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To achieve a fully automatic artificial pancreas (AP), i.e., an AP without the need for meal announcements, the intraperitoneal (IP) route is explored. This route has faster dynamics than the typical subcutaneous (SC) route. Model predictive control (MPC) is the most promising control algorithm, but it requires a predictive and identifiable model. This paper presents the design of such a model for MPC-based dual hormone IP APs. This model is trained and tested on recorded data from anesthetized pigs. Animal experiments show that the saturation of the hepatic first-pass effect is essential in how IP insulin and IP glucagon affect glucose levels. These physiological phenomena must be modeled to estimate the system behavior for various conditions. This, in turn, increases the number of parameters and complicates system identification. The availability of rich experimental data from 26 animal trials motivated the design of a technique to exploit this prior information to ensure the identifiability of our model. Through this technique, most parameters were either modeled as body weight functions or common among animals. The correlation between parameter values and body weight is discovered utilizing prior data from various animal experiments, such as blood glucose, plasma insulin, and glucagon levels, in which hormones were administered intraperitoneally or intravenously. This method simplifies the system identification for every new subject while keeping the model’s essential details that improve the prediction capability relative to comparable models. The model can be exploited in MPC or any other model-based controller of a bi-hormonal IP AP. It can also be used as a simulator to develop control approaches for single and bi-hormonal IP APs.

Two manipulator Jacobian matrix estimators for constrained planar snake robots are developed and tested, which enables the implementation of Jacobian-based obstacle-aided locomotion (OAL) control schemes. These schemes use obstacles in the robot’s vicinity to obtain propulsion. The devised estimators infer manipulator Jacobians for constrained planar snake robots in situations where the positions and number of surrounding obstacle constraints might change or are not precisely known. The first proposed estimator is an adaptation of contemporary research in soft robots and builds on convex optimization. The second estimator builds on the unscented Kalman filter. By simulations, we evaluate and compare the two devised algorithms in terms of their statistical performance, execution times, and robustness to measurement noise. We find that both algorithms lead to Jacobian matrix estimates that are similarly useful to predict end-effector movements. However, the unscented filter approach requires significantly lower computing resources and is not poised by convergence issues displayed by the convex optimization-based method. We foresee that the estimators may have use in other fields of research, such as soft robotics and visual servoing. The estimators may also be adapted for use in general non-planar snake robots.

The design of a model-based control method for an Artificial Pancreas requires a relatively simple and identifiable mathematical model to control glucose levels through hormone delivery. In this work we introduce new, simple nonlinear models to simulate data from experiments where insulin boluses are administrated in the peritoneal cavity. The models account for the delay between insulin administration and its nonlinear transport to other compartments. They were calibrated using experimental data from pigs. The results show that the suggested models are able to describe the data well, with average BIC value of 145. Moreover, the new models were compared with a common linear model which was not able to describe the data well, with BIC value of 920. They were also compared with a common nonlinear model which failed to represent insulin increases in the data and had BIC value of 637. Finally, profile likelihoods were applied for assessing the identifiability of one of the new models. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.

Development of snake robots have been motivated by the ability of snakes to move efficiently in unstructured and cluttered environments. A snake robot has the potential to utilise obstacles for generating locomotion, in contrast to wheeled robots which are unable to move efficiently in rough terrain. In this paper, we propose a local path planning algorithm for snake robots based on obstacle-aided locomotion (OAL). An essential feature in OAL is to determine suitable push-points in the environment that the snake robot can use for locomotion. The proposed method is based on a set of criteria for evaluating a path, and is a novel contribution of this paper. We focus on local path planning and formulate the problem as finding the best next push point and the trajectory towards it. The path is parameterised as a quadratic Bézier curve. The algorithm is implemented and tested with a simulator, employing decentralised joint controllers with references generated by a constant translation speed of the snake along the path. Careful design of the criteria allows us to use simple position and velocity controllers for the joints, circumventing the need for force control. However, the set of feasible paths will be restricted by this approach. The proposed criteria can also be used in a global path planning algorithm; the local focus is due to one of the key use cases of snake robots: operating in unstructured and unknown environments. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

People with diabetes mellitus type 1 could benefit from fully automated systems for glucose control. However, faults in any component of the system can severely compromise the safety of the user. An increasing degree of automation also increases the risk that faults remain undiscovered for longer periods - unless automated routines for fault detection are implemented at the same time. The aim of this article is to give a categorized overview of methods for fault detection in glucose control systems. This overview targets at disclosing hidden potentials for improvement and unresolved issues. Methods for fault detection in glucose control systems have been reviewed and classified with respect to categories such as the type of method and the exploited data basis. Both journal and conference papers were taken into account. Compared to the number of studies on glucose control algorithms, only a few articles have been published on fault detection. Surprisingly few of them consider system information beyond the standard diabetes care data. © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.

