User Based Development and Test of the EXOTIC Exoskeleton: Empowering Individuals with Tetraplegia Using a Compact, Versatile, 5-DoF Upper Limb Exoskeleton Controlled through Intelligent Semi-Automated Shared Tongue Control.

This paper presents the EXOTIC- a novel assistive upper limb exoskeleton for individuals with complete functional tetraplegia that provides an unprecedented level of versatility and control. The current literature on exoskeletons mainly focuses on the basic technical aspects of exoskeleton design and control while the context in which these exoskeletons should function is less or not prioritized even though it poses important technical requirements. We considered all sources of design requirements, from the basic technical functions to the real-world practical application. The EXOTIC features: (1) a compact, safe, wheelchair-mountable, easy to don and doff exoskeleton capable of facilitating multiple highly desired activities of daily living for individuals with tetraplegia; (2) a semi-automated computer vision guidance system that can be enabled by the user when relevant; (3) a tongue control interface allowing for full, volitional, and continuous control over all possible motions of the exoskeleton. The EXOTIC was tested on ten able-bodied individuals and three users with tetraplegia caused by spinal cord injury. During the tests the EXOTIC succeeded in fully assisting tasks such as drinking and picking up snacks, even for users with complete functional tetraplegia and the need for a ventilator. The users confirmed the usability of the EXOTIC.

The impact of interdisciplinarity and user involvement on the design and usability of an assistive upper limb exoskeleton - a case study on the EXOTIC.

This study describes an interdisciplinary approach to develop a 5 degrees of freedom assistive upper limb exoskeleton (ULE) for users with severe to complete functional tetraplegia. Four different application levels were identified for the ULE ranging from basic technical application to interaction with users, interaction with caregivers and interaction with the society, each level posing requirements for the design and functionality of the ULE. These requirements were addressed through an interdisciplinary collaboration involving users, clinicians and researchers within social sciences and humanities, mechanical engineering, control engineering media technology and biomedical engineering. The results showed that the developed ULE, the EXOTIC, had a high level of usability, safety and adoptability. Further, the results showed that several topics are important to explicitly address in relation to the facilitation of interdisciplinary collaboration including, defining a common language, a joint visualization of the end goal and a physical frame for the collaboration, such as a shared laboratory. The study underlined the importance of interdisciplinarity and we believe that future collaboration amongst interdisciplinary researchers and centres, also at an international level, can strongly facilitate the usefulness and adoption of assistive exoskeletons and similar technologies.

A review of computer vision for semi-autonomous control of assistive robotic manipulators (ARMs).

Purpose: The advances in artificial intelligence have started to reach a level where autonomous systems are becoming increasingly popular as a way to aid people in their everyday life. Such intelligent systems may especially be beneficially for people struggling to complete common everyday tasks, such as individuals with movement-related disabilities. The focus of this paper is hence to review recent work in using computer vision for semi-autonomous control of assistive robotic manipulators (ARMs). Methods: Four databases were searched using a block search, yielding 257 papers which were reduced to 14 papers after applying various filtering criteria. Each paper was reviewed with focus on the hardware used, the autonomous behaviour achieved using computer vision and the scheme for semi-autonomous control of the system. Each of the reviewed systems were also sought characterized by grading their level of autonomy on a pre-defined scale.Conclusions: A re-occurring issue in the reviewed systems was the inability to handle arbitrary objects. This makes the systems unlikely to perform well outside a controlled environment, such as a lab. This issue could be addressed by having the systems recognize good grasping points or primitive shapes instead of specific pre-defined objects. Most of the reviewed systems did also use a rather simple strategy for the semi-autonomous control, where they switch either between full manual control or full automatic control. An alternative could be a control scheme relying on adaptive blending which could provide a more seamless experience for the user.Implications for rehabilitationAssistive robotic manipulators (ARMs) have the potential to empower individuals with disabilities by enabling them to complete common everyday tasks. This potential can be further enhanced by making the ARM semi-autonomous in order to actively aid the user.The scheme used for the semi-autonomous control of the ARM is crucial as it may be a hindrance if done incorrectly. Especially the ability to customize the semi-autonomous behaviour of the ARM is found to be important.Further research is needed to make the final move from the lab to the homes of the users. Most of the reviewed systems suffer from a rather fixed scheme for the semi-autonomous control and an inability to handle arbitrary objects.

Organ Segmentation in Poultry Viscera Using RGB-D.

We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11 % is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28 % using only basic 2D image features.

Estimation of Energy Expenditure during Treadmill Exercise via Thermal Imaging.

PURPOSE: Noninvasive imaging of oxygen uptake may provide a useful tool for the quantification of energy expenditure during human locomotion. A novel thermal imaging method (optical flow) was validated against indirect calorimetry for the estimation of energy expenditure during human walking and running. METHODS: Fourteen endurance-trained subjects completed a discontinuous incremental exercise test on a treadmill. Subjects performed 4-min intervals at 3, 5, and 7 km·h (walking) and at 8, 10, 12, 14, 16, and 18 km·h (running) with 30 s of rest between intervals. Heart rate, gas exchange, and mean accelerations of ankle, thigh, wrist, and hip were measured throughout the exercise test. A thermal camera (30 frames per second) was used to quantify optical flow, calculated as the movements of the limbs relative to the trunk (internal mechanical work) and vertical movement of the trunk (external vertical mechanical work). RESULTS: Heart rate, gross oxygen uptake (mL·kg·min) together with gross and net energy expenditure (J·kg·min) rose with increasing treadmill velocities, as did optical flow measurements and mean accelerations (g) of ankle, thigh, wrist, and hip. Oxygen uptake was linearly correlated with optical flow across all exercise intensities (R = 0.96, P < 0.0001; V˙O2 [mL·kg·min] = 7.35 + 9.85 × optical flow [arbitrary units]). Only 3-4 s of camera recording was required to estimate an optical flow value at each velocity. CONCLUSIONS: Optical flow measurements provide an accurate estimation of energy expenditure during horizontal walking and running. The technique offers a novel experimental method of estimating energy expenditure during human locomotion, without use of interfering equipment attached to the subject.

Extremal Regions Detection Guided by Maxima of Gradient Magnitude.

A problem of computer vision applications is to detect regions of interest under different imaging conditions. The state-of-the-art maximally stable extremal regions (MSERs) detects affine covariant regions by applying all possible thresholds on the input image, and through three main steps including: (1) making a component tree of extremal regions' evolution; (2) obtaining region stability criterion; and (3) cleaning up. The MSER performs very well, but, it does not consider any information about the boundaries of the regions, which are important for detecting repeatable extremal regions. We have shown in this paper that employing prior information about boundaries of regions results in a novel region detector algorithm that not only outperforms MSER, but avoids the MSER's rather complicated steps of enumeration and the cleaning up. To employ the information about the region boundaries, we introduce maxima of gradient magnitudes (MGMs) which are shown to be points that are mostly around the boundaries of the regions. Having found the MGMs, the method obtains a global criterion for each level of the input image which is used to find extremum levels (ELs). The found ELs are then used to detect extremal regions. The proposed algorithm which is called extremal regions of extremum levels (EREL) has been tested on the public benchmark data set of Mikolajczyk. The obtained experimental results show that the inclusion of region boundaries through MGMs, results in a detector that detects regions with high repeatability scores and is more robust against noise compared with MSER.