Abstract: Virtual prototyping is a strategic practice in the research and development of innovative products and machine tools. Virtual prototyping allows the integration of multidomain simulations into the designing process to replicate and analyze the impact of design choices on the overall system performance, reducing time-to-market while simultaneously improving quality. The current paper provided a methodological approach to model complex machine tools and perform virtual testing. As a use case for this study, a parallel kinematic machine (Pentapod P800, METROM Mechatronische Maschinen GmbH, Germany) is investigated. The adoption of these complex machine tools within the industrial context and the design of parallel kinematic machines can be eased by the implementation of methodologies capable of reducing efforts and risks during the analysis and the testing phases, prior to actual commissioning. In this scenario, virtual testing guarantees generality, completeness, and quick response. Therefore, the multibody model of the Pentapod P800 was developed following the proposed framework. Then, the simulation of a test trajectory was successfully carried out. The results show that this approach might lead to the design and implementation of a parallel kinematics machine reducing risks, time, and costs.

Keywords: Digital modelling | Multibody modelling | Parallel kinematic machines | Virtual prototyping

Abstract: Performance diagnostics, such as monitoring athletes’ movements, are essential for enhancing their performances. Wearable sensor networks are commonly employed for this purpose. However, designing such networks can be complex and often necessitates expensive and time-consuming pilot studies. To address this, we propose utilizing multibody models and digital sensors to simulate human movements and virtually test the development of sensor setups. In this study, we present an improved multibody model of the human upper limb, building upon an existing virtual environment from the literature for simulating human gestures. Our model incorporates a streamlined representation of the human torso and clavicle, along with their corresponding joints. The primary objective is to introduce a novel actuation method for the updated model, which only requires the positions of a predefined point as input, utilizing inverse kinematics to define the joint parameters. This approach reduces the amount of data required for simulation. To showcase the capabilities of this method, we acquired and utilized the trajectories of the center of gravity of the hand of a handball player executing a set shot to animate the multibody model. During the user study, the reference trajectories were compared to the trajectories obtained for the hand model during the simulation, revealing a strong agreement and affirming the feasibility of the proposed method.

Keywords: Human Movements | Inverse Kinematics | Multibody Modelling | Virtual Environment | Wearable Sensors

Abstract: Humans are able to communicate by a wide variety of means. Gestures often play an important role in this multimodal communication. In order to also ensure robust interaction between humans and machines, it is important that machines are able to recognize human gestures. This typically requires time-consuming subject tests that limit the number of conditions that can be tested. However, by moving these tests from the physical to a virtual environment, each test condition can be evaluated quickly, eliminating the need for numerous repetitions. The purpose of this work was to validate the use of a virtual test environment in comparison to physical testing. This was done by conducting a subject test and developing a virtual model of the human upper limb. The motion profile of the subject performing a simple gesture was recorded with a visual optical motion capture system and used as input for the newly developed virtual model. Acceleration signals captured with an IMU attached to the subject's right wrist were used as a reference signal and compared to signals simulated by a digital twin of the sensor. The pilot study proved the capabilities of the proposed approach and showed some of its limitations.

Keywords: Digital twin | Human body model | Human gestures | Simulation | Virtual environment

Abstract: Objective and reliable assessment of motor functions, such as dexterity, is a key point for evaluating worker’s abilities. In this context, the proposed work presents a tool for objective automatic assessment of the Minnesota Dexterity Test using cameras with depth sensors. Typical performance measurements (i.e., total time and associated percentiles) were estimated using custom algorithms. In addition, the possibility to identify the qualifiers for the code d440 of the International Classification of Functioning, Disability and Health was implemented in the developed algorithms. The proposed tool can also identify the mistakes most frequently committed by the subjects. To prove the capabilities of the proposed method, a series of experimental trials was conducted with 10 healthy young volunteers. Results showed that the developed tool helps clinicians to obtain performance feedback and evaluate patients’ dexterity quickly without bias.

Keywords: Automatic assessment | Biomechanics | Depth cameras | Manual dexterity | Motion capture

Abstract: The analysis of human gait is of fundamental importance for the monitoring and enhancement of athletes’ performances. The kinematics and kinetics of human gait are mostly investigated with optical motion capture systems and force plates that require specialised laboratories and limit the possible test conditions. On the contrary, body-attached sensor networks provide an opportunity for long-term acquisitions in unsupervised, naturalistic scenarios. In this study, a wearable sensor network consisting of two wireless dataloggers and two instrumented insoles with eight pressure sensors each is used. Custom algorithms for the automatic detection of hike events and the estimation of the related temporal parameters based on sensors data are presented. The proposed algorithms were tested against laboratory measurements performed on an instrumented treadmill and showed relative errors of less than 2.5% in the estimation of stride time, step time and cadence. Higher relative errors were found in the estimation of stance and swing phases. The developed algorithms were also applied in a field study. In this paper data from one subject are considered. The aim of this research work is to provide an effective sensor-based methodology for the evaluation of gait parameters in naturalistic settings.