Background The analysis of abdominal sounds can help to diagnose gastro-intestinal diseases. Sounds originating from the stomach and the intestine, the so-called bowel sounds, occur in various forms. They are described as loose successions or clusters of rather sudden bursts. Realistic recordings of abdominal sounds are contaminated with noise and artifacts from which the bowel sounds must be differentiated. Methods The proposed intrinsic mode function-fractal dimension (IMF-FD) filtering utilizes the property of the multivariate empirical mode decomposition (MEMD) to behave as a series of band pass filters. The MEMD decomposes the abdominal signal into its different frequency components. The resulting intrinsic mode functions (IMFs) are modulated in amplitude and frequency where transient sonic events occur. Based on the complexity of the IMFs, measured by their fractal dimension (FD) in sliding windows, the information-carrying IMFs are selected. The filtered signal is formed as the superposition of all selected IMFs. The IMF-FD filter not only enhances the non-linear components of the original signal but also segments them from the rest. Another important aspect of this work is that typical artifacts that occur in the same frequency range as bowel sounds can be subsequently eliminated by heuristic rules. Conclusions The method is tested on a realistic, contaminated data set with promising performance: close to 100% of the manually labeled bowel sounds are identified. © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

The aim of this paper is to achieve a model for prediction of cerebral palsy based on motion data of young infants. The prediction is formulated as a classification problem to assign each of the infants to one of the healthy or with cerebral palsy groups. Unlike formerly proposed features that are mostly defined in the time domain, this study proposes a set of features derived from frequency analysis of infants' motions. Since cerebral palsy affects the variability of the motions, and frequency analysis is an intuitive way of studying variability, suggested features are suitable and consistent with the nature of the condition. In the current application, a well-known problem, few subjects and many features, was initially encountered. In such a case, most classifiers get trapped in a suboptimal model and, consequently, fail to provide sufficient prediction accuracy. To solve this problem, a feature selection method that determines features with significant predictive ability is proposed. The feature selection method decreases the risk of false discovery and, therefore, the prediction model is more likely to be valid and generalizable for future use. A detailed study is performed on the proposed features and the feature selection method: the classification results confirm their applicability. Achieved sensitivity of 86%, specificity of 92% and accuracy of 91% are comparable with state-of-the-art clinical and expert-based methods for predicting cerebral palsy.

In patients with diabetes mellitus type 1, the pancreatic insulin production ceases, causing raise in blood glucose level (BGL) and potentially severe long-term complications. The "holy grail" of diabetes treatment is the artificial pancreas (AP), a closed-loop control system that regulates the user’s BGL by infusing insulin, and possibly glucagon. Numerous attempts have been largely unsuccessful, mainly due to slow dynamics that make it difficult to avoid unwanted BGL excursions. System performance has been improved through improved sensor technology and faster-acting insulin types, but the risk of hypoglycemia is still significant unless the glucose setpoint is unnaturally high. We argue that this problem can be circumvented by choosing appropriate sites for glucose measurement and insulin infusion. While intravascular measurement and infusion provides the fastest dynamics and thus the best conditions for closed-loop control, it is only viable in inpatients mainly due to danger of infections and limited sensor durability. On the other extreme, state-of-the-art subcutaneous systems exhibit significant time delays and diffusion dynamics, yielding poor BGL control in the event of disturbances like meals and physical activity. Avoiding dangerous hypoglycemia therefore comes at the expense of daily episodes of elevated BGL (typically 10-15 mmol/L) that increase the risk of long-term complications. Furthermore, slow insulin uptake from subcutis remains as a major challenge. Hence we advocate the double intraperitoneal (IP) AP. Here, insulin is released into the abdominal cavity (peritoneum) through a semi-permanent port, which also allows access for IP glucose sensing. This improves both sensing and absorption dynamics. Thus the closed-loop control may be significantly tighter, allowing a setpoint closer to the healthy normal BGL of approximately 4.5 mmol/L whilst potentially improving system safety. These statements are supported by results from our own research and the literature.

