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This article investigates the effect of additional calibration modules (CMs) in a 3-D real-time localization system during the calibration of the anchor position. A quick calibration is desirable for new anchors that are positionally undetermined in the working coordinate system. Three localization methods were tested for the anchor calibration: multidimensional scaling, semidefinite programming, and iterative trilateration. First, the accuracy of the anchor localization was studied by simulating a change in the number of additional CMs and their positions. Second, tests on a real system with ultrawideband modules were performed to validate the improvements in the anchor calibration when using the additional CMs. The experimental results revealed an improvement in the anchor localization for all three methods, where the average positional error was improved by 0.01 m in the first scenario and 0.30 m in the second scenario. The MDS method had the best absolute performance, with an average positional error that was as much as two times less in comparison with the other two methods. This investigation demonstrates that the positional error can be successfully reduced by using additional CMs. The calibration of anchor positions in the working coordinate system using additional CMs resulted in a 3-D error of less than 0.32 m. [ABSTRACT FROM AUTHOR]

This paper presents a sensory fusion method for estimation of joint angles of serial kinematic chains with rotational degrees of freedom based on magnetoinertial measurements—Magnetoinertial tracking based on JAcobian PseudoInverse (MIJAPI). The concept takes into account the mechanism kinematic model, and the computation relies on the differential kinematics inversion (inverse kinematics solution based on the Jacobian inverse). A Moore–Penrose weighted left pseudoinverse of the mechanism Jacobian matrix is applied to solve a (typically) overdetermined system (redundant measurements resulting from constraints related to attachments of magnetoinertial sensors) in a least-squares approach. Calculation of a gain matrix for correcting the estimated angles is based on Kalman-adaptive algorithm. The quality of the proposed approach was compared to different solutions based on the Unscented Kalman filter. In terms of computational complexity, the MIJAPI concept outperforms the Kalman-based approaches. Better results were also noticed in conditions with significant measurement disturbances and sensor misalignments. The method is applicable in the fields of human motion tracking/analysis as well as robotics. [ABSTRACT FROM AUTHOR]

The direction of the Earth’s magnetic field is used as a reference vector to determine the heading in orientation estimation with wearable sensors. However, the magnetic field strength is weak and can be easily disturbed in the vicinity of ferromagnetic materials, which may result in inaccurate estimate of orientation. This paper presents a novel method for estimating and compensating for magnetic disturbances. The compensation algorithm is implemented within a kinematic-based extended Kalman filter and is based on an assessment of the magnetic disturbance and the change of orientation in each time step. The proposed algorithm was experimentally validated by measuring the orientation of a simple mechanical system with three degrees of freedom in an artificially disturbed magnetic field. The results of the experimental evaluation show that an Kalman filter algorithm that incorporates compensating for magnetic disturbances is capable of estimating the orientation with moderate error (the absolute median errors \Delta \widetilde \theta X,\Delta \widetilde \theta Z < {2}^{\circ } , \Delta \widetilde \theta Y < {9}^{\circ } ) when the Earth’s magnetic field is disturbed by magnetic disturbance with a magnitude equal to twice the magnitude of the Earth’s own magnetic field in different directions. [ABSTRACT FROM PUBLISHER]

Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy-logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10 000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities. [ABSTRACT FROM PUBLISHER]

State-of-the-art technologies empower people with motor disabilities to carry out activities of daily living, thus enabling a better quality of life. Personal mobility is crucial for the well-being of individuals with motor impairments. In fact, studies have shown that, among people with motor disabilities, those having better mobility report greater satisfaction with their quality of life than those having lower mobility [1]-[3]. Motor functions can be improved, recovered, or partially substituted with various robot-based technologies, such as robotic prostheses, exoskeletons, and electric wheelchairs. Robotic wheelchairs allow those with motor disabilities practical and efficient electric mobility. [ABSTRACT FROM PUBLISHER]

This article describes the design of an innovative system for early intervention (EI) at home in infancy. The aim is to develop a smart device capable of promoting and measuring the actions of infants in the first months of life. The CareToy system, inspired by a commercially available gym for infants and equipped with a variety of sensors, can provide an intensive, individualized, home-based and familycentered EI program remotely telemonitored by clinicians. An array of sensors measures the activity of infants inside the gym. The sensor signals related to infant's movement, interaction with toys, and pressure distribution are acquired and processed in real time to classify the infant's activities and behavior. Rewards (feedback) are provided to the infants on the basis of their activity. Data are interpreted offline in terms of clinical meaning, and experimental tests are also reported. [ABSTRACT FROM AUTHOR]

Bioinspiration in robotics deals with applying biological principles to the design of better performing devices. In this article, we propose a novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton. In particular, the use of motor primitives for assisting different locomotion modes (i.e., ground-level walking at several cadences and ascending and descending stairs) is explored by means of two different strategies. In the first strategy, identified motor primitives are combined through weights to directly produce the desired assistive torque profiles. In the second strategy, identified motor primitives are combined to serve as neural stimulations to a virtual model of the musculoskeletal system, which, in turn, produces the desired assistive torque profiles. [ABSTRACT FROM PUBLISHER]

