Duusjohannsen3789

Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55 ± 1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use.In recent years, the Simultaneous Magnetic Actuation and Localization (SMAL) technology has been developed to accelerate and locate the wireless capsule endoscopy (WCE) in the intestine. In this paper, we propose a novel approach to detect the state of the capsule for improving the localization results. By creating a function to fit the relationship between the theoretical values of the actuating magnetic field and the measurement results, we present an algorithm for automatic estimation of the capsule state according to the fitting parameters. Experiment results on phantoms demonstrate the feasibility of the proposed method for detecting different states of the capsule during magnetic actuation.Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand torque assistance to wheelchair users. Although the available power can reduce the physical load of wheelchair propulsion, it may also cause maneuverability and controllability issues. Commercially-available PAPAW controllers are insensitive to environmental changes, leading to inefficient and/or unsafe wheelchair movements. In this regard, adaptive velocity/torque control strategies could be employed to improve safety and stability. To investigate this objective, we propose a context-aware sensory framework to recognize terrain conditions. In this paper, we present a learning-based terrain classification framework for PAPAWs. Study participants performed various maneuvers consisting of common daily-life wheelchair propulsion routines on different indoor and outdoor terrains. Relevant features from wheelchair frame-mounted gyroscope and accelerometer measurements were extracted and used to train and test the proposed classifiers. Our findings revealed that a one-stage multi-label classification framework has a higher accuracy performance compared to a two-stage classification pipeline with an indoor-outdoor classification in the first stage. We also found that, on average, outdoor terrains can be classified with higher accuracy (90%) compared to indoor terrains (65%). This framework can be used for real-time terrain classification applications and provide the required information for an adaptive velocity/torque controller design.Human-robot interactions help in various industries and enhance the user experience in different ways. However, constant safety monitoring is needed in environments where human users are at risk, such as rehabilitation therapy, space exploration, or mining. One way to improve safety and performance in robotic tasks is to include biological information of the user in the control system. This can help regulate the energy that is delivered to the user. In this work, we estimate the energy absorbing capabilities of the human arm, using the metric Excess of Passivity (EOP). selleckchem EOP data from healthy subjects were obtained based on Forcemyography of the subjects' arm, to expand the sources of biological information and improve estimations.Clinical relevance- This protocol can help determine the ability of rehabilitation patients to withstand robotic stimulation with high amplitudes of therapeutic forces, as needed in assistive therapy.Sonomyography (ultrasound imaging) offers a way of classifying complex muscle activity and configuration, with higher SNR and lower hardware requirements than sEMG, using various supervised learning algorithms. The physiological image obtained from an ultrasound probe can be used to train a classification algorithm which can run on real time ultrasound images. The predicted values can then be mapped onto assistive or teleoperated robots. This paper describes the classification of ultrasound information and its subsequent mapping onto a soft robotic gripper as a step toward direct synergy control. Support Vector Classification algorithm has been used to classify ultrasound information into a set of defined states open, closed, pinch and hook grasps. Once the model was trained with the ultrasound image data, real time input from the forearm was used to predict these states. The final predicted state output then set joint stiffnesses in the soft actuators, changing their interactions or synergies, to obtain the corresponding soft robotic gripper states. Data collection was carried out on five different test subjects for eight trials each. An average accuracy percentage of 93% was obtained averaged over all data. This real-time ultrasound-based control of a soft robotic gripper constitutes a promising step toward intuitive and robust biosignal-based control methods for robots.Collaborative robots are advancing the healthcare frontier, in applications such as rehabilitation and physical therapy. Effective physical collaboration in human-robot systems require an understanding of partner intent and capability. Various modalities exist to convey such information between human agents, however, natural interactions between humans and robots are difficult to characterise and achieve. To enhance inter-agent communication, predictive models for human movement have been devised. One such model is Fitts' law. Many works using Fitts' law rely on massless interfaces. However, this coupling between human and robot, and the inertial effects experienced, may affect the predictive ability of Fitts' law. Experiments were conducted on human-robot dyads during a target-directed force exertion task. From the interactions, the results indicate that there is no observable effect regarding Fitts' law's predictive ability.