Rapid, accurate and robust glucose measurements are needed to make a safe artificial pancreas for the treatment of diabetes mellitus type 1 and 2. The present gold standard of continuous glucose sensing, subcutaneous (SC) glucose sensing, has been claimed to have slow response and poor robustness towards local tissue changes such as mechanical pressure, temperature changes, etc. The present study aimed at quantifying glucose dynamics from central circulation to intraperitoneal (IP) sensor sites, as an alternative to the SC location. Intraarterial (IA) and IP sensors were tested in three anaesthetized non-diabetic pigs during experiments with intravenous infusion of glucose boluses, enforcing rapid glucose level excursions in the range 70--360 mg/dL (approximately 3.8--20 mmol/L). Optical interferometric sensors were used for IA and IP measurements. A first-order dynamic model with time delay was fitted to the data after compensating for sensor dynamics. Additionally, off-the-shelf Medtronic Enlite sensors were used for illustration of SC glucose sensing. The time delay in glucose excursions from central circulation (IA) to IP sensor location was found to be in the range 0--26 s (median: 8.5 s, mean: 9.7 s, SD 9.5 s), and the time constant was found to be 0.5--10.2 min (median: 4.8 min, mean: 4.7 min, SD 2.9 min). IP glucose sensing sites have a substantially faster and more distinctive response than SC sites when sensor dynamics is ignored, and the peritoneal fluid reacts even faster to changes in intravascular glucose levels than reported in previous animal studies. This study may provide a benchmark for future, rapid IP glucose sensors.

The artificial pancreas requires fast and reliable glucose measurements. The peritoneal space has shown promising results, and in one of our studies we detected glucose changes in the peritoneal space already at the same time as in the femoral artery. The peritoneal lining is highly vascularised, covered by a single layer of mesothelial cells and therefore easily accessible for proper sensor technology, e.g. optical technology. We hypothesize that the rapid intraperitoneal glucose dynamics observed in our study was possible because the sensors were located directly at the peritoneal lining, at the point where the glucose molecules entered the peritoneal space. Glucose travels slowly in fluids by diffusion, and a longer distance between the sensor and the peritoneal lining would consequently result in slower dynamics. We therefore propose to place the glucose sensor in an artificial pancreas as closely to the peritoneal lining as possible, or even utilize appropriate sensor technology to measure glucose in the peritoneal lining itself.

Rotations of the Cartesian reference frames are used extensively in many fields of research such as navigation and guidance systems, and motion tracking systems. Of particular interest is the case when an infinitesimal rotation is followed or preceded by a large rotation. If the order of rotations is required to be changed, there is no formula to describe a good approximation. Instead, researchers usually use dot product of the unit vectors of the coordinate systems before and after the reversal. In this work, we provide a direct formula for such approximation. Further, we show that the proposed formulae can be used to construct the inverse of any transformation comprising two successive rotations about different axes by taking many successive infinitesimal rotations approximated properly. Moreover, we present a case study for which the proposed formulae can be used to find the solution in a direct way compared to other techniques.

We aim at predicting cerebral palsy at an early stage using normal video camera.A new motion segmentation method is proposed that benefits from prior knowledge.A particle matching technique is used to reduce the dependency on prior knowledge.The proposed method segments the objects, infants body parts, with high performance.The method is tested on a standard benchmark and outperforms the previous methods. Motion segmentation refers to the task of segmenting moving objects subject to their motion in order to distinguish and track them in a video. This is a challenging task in situations where different objects share similar movement patterns, or in cases where one object is occluded by others in part of the scene. In such cases, unsupervised motion segmentation fails and additional information is needed to boost the performance. Based on a formulation of the clustering task as an optimization problem using a multi-labeled Markov Random Field, we develop a semi-supervised motion segmentation algorithm by setting up a framework for incorporating prior knowledge into the segmentation algorithm. Prior knowledge is given in the form of manually labelling trajectories that belong to the various objects in one or more frames of the video. Clearly, one wishes to limit the amount of manual labelling in order for the algorithm to be as autonomous as possible. Towards that end, we propose a particle matching procedure that extends the prior knowledge by automatically matching particles in frames over which fast motion or occlusion occur. The performance of the proposed method is studied through a variety of experiments on videos involving fast and complicated motion, occlusion and re-appearance, and low quality film. The qualitative and quantitative results confirm reliable performance on the types of applications our method is designed for.

