Marko Munih

Analysis of standing up and sitting down in humans: Definitions and normative data presentation

A formal definition of human standing up and sitting down movements based on sagittal plane goniometric and force plate data from 20 normal subjects is presented. This definition is comparable to the established gait cycle diagram, and consists of defined characteristic events and relative time intervals between them. The characteristic events are selected primarily on changes in ground reaction forces. The terminology proposed may be valuable for introducing more formalized and standardized reporting of both qualitative and quantitative studies in both normals and in patients. This presentation is directed toward the process of defining generally acceptable standards for human standing up and sitting down movements.

Upper limb motion analysis using haptic interface

An objective test for evaluating the functional studies of the upper limbs (UL) in patients with neurological diseases (ND) is presented. The method allows assessment of kinematic and dynamic motor abilities of UL. Our methodology is based on creating a virtual environment, using a computer display for visual information and a PHANTOM haptic interface. The haptic interface is used as a kinematic measuring device and for providing tactile feedback to the patient. In virtual environment, a labyrinth in patients frontal plane was created at the start of each test. By moving the haptic interface control stick the patient was able to move the pointer (a ball) through the labyrinth in three dimensions and to feel the reactive forces of the wall. The new test offers a wide range of numerical and graphic results. It has so far been applied to 13 subjects with various forms of ND (e.g., Friedreich Ataxia, Parkinsons disease, Multiple Sclerosis) as well as to healthy subjects. The comparison in performance between right and left UL has been carried out in healthy subjects.

Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle

To restore functional use of paralyzed muscles by automatically controlled stimulation, an accurate quantitative model of the stimulated muscles is desirable. The most commonly used model for isometric muscle has had a Hammerstein structure, in which a linear dynamic block is preceded by a static nonlinear function. To investigate the accuracy of the Hammerstein model, the responses to a pseudo-random binary sequence (PRES) excitation of normal human plantarflexors, stimulated with surface electrodes, were used to identify a Hammerstein model but also four local models which describe the responses to small signals at different mean levels of activation. Comparison of the local models with the linearized Hammerstein model showed that the Hammerstein model concealed a fivefold variation in the speed of response. Also, the small-signal gain of the Hammerstein model was in error by factors up to three. We conclude that, despite the past widespread use of the Hammerstein model, it is not an accurate representation of isometric muscle. On the other hand, local models, which are more accurate predictors, can be identified from the responses to short PRES sequences. The utility of local models for controller design is discussed.

A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing

Physiological computing represents a mode of human-computer interaction where the computer monitors, analyzes and responds to the users psychophysiological activity in real-time. Within the field, autonomic nervous system responses have been studied extensively since they can be measured quickly and unobtrusively. However, despite a vast body of literature available on the subject, there is still no universally accepted set of rules that would translate physiological data to psychological states. This paper surveys the work performed on data fusion and system adaptation using autonomic nervous system responses in psychophysiology and physiological computing during the last ten years. First, five prerequisites for data fusion are examined: psychological model selection, training set preparation, feature extraction, normalization and dimension reduction. Then, different methods for either classification or estimation of psychological states from the extracted features are presented and compared. Finally, implementations of system adaptation are reviewed: changing the system that the user is interacting with in response to cognitive or affective information inferred from autonomic nervous system responses. The paper is aimed primarily at psychologists and computer scientists who have already recorded autonomic nervous system responses and now need to create algorithms to determine the subjects psychological state.

Feedback control of unsupported standing in paraplegia. I. Optimal control approach

This is the first of a pair of papers which describe an investigation into the feasibility of providing artificial balance to paraplegics using electrical stimulation of the paralyzed muscles. By bracing the body above the shanks, only stimulation of the plantarflexors is necessary. This arrangement prevents any influence from the intact neuromuscular system above the spinal cord lesion. In this paper, we extend the design of the controllers to a nested-loop LQG (linear quadratic Gaussian) stimulation controller which has ankle moment feedback (inner loops) and inverted pendulum angle feedback (outer loop). Each control loop is tuned by two parameters, the control weighting and an observer rise-time, which together determine the behavior. The nested structure was chosen because it is robust, despite changes in the muscle properties (fatigue) and interference from spasticity.

