Magnetic-free Extended Kalman Filter for upper limb kinematic assessment in Yoga.

Human motion analysis is gaining increased importance in several fields, from movement assessment in rehabilitation to recreational applications such as virtual coaching. Among all the technologies involved in motion capture, Magneto-Inertial Measurements Units (MIMUs) is one of the most promising due to their small dimensions and low costs. Nevertheless, their usage is strongly limited by different error sources, among which magnetic disturbances, which are particularly problematic in indoor environments. Inertial Measurement Units (IMUs) could, thus, be considered as alternative solution. Indeed, relying exclusively on accelerometers and gyroscopes, they are insensitive to magnetic disturbances. Even if the literature has started to propose few algorithms that do not take into account magnetometer input, their application is limited to robotics and aviation. The aim of the present work is to introduce a magnetic-free quaternion based Extended Kalman filter for upper limb kinematic assessment in human motion (i.e., yoga). The algorithm was tested on five expert yoga trainers during the execution of the sun salutation sequence. Joint angle estimations were compared with the ones obtained from an optoelectronic reference system by evaluating the Mean Absolute Errors (MAEs) and Pearson's correlation coefficients. The achieved worst-case was 6.17°, while the best one was 2.65° for MAEs mean values. The accuracy of the algorithm was further confirmed by the high values of the Pearson's correlation coefficients (lowest mean value of 0.86).Clinical Relevance- The proposed work validated a magnetic free algorithm for kinematic reconstruction with inertial units. It could be used as a wearable solution to track human movements in indoor environments being insensitive to magnetic disturbances, and thus could be potentially used also for rehabilitation purposes.

Perception of Time-Discrete Haptic Feedback on the Waist is Invariant With Gait Events.

The effectiveness of haptic feedback devices highly depends on the perception of tactile stimuli, which differs across body parts and can be affected by movement. In this study, a novel wearable sensory feedback apparatus made of a pair of pressure-sensitive insoles and a belt equipped with vibrotactile units is presented; the device provides time-discrete vibrations around the waist, synchronized with biomechanically-relevant gait events during walking. Experiments with fifteen healthy volunteers were carried out to investigate users' tactile perception on the waist. Stimuli of different intensities were provided at twelve locations, each time synchronously with one pre-defined gait event (i.e. heel strike, flat foot or toe off), following a pseudo-random stimulation sequence. Reaction time, detection rate and localization accuracy were analyzed as functions of the stimulation level and site and the effect of gait events on perception was investigated. Results revealed that above-threshold stimuli (i.e. vibrations characterized by acceleration amplitudes of 1.92g and 2.13g and frequencies of 100 Hz and 150 Hz, respectively) can be effectively perceived in all the sites and successfully localized when the intertactor spacing is set to 10 cm. Moreover, it was found that perception of time-discrete vibrations was not affected by phase-related gating mechanisms, suggesting that the waist could be considered as a preferred body region for delivering haptic feedback during walking.

Movement analysis in early infancy: Towards a motion biomarker of age.

BACKGROUND: Early motor development is characterized by progressive changes in general movements paralleled by a gradual organization of the four limbs' repertoire towards the midline, as shown by computerised movement analysis. AIMS: Our aim was to test the performance of quantitative computerised kinematic indexes as predictors of post-term age in an independent cohort of typically developing subjects at fidgety age, tested cross-sectionally. SUBJECTS: We selected twelve low risk term infants, who were video recorded between 9 and 20 weeks (fidgety age) during one spontaneous movements session. STUDY DESIGN: We correlated post-term age with I)indexes of coordination including interlimb correlation of velocity and position, II)indexes of distance, including interlimb and limb-to- ground, both expressed as linear distance and as probability of midline limbs position III)indexes of global movement quality by calculating Hjorth's activity, mobility and complexity parameters. All indexes were calculated for both upper and lower limbs. RESULTS: Significant positive correlations were found between post-term age and indexes of distance, and probability of occurrence of upper-limb antigravity patterns, and with both indexes of global movement quality. By combining linear and non-linear parameters related to the upper limb kinematics, we determined individual post-term age with a mean error of <1 week (5.2 days). No correlations were found between age and indexes of coordination. CONCLUSIONS: Quantitative computerised analysis of upper-limb movements is a promising predictor of post-term age in typically developing subjects at fidgety age.