Biological snakes are capable of exploiting roughness in the terrain for locomotion. This feature allows them to adapt to different types of environments. Snake robots that can mimic this behaviour could be fitted with sensors and used for transporting tools to hazardous or confined areas that other robots and humans are unable to access. Snake robot locomotion in a cluttered environment where the snake robot utilises a sensory-perceptual system to perceive the surrounding operational environment for means of propulsion can be defined as perception-driven obstacle-aided locomotion (POAL). The initial testing of new control methods for POAL in a physical environment using a real snake robot imposes challenging requirements on both the robot and the test environment in terms of robustness and predictability. This paper introduces SnakeSIM, a virtual rapid-prototyping framework that allows researchers for the design and simulation of POAL more safely, rapidly and efficiently. SnakeSIM is based on the Robot Operating System (ROS) and it allows for simulating the snake robot model in a virtual environment cluttered with obstacles. The simulated robot can be equipped with different sensors. Tactile perception can be achieved by using contact sensors to retrieve forces, torques, contact positions and contact normals. A depth camera can be attached to the snake robot head for visual perception purposes. Furthermore, SnakeSIM allows for exploiting the large variety of robotics sensors that are supported by ROS. The framework can be transparently integrated with a real robot. To demonstrate the potential of SnakeSIM, a possible control approach for POAL is considered as a case study. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Meals are one of the greatest challenges to glucose regulation in diabetes mellitus type 1. Several times each day, food causes heavily elevated blood glucose concentrations that may result in long-term complications. Meal-time insulin boluses are administered to mitigate these hyperglycemic periods. Sporadic omissions of prandial boluses impair the outcome of the insulin therapy by leading to significant variations in blood glucose levels. As continuous glucose monitoring (CGM) becomes more common, an automated detection based on CGM data could support patients by reminding about missed boluses. In fully automated systems, meal detection could temporarily modify controller parameters until the meal is mitigated. In the present study, moving horizon estimation (MHE) and linear discriminant analysis (LDA), abbreviated “MHE+LDA”, are proposed for meal detection. An augmented version of Bergman's minimal model is used for the estimator model. Neither the model parameters nor the MHE tuning are individualized. The method is tested in simulations on the UVa/Padova simulator and its performance is compared to two other methods, namely threshold checking of the current estimated glucose appearance and the GRID algorithm. All meals are detected by MHE+LDA within 35 min while the two comparative methods do not detect the smallest simulated meal. The combination of MHE and LDA outperforms the two other methods also with respect to time of detection. The MHE+LDA method's ability to identify even smaller meals without the need for individual tuning suggests that the method should be further investigated. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This is the authors accepted and refereed manuscript to the article. This paper considers the lateral undulation motion of snake robots. The first contribution of the paper is a model of lateral undulation dynamics developed for control design and stability analysis purposes. The second contribution is an analysis of the simplified model that shows that any asymptotically stabilizing control law for the snake robot to an equilibrium point must be time varying. Furthermore, the analysis shows that a snake robot (with four links) is strongly accessible from almost any equilibrium point, except for certain singular configurations, and that the robot does not satisfy sufficient conditions for small-time local controllability. The third contribution is based on using averaging theory to prove that the average velocity of the robot during lateral undulation will converge exponentially fast to a steady-state velocity which is given analytically as a function of the gait pattern parameters. From the averaging analysis, we also derive a set of fundamental relationships between the gait parameters of lateral undulation and the resulting forward velocity of the snake robot. The paper presents simulation results and results from experiments with a physical snake robot that support the theoretical findings. This is the authors accepted and refereed manuscript to the article. Copyright © Cambridge University Press 2013

