonstrate the basic mechanical design of the device, the sensor setup as well as its software integration.

General robot kinematics decomposition without intermediate markers.

The calibration of serial manipulators with high numbers of degrees of freedom by means of machine learning is a complex and time-consuming task. With the help of a simple strategy, this complexity can be drastically reduced and the speed of the learning procedure can be increased. When the robot is virtually divided into shorter kinematic chains, these subchains can be learned separately and hence much more efficiently than the complete kinematics. Such decompositions, however, require either the possibility to capture the poses of all end effectors of all subchains at the same time, or they are limited to robots that fulfill special constraints. In this paper, an alternative decomposition is presented that does not suffer from these limitations. An offline training algorithm is provided in which the composite subchains are learned sequentially with dedicated movements. A second training scheme is provided to train composite chains simultaneously and online. Both schemes can be used together with many machine learning algorithms. In the simulations, an algorithm using parameterized self-organizing maps modified for online learning and Gaussian mixture models (GMMs) were chosen to show the correctness of the approach. The experimental results show that, using a twofold decomposition, the number of samples required to reach a given precision is reduced to twice the square root of the original number.

Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions.

Reinforcement learning methods can be used in robotics applications especially for specific target-oriented problems, for example the reward-based recalibration of goal directed actions. To this end still relatively large and continuous state-action spaces need to be efficiently handled. The goal of this paper is, thus, to develop a novel, rather simple method which uses reinforcement learning with function approximation in conjunction with different reward-strategies for solving such problems. For the testing of our method, we use a four degree-of-freedom reaching problem in 3D-space simulated by a two-joint robot arm system with two DOF each. Function approximation is based on 4D, overlapping kernels (receptive fields) and the state-action space contains about 10,000 of these. Different types of reward structures are being compared, for example, reward-on- touching-only against reward-on-approach. Furthermore, forbidden joint configurations are punished. A continuous action space is used. In spite of a rather large number of states and the continuous action space these reward/punishment strategies allow the system to find a good solution usually within about 20 trials. The efficiency of our method demonstrated in this test scenario suggests that it might be possible to use it on a real robot for problems where mixed rewards can be defined in situations where other types of learning might be difficult.