Keywords: Gait analysis | Hike events | Pressure insoles | Temporal parameters | Wearables

Abstract: The coexistence of autonomous vehicles and humans in public traffic represents a challenging space sharing conflict. In fact, coordination in road traffic is usually made possible by the capacity of humans to communicate their intentions to the fellow road users. With the advent of autonomous vehicles, new ways to communicate intentions should be investigated. To increase the safety of cyclists in mixed traffic is important to early recognize their intentions. This work aims to lay the foundations for augmenting bicycle frames and cycling garments with sensors. To this end, a custom wearable sensor network comprised of three inertial measurement units and a pedaling cadence sensor is proposed and used in a field study with eight participants. The aim of this pilot study is to early-identify the intention of cyclists to perform a left turn, that in right-handed traffic, represents a potentially dangerous maneuver. Collected data are analyzed and annotated, with the support of video, to classify the actions performed by a cyclist when approaching a left turn. Three events where identified: (i) turning of the head, (ii) interruption of pedaling, (iii) left hand out. Results show how the proposed methodology represents a feasible solution to early-identify the intention of a cyclist to turn left.

Keywords: Cycling | Mixed Traffic | Prediction | Safety | Wearable Sensors

Abstract: In this work, we propose a framework that can be used for virtual evaluation of Body-Attached Sensor Networks. Normally, the evaluation of Body-Attached Sensor Networks requires numerous subject tests under laboratory conditions. However, since it is difficult to perform the same motion repeatedly without minute deviations, numerous replicate measurements are required to obtain statistically meaningful measurements. To overcome this limitation, we propose the use of virtual environments. These provide both a high degree of flexibility, since a movement can be repeated in the same way each time, and the ability to test many different sensor setups quickly and with little effort. To this end, we modeled the human body parts of interest using the MATLAB tool Simscape Multibody. Digital twins were then implemented in this model to represent real sensors along with their sensor properties at arbitrary locations. This makes it possible to check many different sensor types and their position on the body in a short time without having to perform subject tests. This framework creates a solid basis for the development of effective Body-Attached Sensor Networks.

Keywords: Digital twins | Multibody simulation | Sensor networks | Virtual environment | Wearable

Abstract: The conventional camera-based systems and electronic gloves for gesture recognition are limited by the influence of lighting conditions, occlusions, and movement restrictions. A wearable smart band with integrated nanocomposite pressure sensors has been developed to overcome these shortcomings. The sensors consist of homogeneously dispersed carbon nanotubes in a polydimethylsiloxane polymer matrix prepared by an optimized synthesis process. The sensor band can actively monitor contractions/relaxations of muscles in the arm due to the sensor's high sensitivity in the low forces and stability. The band has eight sensors placed on a stretchable adhesive textile material and connected to a data logger with a multiplexed sensor interface and wireless communication capabilities. The novel smart band was validated by measurements on ten subjects to perform numerical gestures in American sign language from 0 to 9 with ten trials each. The data were recorded at 100 Hz, and a total of 100 datasets were generated for each subject. By feeding the datasets to an extreme machine learning algorithm that selects features, weights, and biases to classify the gestures, an overall gesture recognition accuracy of 93% could be achieved.

Keywords: American sign language | gesture recognition | polymer carbon nanocomposite (PCN) pressure sensors | Sensor applications | wearable smart band

Abstract: This research activity aims to develop new cycling gloves. A first step was focused on the definition of the functional requirements through user centred design methods. Since vibrations coming to the hand-arm system of a cyclist have a considerable effect a second step was concentrated on the analysis of hand-arm vibrations in road cycling. The paper shows results of laboratory tests executed for three different hand sizes, three different frequency ranges, with two different type of gloves and without gloves. Load conditions used for the test were determined with a former field test. Results obtained were analysed using Analysis of Variance (ANOVA), that showed no significant effect of existing gloves in reducing vibration transmissibility. This led to the need of new kind of cycling gloves that could reduce those vibrations and increase the cyclist’s comfort.

Keywords: Bioengineering | Cycling gloves | Design of experiments | Road cycling | Sport equipment | User centred design | Vibration transmission