Snake robots equipped with sensors and tools could potentially contribute to applications such as fire-fighting, industrial inspection, search-and-rescue and more. Such capabilities would require that a snake robot has a high degree of awareness of its surroundings (i.e. perception-driven locomotion) and is able to exploit objects and irregularities in its environment to gain propulsion (i.e. obstacle-aided locomotion). In this work, a simplified snake robot model is proposed to deal with a lower-dimensional system that allows for establishing the foundation elements of perception-driven obstacle-aided locomotion. To achieve this, a virtual partitioning of the snake into parametrised virtual functional segments (VFS) is presented based on the concept of virtual constraints (VC). The snake robot body is approximated by using a chain of continuous curves with the fewest possible parameters. These parameters can be treated as degrees of freedom of a “constrained system” and consequently be subjected to modelling and control at a higher abstraction level. The main contribution of the proposed conceptual approach is that the robot joint space can be reduced into a lower-dimensional space for articulation. This concept replaces the analysis of the individual mechanical degrees of freedom of the snake by the analysis of the functional roles of the parametrised VFS. The VFS are defined in relation to task space coordinates, and the roles of the physical links and joints change according to a defined set of transition events as they move along the robot's path. This method is a preliminary step towards realising perception-driven obstacle-aided locomotion for snake robots.

Biological snakes can gracefully traverse a wide range of different and complex environments. Snake robots that can mimic this behaviour could be fitted with sensors and also transport tools to hazardous or confined areas that other robots and humans are unable to access. To carry out such tasks, snake robots must have a high degree of awareness of their surroundings (i.e. perception-driven locomotion) and be capable of efficient obstacle exploitation (i.e. obstacle-aided locomotion) to gain propulsion. These aspects are important to realise the large variety of possible snake robot applications in real-life operations such as fire-fighting, industrial inspection, search-and-rescue and more. In this paper, an elaborate review and discussion of the state-of-the-art, challenges and possibilities of perception-driven obstacle-aided locomotion for snake robots is presented for the first time. Pertinent to snake robots, we focus on current strategies for obstacle avoidance, obstacle accommodation, and obstacle-aided locomotion. Moreover, we put obstacle-aided locomotion into the context of perception and mapping. To this end, we present an overview of relevant key technologies and methods within environment perception, mapping and representation that constitute important aspects of perception-driven obstacle-aided locomotion.

Author postprint As a step toward enabling snake robots to move in cluttered environments, this paper proposes a control strategy that combines environment adaptation with directional control in order to achieve straight line path following control in environments with obstacles. Moreover, the paper presents the design of a mechanical snake robot with tactile sensing capabilities that allow the robot to sense its environment. Experimental results are presented where the snake robot is successfully propelled through different obstacle environments with the proposed control strategy.

Cerebral palsy (CP) is a perinatally acquired nonprogressive brain damage resulting in motor impairment affecting mobility and posture. Early identification of infants with CP is desired to target early interventions and follow-up. During early infancy, distinct motion patterns occur which are highly predictive for later disability. These motor patterns can be observed and recorded. In this paper, a method to predict later CP based on early video recordings of the infants' spontaneous movements, applying optical flow and statistical pattern recognition, is presented. We extract motion information from video recordings of young infants using a total variation related optical flow method. By using wavelet analysis features from motion trajectories of points initiated on a regular grid were extracted and classified using a support vector machine.

This is the authors accepted and refereed manuscript to the article. In a recent paper, the authors have proposed a control strategy for a snake robot during obstacle-aided locomotion. In this paper, experimental results are presented where the controller is shown to successfully maintain the forward propulsion of a physical snake robot in a course with different obstacle configurations. This is the authors accepted and refereed manuscript to the article. ("(c) 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Rapid improvements in materials, design, and treatment in upper limb prosthetics makes assessment an important area. After meetings at international conferences and workshops, a special interest group of professionals concerned with the provision of upper limb prostheses was formed to create

A need for a systematic measurement of function for upper limb prosthetics has been identified. Using the World Health Organization-International Classification of Functioning, Disability and Health (WHO-ICF) model, it was clear that no single test was able to cover the entire cycle of prost

This paper describes the design and performance of a prototype remote palpation instrument with tactile feedback for laparoscopic surgery. Psychophysical experiments aiming at comparing palpation with and without tactile feedback are also described. The remote palpation instrument is designed to provide the surgeon with information about size and contact force distribution. The instrument consists of a tactile sensor and a tactile display, both of which are small enough to fit onto a conventional laparoscopic grasper. Static tests showed that hardness, size and shape were successfully rendered by the tactile display. Dynamic testing showed that the bandwidth is suboptimal, but at low frequencies the output from the remote palpation instrument is useful. For the psychophysical testing, nine subjects were asked to discriminate hardness and size of objects with and without tactile feedback. The results indicated that the instrument with tactile feedback can be helpful for hardness discrimination, although the difference between the two instruments was not statistically significant for neither hardness nor size. Comparisons with earlier, similar experiments showed that the quality of the grasper and size of the forceps affected the results, indicating that there is a great potential for improving laparoscopic graspers in general.