Restoring the mobility of transfemoral dysvascular amputees is essential to their rehabilitation process. Impeding the restoration of mobility, exhaustion is often the cause of noneffective deambulation of elderly lowerlimb amputees using a prosthesis as they use more energy for locomotion than younger amputees do. This article presents finite-state control of a novel powered prosthesis prototype for transfemoral amputees based on whole-body awareness. Intention detection was implemented through a noninvasive, distributed wireless wearable sensory system. The cybernetic lower-limb cognitive orthoprosthesis (CYBERLEGs) system was evaluated in a study involving three amputees. The subjects were able to walk with the prosthesis without training, showing accurate performance of the intention detection. The functionality of the CYBERLEGs approach was confirmed by gait pattern analysis and intention detection statistics. [ABSTRACT FROM AUTHOR]

The inertial/magnetic measurement units are an affordable instrument for the determination of orientation. The sensors embedded in the system are affected by nonidealities that can be greatly compensated by proper calibration, by determining sensor parameters, such as bias, misalignment, and sensitivity/gain. This paper presents an online calibration method for a three-axial magnetometer using a 3-D Helmholtz coil. The magnetometer is exposed to different directions of the magnetic field created by the 3-D coil. The parameters are estimated by using an unscented Kalman filter. The directions are calculated online by using a sensor parameter covariance matrix. The method evaluation is achieved by first running numerous simulations, followed by experiments using a real magnetometer, finally resulting in better accuracy of parameter estimation with a low number of measurement iterations compared with the method where magnetic field directions are determined manually. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

This paper examines the usefulness of psychophysiological measurements in a biocooperative feedback loop that adjusts the difficulty of an upper extremity rehabilitation task. Psychophysiological measurements (heart rate, skin conductance, respiration, and skin temperature) were used both by themselves and in combination with task performance and biomechanics. Data fusion was performed with discriminant analysis, and a special adaptive version was implemented that can gradually adapt to a subject. Both healthy subjects and hemiparetic patients participated in the study. The accuracy of the biocooperative controller was defined as the percentage of times it matched the subjects' preferences. The highest accuracy rate was obtained for task performance (approximately 82% for both healthy subjects and patients), with psychophysiological measurements yielding relatively low accuracy (approximately 60%). The adaptive approach increased accuracy of psychophysiological measurements to 76.4% for healthy subjects and 68.8% for patients. Combining psychophysiology with task performance yielded an accuracy rate of 84.7% for healthy subjects and 89.4% for patients. Results suggest that psychophysiological measurements are not reliable as a primary data source in motor rehabilitation, but can provide supplementary information. However, it is questionable whether the amount of additional information justifies the increased complexity of the system. [ABSTRACT FROM PUBLISHER]

This paper presents the analysis of four psychophysiological responses in post-stroke upper extremity rehabilitation. The goal was to determine which psychophysiological responses would provide the most reliable information about subjects' psychological states during rehabilitation. Heart rate, skin conductance, respiration, and skin temperature were recorded in a stroke group and a control group during two difficulty levels of a pick-and-place task performed in a virtual environment using a haptic robot and during a cognitive task. Psychophysiological measurements were correlated with results of a self-report questionnaire. All four responses showed significant changes in response to the different tasks. Skin conductance differentiated between the two difficulty levels and was correlated with self-reported arousal in both stroke and control groups. Skin temperature differentiated between the two difficulty levels for the control group, but provided poor results for the stroke group. Heart rate and respiration increased during tasks, but their connection to psychological state was unclear. Results suggest that, of the four measured responses, skin conductance offers the most potential as a psychological state indicator, with other measures providing supplementary information. Psychophysiological measurements could thus be used in closed-loop biocooperative systems that would detect the user's psychological state and change the course of therapy accordingly. [ABSTRACT FROM PUBLISHER]

The article reports on the contact instability of admittance control of a haptic interface. A grasp force-measuring handle was designed to evaluate the influence of the human operator on the stability of the haptic interaction. The authors examined model stability with root-locus analysis. Experiments on an actual haptic interface were performed to verify the effects of contact instability. The experiment results showed that stability is highly dependent upon the stiffness of the human operator grasp.

A haptic device with two active degrees-of-freedom and a tendon-driven transmission system was designed, later on constructed, and submitted to testing. It was embodied as a light-weight mechanism with a small workspace that wraps the finger workspace and can generate forces up to 10 N suitable for finger exercise. The control loop and the user application were implemented on a personal computer in the Microsoft Windows environment. Along with the device, application covering several different experiment types was developed. The system was evaluated in a group of stroke patients during a one-month period of therapy. Results for two types of experiments are presented. The progress of the patient affected hand side was found to be greater than for the one nonaffected, however, the mean values of the relevant parameters on the nonaffected side of patients are higher than those for the affected side. Results were compared to the motor component of functional independence measure (M-FIM) measured clinical scale. [ABSTRACT FROM AUTHOR]