Report of an IEEE task force--an IEEE opinion on research needs for biomedical engineering systems.
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Artificial Intelligence↗
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A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.
This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.
Action Potentials↗
Introduction to the special issue on advances in clinical and health-care knowledge management.
Clinical and health-care knowledge management (KM) as a discipline has attracted increasing worldwide attention in recent years. The approach encompasses a plethora of interrelated themes including aspects of clinical informatics, clinical governance, artificial intelligence, privacy and security, data mining, genomic mining, information management, and organizational behavior. This paper introduces key manuscripts which detail health-care and clinical KM cases and applications.
Confidentiality↗
Adaptive force regulation of muscle strengthening rehabilitation device with magnetorheological fluids.
In rehabilitation from neuromuscular trauma or injury, strengthening exercises are often prescribed by physical therapists to recover as much function as possible. Strengthening equipment used in clinical settings range from low-cost devices, such as sandbag weights or elastic bands to large and expensive isotonic and isokinetic devices. The low-cost devices are incapable of measuring strength gains and apply resistance based on the lowest level of torque that is produced by a muscle group. Resistance that varies with joint angle can be achieved with isokinetic devices in which angular velocity is held constant and variable torque is generated when the patient attempts to move faster than the device but are ineffective if a patient cannot generate torque rapidly. In this paper, we report the development of a versatile rehabilitation device that can be used to strengthen different muscle groups based on the torque generating capability of the muscle that changes with joint angle. The device is low cost, is smaller than other commercially available machines, and can be programmed to apply resistance that is unique to a particular patient and that will optimize strengthening. The core of the device, a damper with smart magnetorheological fluids, provides passive exercise force. A digital adaptive control is capable of regulating exercise force precisely following the muscle strengthening profile prescribed by a physical therapist. The device could be programmed with artificial intelligence to dynamically adjust the target force profile to optimize rehabilitation effects. The device provides both isometric and isokinetic strength training and can be developed into a small, low-cost device that may be capable of providing optimal strengthening in the home.
Adaptation, Physiological↗
Development of hybrid genetic algorithms for product line designs.
In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operator's process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairy large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options.
Journal Article↗
Fuzzy differential inclusions in atmospheric and medical cybernetics.
Uncertainty management in dynamical systems is receiving attention in artificial intelligence, particularly in the fields of qualitative and model based reasoning. Fuzzy dynamical systems occupy a very important position in the class of uncertain systems. It is well established that the fuzzy dynamical systems represented by a set of fuzzy differential inclusions (FDI) are very convenient tools for modeling and simulation of various uncertain systems. In this paper, we discuss about the mathematical modeling of two very complex natural phenomena by means of FDIs. One of them belongs to the atmospheric cybernetics (the term has been used in a broad sense) of the genesis of a cyclonic storm (cyclogenesis), and the other belongs to the bio-medical cybernetics of the evolution of tumor in a human body. Since a discussion of the former already appears in a previous paper by the first author, here, we present very briefly a theoretical formalism of cyclone formation. On the other hand, we treat the latter system more elaborately. We solve the FDIs with the help of an algorithm developed in this paper to numerically simulate the mathematical models. From the simulation results thus obtained, we have drawn a number of interesting conclusions, which have been verified, and this vindicates the validity of our models.
Algorithms↗
From fuzzy logic toward plurimonism: the science of active and empathic observation.
Plurimonism is a new philosophy and method of science. It holds that the revolution in computer science and artificial intelligence has reached the point that all the sciences in general can now account for the complex relations of an irreducible plurality of unique observers engaged in describing the same event. Plurimonism seeks to describe the conscious and unconscious relations of the scientific observer during the act of observation of a given event while preserving the historical uniqueness and indivisible identity of each such observer. Using the framework of plurimonism, we mathematically formulate the problem of empathy. This self-reflective mathematical model entails four components of the empathic process involving two observers. They are: 1) the self; 2) the self's-other; 3) the other; and 4) the other's-self. It measures the degree of accuracy of the therapist-observer's empathy, as well as conscious and unconscious processes involved in the patient-observer's idealization and the therapist-observer's confidence in clinical psychotherapy. Ratings are obtained from both patient and therapist from four different points of view. The plural views of the patient's global assessment of functioning (GAF) are from: 1) the therapist's view (TGAF); 2) the patient's view (PGAF); 3) the therapist empathic view (TEGAF), which represents the therapist's estimate of PGAF; and 4) the patient's empathic estimate of the TGAF. The GAF scale is the standard dimensional 100-point-scale measure used in psychiatry for recording a patient's functioning. The patient's estimate of the therapist's degree of accuracy as well as the therapist's confidence in his or her empathic accuracy is also represented. Three formulae are presented that describe the degree of the therapist's empathic accuracy, the patient's over-idealization/under-idealization, and the therapist's over-confidence/under-confidence. The concept of empathy is here restricted to mean the degree to which one observer can take the point of view of another observer when both are observing the same thing.