In this paper we aim at predicting cerebral palsy, the most serious and lifelong motor function disorder in children, at an early age by analysing infants' motion data. An essential step for doing so is to extract informative features with high class separability. We propose a set of features derived from frequency analysis of the motion data. Then, we evaluate the practicality of our features on one of the richest data sets collected to study this disease. In this data set, the motion data are extracted from both electromagnetic sensors as well as video camera. The proposed features are used for classifying both data sets. Using these features, we manage to achieve promising classification performance. Classification accuracy of 91% for the sensor data and 88% for the video-derived data show not only the advantage of employing these features for predicting cerebral palsy, but also that replacing electromagnetic sensors with a video camera is feasible.

Analysing distinct motion patterns that occur during infancy can be a way through early prediction of cerebral palsy. This analysis can only be performed by well-trained expert clinicians, and hence can not be widespread, specially in poor countries. In order to decrease the need for experts, computer-based methods can be applied. If individual motions of different body parts are available, these methods could achieve more accurate results with better clinical insight. Thus far, motion capture systems or the like were needed in order to provide such data. However, these systems not only need laboratory and experts to set up the experiment, but they could be intrusive for the infant's motions. In this paper we build up our prediction method on a solution based on a single video camera, that is far less intrusive and a lot cheaper. First, the motions of different body parts are separated, then, motion features are extracted and used to classify infants to healthy or affected. Our experimental results show that visually obtained motion data allows cerebral palsy detection as accurate as state-of-the-art electromagnetic sensor data.

Two experiments aiming at comparing palpation with gloved fingers, conventional laparoscopic instruments, and a laparoscopic instrument with a sensor array attached to its end effector are described. The sensor array provides the surgeon with visually presented tactile information. Fifteen subjects were asked to discriminate hardness and size of objects (rubber balls hidden in pig's intestine) with the 3 palpation methods. The experiments showed that the gloved fingers are better at differentiating hardness and size compared with conventional laparoscopic instruments and the instrument with sensor. There was no significant difference between conventional instruments and the instrument with sensor, although the results showed a higher average score with the instrument with sensor. This indicates that visual presentation may not be an ideal way of presenting tactile information. It also indicates that the presence of the array does not make the task more difficult.

Rotational data in the form of measured three-dimensional rotations or orientations arise naturally in many fields of science, including biomechanics, orthopaedics and robotics. The cyclic topology of rotation spaces calls for special care and considerations when performing statistical analysis of rotational data. Relevant theory has been developed during the last three decades, and has become a standard tool in some areas. In relation to the study of human kinematics and motion however, these concepts have hardly been put to use. This paper gives an introduction to the intricacies of three-dimensional rotations, and provides a thorough geometric interpretation of several approaches to averaging rotational data. A set of novel, simple operators is presented. Simulations and a prosthetics-related real-world example involving wrist kinematics illuminate important aspects of the results. Finally generalizations and related subjects for further research are suggested.

This is the authors accepted and refereed manuscript to the article. This paper gives a treatment of various aspects related to snake locomotion. A mathematical model and a physical implementation of a modular snake robot are presented. A control strategy is also developed, yielding a general expression for different gait patterns. Two forms of locomotion have been simulated with the mathematical model, and experiments with the physical snake robot have been conducted. The simulation results revealed the parameter through which directional control may be achieved for each gait pattern. Experiments with the physical snake robot gave a crude qualitative verification of these findings. Keywords: Mathematical model, kinematics, dynamics, control algorithm, simulation, actuators, mobile robot, movement. This is the authors accepted and refereed manuscript to the article. Copyright © 2005 IFAC. http://www.elsevier.com/books/book-series/ifac-proceedings-volumes