Rehabilitation robotics

The paper presents the background, main achievements and components of rehabilitation robotics in a simple way, using non-technical terms. The introductory part looks at the development of robotic approaches in the rehabilitation of neurological patients and outlines the principles of robotic device interactions with patients. There follows a section on virtual reality in rehabilitation. Hapticity and interaction between robot and human are presented in order to understand the added value of robotics that cannot be exploited in other devices. The importance of passive exercise and active tasks is then discussed using the results of various clinical trials, followed by the place of upper and lower extremity robotic devices in rehabilitation practice. The closing section refers to the general importance of measurements in this area and stresses quantitative measurements as one of the advantages in using robotic devices.

HIFE-haptic interface for finger exercise

A haptic device with two active degrees of freedom and a tendon-driven transmission system was designed, built, and tested. It was constructed as a mechanism with a small workspace that envelops a finger workspace and can generate forces up to 10 N, suitable for finger exercise. Kinematic and dynamic model equations of the haptic device are presented in the paper. The control strategies, the implementation of the application on a PC, the real-time millisecond-class control environment, running under the MS Widows operating system, and safety mechanisms are described. Also, the duration test for the maximum sustained output force, and validations of accuracy of the output force and the consistency of the followed path, were performed. The performance, accuracy, and safety of the device were found to be very good, which makes the device suitable for rehabilitation purposes.

Implantable telemeter for long-term electroneurographic recordings in animals and humans

A system is described that amplifies an electroneurographic signal (ENG) from a tripolar electrode nerve cuff and transmits it from the implanted amplifier to an external drive box. The output was raw ENG, bandpass filtered from 800 to 8000 Hz. The implant was powered by radio-frequency induction and operated for coil-to-coil separations up to 30 mm. The testing and performance of the system is described. The input-referred noise was never more than 1μV RMS, and, at some positions of the radio-frequency field, was 0.7μV, close to the expected value for the amplifier used. The common-mode rejection ratio (CMRR) depended on the impedance imbalance from the cuff and the length of input cable. Devices with a short cable and low source impedance had CMRR of 84 dB, but, with 31 cm of cable and a real cuff, the CMRR fell to 66 dB. Recovery from a stimulus artifact took 5ms. The responses of the cuff to external potential gradients and to common-mode signals are described theoretically or by simulation. The devices are available for use in neuroprosthetic or neurophysiological research.

Automated detection of gait initiation and termination using wearable sensors

This paper presents algorithms for detection of gait initiation and termination using wearable inertial measurement units and pressure-sensitive insoles. Body joint angles, joint angular velocities, ground reaction force and center of plantar pressure of each foot are obtained from these sensors and input into supervised machine learning algorithms. The proposed initiation detection method recognizes two events: gait onset (an anticipatory movement preceding foot lifting) and toe-off. The termination detection algorithm segments gait into steps, measures the signals over a buffer at the beginning of each step, and determines whether this measurement belongs to the final step. The approach is validated with 10 subjects at two gait speeds, using within-subject and subject-independent cross-validation. Results show that gait initiation can be detected timely and accurately, with few errors in the case of within-subject cross-validation and overall good performance in subject-independent cross-validation. Gait termination can be predicted in over 80% of trials well before the subject comes to a complete stop. Results also show that the two sensor types are equivalent in predicting gait initiation while inertial measurement units are generally superior in predicting gait termination. Potential use of the algorithms is foreseen primarily with assistive devices such as prostheses and exoskeletons.

Three-Axial Accelerometer Calibration Using Kalman Filter Covariance Matrix for Online Estimation of Optimal Sensor Orientation

Inexpensive inertial/magnetic measurement units can be found in numerous applications and are typically used to determine orientation. Due to the presence of nonidealities in measurement systems, the calibration of the sensor is thus needed to determine sensor parameters such as bias, misalignment, and gain/sensitivity. In this paper, an online automatic calibration method for a three-axial accelerometer is presented. Parameters are estimated using an unscented Kalman filter. The sensor is placed in a number of different orientations using a robotic arm. These orientations are calculated online from the parameter covariance matrix and represent estimated optimal sensor orientations for parameter estimation. Numerous simulations are run to evaluate the proposed calibration method, and a comparison is made with an offline least mean squares calibration method. The simulation results show that calibration with the proposed method results in higher accuracy of parameter estimation when using less than 100 iterations. The proposed calibration method is also applied to a real accelerometer using a low number of iterations. The results show only slight (less than 0.4%) changes in parameter values between different calibration runs. The proposed calibration method provides an accurate parameter estimation using a small number of iterations without the need for manually predefining orientations of the sensor, and the method can be used in combination with other offline calibration methods to achieve even higher accuracy.