Cybernetics↗
Evaluating alternative gait strategies using evolutionary robotics.
Evolutionary robotics is a branch of artificial intelligence concerned with the automatic generation of autonomous robots. Usually the form of the robot is predefined and various computational techniques are used to control the machine's behaviour. One aspect is the spontaneous generation of walking in legged robots and this can be used to investigate the mechanical requirements for efficient walking in bipeds. This paper demonstrates a bipedal simulator that spontaneously generates walking and running gaits. The model can be customized to represent a range of hominoid morphologies and used to predict performance parameters such as preferred speed and metabolic energy cost. Because it does not require any motion capture data it is particularly suitable for investigating locomotion in fossil animals. The predictions for modern humans are highly accurate in terms of energy cost for a given speed and thus the values predicted for other bipeds are likely to be good estimates. To illustrate this the cost of transport is calculated for Australopithecus afarensis. The model allows the degree of maximum extension at the knee to be varied causing the model to adopt walking gaits varying from chimpanzee-like to human-like. The energy costs associated with these gait choices can thus be calculated and this information used to evaluate possible locomotor strategies in early hominids.
Animals↗
Clinical reasoning in nursing.
This paper traces the development of the concept of nursing diagnosis and the various approaches being used to explain the cognitive processes used by practitioners in diagnosing patient problems. Three main types of explanation are compared; hypothesis generation/testing, decision analysis, and the information processing model. The recent development of the latter approach within the field of artificial intelligence is described and expert system research introduced. Finally, the potential benefits of the advent of expert systems into nursing are discussed.
Decision Support Techniques↗
Digital chameleons: automatic assimilation of nonverbal gestures in immersive virtual environments.
Previous research demonstrated social influence resulting from mimicry (the chameleon effect); a confederate who mimicked participants was more highly regarded than a confederate who did not, despite the fact that participants did not explicitly notice the mimicry. In the current study, participants interacted with an embodied artificial intelligence agent in immersive virtual reality. The agent either mimicked a participant's head movements at a 4-s delay or utilized prerecorded movements of another participant as it verbally presented an argument. Mimicking agents were more persuasive and received more positive trait ratings than nonmimickers, despite participants' inability to explicitly detect the mimicry. These data are uniquely powerful because they demonstrate the ability to use automatic, indiscriminate mimicking (i.e., a computer algorithm blindly applied to all movements) to gain social influence. Furthermore, this is the first study to demonstrate social influence effects with a nonhuman, nonverbal mimicker.
Acculturation↗
Predicting outcome of anterior temporal lobectomy using simulated neural networks.
PURPOSE: Anterior temporal lobectomy (ATL) is an important option for treatment of medically refractory seizures. Patient selection is not always clear-cut, and there is inherent morbidity and mortality associated with the invasive and expensive surgical protocols. To determine whether patient selection might be facilitated by application of artificial intelligence, we developed a model that predicted seizure outcome after ATL, using a simulated neural network (SNN). METHODS: Predictions of the model were compared with predictions derived from conventional discriminant function analysis. Neural networks and discriminant functions were devised that would predict the occurrence of both Class 1 outcomes (totally seizure-free), and Class 1 or Class 2 outcomes (nearly or totally seizure-free), using data from 87 patients from three surgical centers. The SNNs and discriminant functions were developed using data from a randomly selected subsample of 65 patients, and both models were cross-validated, using the remaining 22 patients. RESULTS: The discriminant functions showed overall predictive accuracy of 78.5% and 72.7%, while the neural networks demonstrated overall accuracy of 81.8% and 95.4%. CONCLUSIONS: Simulated neural networks show promise as adjuncts to decision-making in the selection of epilepsy surgery patients.
Adolescent↗
On asking the right questions.
Despite the vast array of theoretical formulations now available, none is sufficiently well developed. Some of the philosophical questions thrown up by such material are posed. Under the heading of "Discourse," the questions of what constitutes reasonableness and how logical validity is established are raised. Under the heading of "Epistemology," the nature of concept formulation and causality is raised. Under the heading of "Language," the nature of linguistic representation and artificially constructed language or "artificial intelligence" is raised. It is suggested that these and other issues in the philosophy of science require the urgent attention of family therapy theorists.
Communication↗
Sketching the future: trends influencing nursing informatics.
Technologies emerging in the fields of telecommunications, video and digital imaging, and microprocessing are shaping the future of nursing practice. To measure up to the future needs of nursing, nurses of today must have the vision and desire to become computer aware and technologically literate. Hypothetical future situations pose challenges related to current nursing informatics and artificial intelligence issues. Discussion includes technology issues related to the lifetime clinical health record. Areas that the Center for Nursing Research considers priorities for informatics suggest directions for nursing technology efforts. This article calls on all nurses to become active in designing and molding future clinical practice systems.