Background Neck pain is associated with several alterations in neck motion and motor control. Previous studies have investigated single constructs of neck motor control, while few have applied a comprehensive set of tests to investigate cervical motor control. This comparative cross- sectional study aimed to investigate different motor control constructs in neck pain patients and healthy controls. Methods A total of 166 subjects participated in the study, 91 healthy controls (HC) and 75 neck pain patients (NP) with long-lasting moderate to severe neck pain. Neck flexibility, proprioception, head steadiness, trajectory movement control, and postural sway were assessed using a 3D motion tracking system (Liberty). The different constructs of neck motion and motor control were based on tests used in previous studies. Results Neck flexibility was lower in NP compared to HC, indicated by reduced cervical ROM and conjunct motion. Movement velocity was slower in NP compared to HC. Tests of head steadiness showed a stiffer movement pattern in NP compared to HC, indicated by lower head angular velocity. NP patients departed less from a predictable trajectory movement pattern (figure of eight) compared to healthy controls, but there was no difference for unpredictable movement patterns (the Fly test). No differences were found for postural sway in standing with eyes open and eyes closed. However, NP patients had significantly larger postural sway when standing on a balance pad. Proprioception did not differ between the groups. Largest effect sizes (ES) were found for neck flexibility (ES range: 0.2- 0.8) and head steadiness (ES range: 1.3- 2.0). Neck flexibility was the only construct that showed a significant association with current neck pain, while peak velocity was the only variable that showed a significant association with kinesiophobia. Conclusions NP patients showed an overall stiffer and more rigid neck motor control pattern compared to HC, indicated by lower neck flexibility, slower movement velocity, increased head steadiness and more rigid trajectory head motion patterns. Only neck flexibility showed a significant association with clinical features in NP patients. Electronic supplementary material The online version of this article (doi:10.1186/s12891-015-0517-2) contains supplementary material, which is available to authorized users.

This paper presents a novel approach to obstacle-aided locomotion - pushing against obstacles in the environment to achieve propulsion - of snake robots. These machines are robots designed to mimic the propulsive behavior of biological snakes, and in nature obstacle-aided locomotion is the main means of propulsion for serpents. The solution presented in this paper is based on a static analysis of a snake robot in contact with a certain number of known obstacles. Given a set of external contact points and a desired total propulsive force, we show which motor torques are necessary to achieve this. The problem typically presents an infinite number of solutions, and is therefore solved by minimizing the consumed energy (using motor torque as a proxy). The results are verified with a digital computer.

In this paper, motion planning of snake robots is considered. In particular, we develop a mapping from a continuous 3D curve to the position and shape of a snake robot. The snake robot's position is the x, y, z coordinates of its center of mass; its shape is defined by a series of angles representing the rotation of each joint relative the the inertial reference frame. Smooth curves are often easier to use in path planning and design of gait patterns, but snake robots are non-smooth. A mapping is therefore necessary. The mapping is optimal in the least squares sense. The optimal configuration is found by explicitly differentiating the cost function, and finding the equilibria. The method is compared to two other methods in literature, and has lower mean square error than both these other methods.

We end this book with a discussion which points out important research challenges that must be addressed before we will ever see useful snake robots outside the laboratory. This book has presented experimental results that demonstrate planar snake robot locomotion in cluttered environments based on environment sensing and body shape adaptation. However, to our best knowledge, the fact still remains that non-planar (3D) locomotion in cluttered environments based on environment sensing and body shape adaptation has not yet been demonstrated. Our primary claim is therefore that future applications of snake robots require significantly more research on adaptive behaviour during motion in unknown and cluttered environments.

In this chapter, we propose a control strategy that enables a snake robot to propel its body forward by active use of the interaction with obstacles in its environment. This form of propulsion is called obstacle-aided locomotion. Obstacle-aided locomotion represents an interesting control problem for which previous research is very limited. The literature review presented in the Introduction of this book clearly shows that a large majority of control strategies proposed for snake robots so far assume that the environment of the robot is flat. We believe control strategies for snake robots that consider environment interaction are important since the main advantage of these mechanisms are their potential ability to move in uneven and cluttered environments.

The underlying theme of this book is analytical approaches aimed at increasing our understanding of snake robot locomotion. The mathematical model of the snake robot is the basis for these analytical studies, which means that the analysis relies heavily on the form and complexity of the model. In this chapter, we employ first principles to develop a mathematical model of the kinematics and dynamics of a snake robot with N links moving on a horizontal and flat surface. The links of the robot are influenced by ground friction forces which propel the motion. Due to the many degrees of freedom of the robot and the dynamical couplings between the links, the resulting model will turn out to be quite complex. We will eliminate some of this complexity by partially linearising the model. This is achieved by introducing a change of coordinates which enables us to partition the model into an actuated part (the joint angles of the snake robot) and an unactuated part (the position and orientation of the snake robot). Through an input transformation, we are then able to linearise the actuated part of the model.

In this chapter, we describe the development of the snake robot Kulko. While the snake robot Wheeko was used as the experimental platform in Part I of this book, Kulko serves as the experimental platform in Part II. The joint modules of Kulko are covered by contact force sensors to allow the robot to sense its environment, and spherical shells that give the robot a smooth outer surface, thereby allowing slithering (gliding) motion in uneven and cluttered environments. In the following, we will detail the design and implementation of the robot, and present experimental results that validate the function of the contact force measurement system. At the end of this chapter, we will also propose an alternative strategy for contact force sensing, which has the advantage that the sensor system can be well protected inside the snake robot.

Research on snake robots has been conducted for several decades. However, our understanding of snake locomotion so far is for the most part based on empirical studies of biological snakes and simulation-based synthesis of relationships between parameters of the snake robot. Armed with the mathematical model of the snake robot presented earlier in this book, we attempt in this chapter to contribute to the understanding of snake robots by employing nonlinear system analysis tools for investigating fundamental properties of their dynamics. We will also derive several interesting properties of snake robot locomotion simply by investigating the equations of motion of the robot, some of which will be instrumental in the development of a simplified model later in this book.

In the previous chapter, we extended our toolbox with a new and more manageable model of snake robot locomotion. With this new model at our disposal, an intriguing question is whether we can use the model to derive new properties of snake robot dynamics. In this chapter, we will show that this indeed is the case.

In this chapter, we turn to the problem of controlling the heading and position of the snake robot, and in particular, we consider the problem of enabling the robot to track a straight path. Straight line path following capabilities are important for many future applications of snake robots since they enable a snake robot to follow a desired path given by waypoints interconnected by straight lines.

We begin the second part of this book by extending the model presented at the beginning of this book to include contact forces from external obstacles in the environment around the snake robot. Since the interaction with an obstacle represents a discrete event that only occurs when a link of the snake robot comes into contact with an obstacle, the robot will be subjected to both continuous and discontinuous dynamics in this environment. We will therefore describe the dynamics of the snake robot in terms of a hybrid model.

In this chapter, we return to the problem of enabling a snake robot to track a planar path. Previously in this book, we proposed a straight line path following controller and employed a Poincare map to prove that the state variables of the snake robot, except for the position along the path, trace out a locally exponentially stable periodic orbit during motion along the desired path. The drawback of the analysis based on the Poincare map, however, is that we are only able to infer conclusions regarding the local stability properties in the vicinity of the desired path. Moreover, since the analysis is based on simulating the model of the snake robot, the stability proof is only valid for the given choice of numerical controller parameters.

With respect to control design, this chapter represents the culmination of this book and can be regarded as a fusion of the control efforts reported in the previous chapters. In particular, forward propulsion of snake robots on planar surfaces based on oscillatory body shape changes was studied early in this book. Subsequently, this oscillatory gait pattern was extended with directional control capabilities that enabled the snake robot to track straight paths and paths defined by waypoints. The oscillatory gait pattern was further extended in the previous chapter with environment adaptation capabilities that enabled the snake robot to maintain forward propulsion in environments with obstacles. Directional control was, however, not considered in the previous chapter.

The work underlying this book includes the development of two mechanical snake robots. The first snake robot is called Wheeko and was developed to enable experiments related to snake robot locomotion across flat surfaces. Wheeko is described in more detail in this chapter. The second snake robot, which is called Kulko, is described in a later chapter of this book and was developed for the purpose of experiments related to snake robot locomotion in environments containing obstacles. The internal structure of Wheeko and Kulko are identical. The difference between the two robots concern their outer structure. The joint modules of Wheeko are equipped with passive wheels to give the robot anisotropic ground friction properties during motion across flat surfaces. The joint modules of Kulko, on the other hand, are covered by contact force sensors and spherical shells that give the robot a smooth outer surface, thereby allowing gliding motion in uneven and cluttered environments.

The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed. (c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.