Computer Literacy↗
Identifying remote protein homologs by network propagation.
Perhaps the most widely used applications of bioinformatics are tools such as psi-blast for searching sequence databases. We describe a recently developed protein database search algorithm called rankprop. rankprop relies upon a precomputed network of pairwise protein similarities. The algorithm performs a diffusion operation from a specified query protein across the protein similarity network. The resulting activation scores, assigned to each database protein, encode information about the global structure of the protein similarity network. This type of algorithm has a rich history in associationist psychology, artificial intelligence and web search. We describe the rankprop algorithm and its relatives, and we provide evidence that the algorithm successfully improves upon the rankings produced by psi-blast.
Algorithms↗
Techniques in evaluating nursing expert systems: A case study.
This study addresses the problems in evaluating nursing diagnostic artificial intelligence (AI) expert systems. Two separate experiments (N = 49) were conducted using a computer expert system. The first experiment, the "white box" experiment (n = 9), compared the diagnostic techniques applied by experience RNs against the programmed techniques used by the expert system. The second experiment, the "black box" experiment (n = 40), compared diagnostic results of beginning nurses against the computer expert systems results. In some cases the computer outperformed the nurses and vice versa. The evaluation techniques, as applied in both experiments, enhance the ability of nurses to evaluate and select AI expert systems to be used in computer-assisted diagnosis of nursing problems.
Clinical Competence↗
Selective networks and recognition automata.
The results we have presented demonstrate that a network based on a selective principle can function in the absence of forced learning or an a priori program to give recognition, classification, generalization, and association. While Darwin II is not a model of any actual nervous system, it does set out to solve one of the same problems that evolution had to solve--the need to form categories in a bottom-up manner from information in the environment, without incorporating the assumptions of any particular observer. The key features of the model that make this possible are (1) Darwin II incorporates selective networks whose initial specificities enable them to respond without instruction to unfamiliar stimuli; (2) degeneracy provides multiple possibilities of response to any one stimulus, at the same time providing functional redundancy against component failure; (3) the output of Darwin II is a pattern of response, making use of the simultaneous responses of multiple degenerate groups to avoid the need for very high specificity and the combinatorial disaster that would imply; (4) reentry within individual networks vitiates the limitations described by Minsky and Papert for a class of perceptual automata lacking such connections; and (5) reentry between intercommunicating networks with different functions gives rise to new functions, such as association, that either one alone could not display. The two kinds of network are roughly analogous to the two kinds of category formation that people use: Darwin, corresponding to the exemplar description of categories, and Wallace, corresponding to the probabilistic matching description of categories. These principles lead to a new class of pattern-recognizing machine of which Darwin II is just an example. There are a number of obvious extensions to this work that we are pursuing. These include giving Darwin II the capability to deal with stimuli that are in motion, an ability that probably precedes the ability of biological organisms to deal with stationary stimuli, giving it the capability to deal with multiple stimulus objects through some form of attentional mechanism, and giving it a means to respond directly and to receive feedback from the world so that it can learn conventionally. Already, however, we have shown that a working pattern-recognition automaton can be built based on a selective principle. This development promises ultimately to show us how to build recognizing machines without programs and to provide a sound basis for the study of both natural and artificial intelligence.
Automation↗
Chaos in coupled optimizers.
Autonomous optimizers, a subclass of dynamical systems, arise on the intersection between artificial intelligence and deductive biology. Two simple optimizers coupled symmetrically à la Rashevsky give rise to chaotic attractors and (possibly) cloud attractors. More complicated autonomous optimizers (endowed with internal simulation) will be subject to more complicated interactional function changes. Humanistic ideas of Mead and Bateson may come within scientific reach. Conversely, the physical measurement problem may turn out to belong partly to the humanities.
Adaptation, Physiological↗
Extrapolation from animals to man. The integration of pharmacokinetics and pharmacodynamics.
This paper has focused on the difficulties of extrapolating toxicological or pharmacological data obtained from animals to those expected in man. For some drugs, under certain conditions, there may be no problem, but for many, this is clearly not the case. Differences in apparent activity are impossible to reconcile without "normalizing" the dose for differences in pharmacokinetics and metabolism. The increasing use of artificial intelligence and expert systems in drug investigations may provide a greater insight into why these differences may occur and allow prediction but, in the end, they must be tested in the experiments undertaken. The use of kinetic dynamic relationships in different species will certainly help in this regard and, wherever possible, should be included in experimental design to build up a database of experience since such information is sadly lacking. But we must interpret with caution the data produced by those that continue to extrapolate animal data to humans without some attempt to discuss in detail the validity of their assumptions.
Animals↗