Accuracy of the Orientation Estimate Obtained Using Four Sensor Fusion Filters Applied to Recordings of Magneto-Inertial Sensors Moving at Three Rotation Rates.

Magneto-Inertial technology is a well-established alternative to optical motion capture for human motion analysis applications since it allows prolonged monitoring in free-living conditions. Magneto and Inertial Measurement Units (MIMUs) integrate a triaxial accelerometer, a triaxial gyroscope and a triaxial magnetometer in a single and lightweight device. The orientation of the body to which a MIMU is attached can be obtained by combining its sensor readings within a sensor fusion framework. Despite several sensor fusion implementations have been proposed, no well-established conclusion about the accuracy level achievable with MIMUs has been reached yet. The aim of this preliminary study was to perform a direct comparison among four popular sensor fusion algorithms applied to the recordings of MIMUs rotating at three different rotation rates, with the orientation provided by a stereophotogrammetric system used as a reference. A procedure for suboptimal determination of the parameter filter values was also proposed. The findings highlighted that all filters exhibited reasonable accuracy (rms errors <; 6.4°). Moreover, in accordance with previous studies, every algorithm's accuracy worsened as the rotation rate increased. At the highest rotation rate, the algorithm from Sabatini (2011) showed the best performance with errors smaller than 4.1° rms.

Pre-impact detection algorithm to identify lack of balance due to tripping-like perturbations.

This study investigates the performance of an updated version of our pre-impact detection algorithm while parsing out hip kinematics in order to identify unexpected tripping-like perturbations during walking. This approach grounds on the hypothesis that due to unexpected gait disturbances, the cyclic features of hip kinematics are suddenly altered thus promptly highlighting that the balance is challenged. To achieve our goal, hip angles of eight healthy young subjects were recorded while they were managing unexpected tripping trials delivered during the steady locomotion. Results showed that the updated version of our pre-impact detection algorithm allows for identifying a lack of balance due to tripping-like perturbations, after a suitable tuning of the algorithm parameters. The best performance is represented by a mean detection time ranging within 0.8-0.9 s with a low percentage of false alarms (i.e., lower than 10%). Accordingly, we can conclude that the proposed strategy is able to detect lack of balance due to different kinds of gait disturbances (e.g., slippages, tripping) and that it could be easily implemented in lower limb orthoses/prostheses since it only relies on joint angles.

Evaluation of time-frequency features as detectors of lack of balance due to tripping-like perturbations.

Unbalancing events during gait can end up in falls and, thus, injury. Detecting events that could bring to fall and consequently activating fall prevention systems before the impact may help to mitigate related injuries. However, there is uncertainty about signals and methods that could offer the best performance. In this paper we investigated a novel trip detection method based on time-frequency features to evaluate the performances of these features as trip detectors. Hip angles of eight healthy young subjects were recorded while performing unexpected tripping trials delivered during steady locomotion. Then the Short-Time Fourier Transform (STFT) of the hip angle was estimated. Median frequency, power, centroidal frequency as well as frequency dispersion were computed for each time sliced power spectrum. These features were used as input for a trip detection algorithm. We assessed detection time (Tdetect), specificity (Spec) and sensitivity (Sens) for each feature. Performances obtained with median frequencies over time(Tdetect 0.91 ± 0.47 s; Sens 0.96) were better than those obtained using the hip angle signal in time domain (Tdetect 1.19 ± 0.27 s; Sens 0.83). Other features did not show significant results. Thus, median frequency over time expected to achieve effective real-time event detection systems, with the aim of a future on-board application concerning detection and prevention measures.

A novel functional calibration method for real-time elbow joint angles estimation with magnetic-inertial sensors.

Magnetic-inertial measurement units (MIMUs) are often used to measure the joint angles between two body segments. To obtain anatomically meaningful joint angles, each MIMU must be computationally aligned (i.e., calibrated) with the anatomical rotation axes. In this paper, a novel four-step functional calibration method is presented for the elbow joint, which relies on a two-degrees-of-freedom elbow model. In each step, subjects are asked to perform a simple task involving either one-dimensional motions around some anatomical axes or a static posture. The proposed method was implemented on a fully portable wearable system, which, after calibration, was capable of estimating the elbow joint angles in real time. Fifteen subjects participated in a multi-session experiment that was designed to assess accuracy, repeatability and robustness of the proposed method. When compared against an optical motion capture system (OMCS), the proposed wearable system showed an accuracy of about 4° along each degree of freedom. The proposed calibration method was tested against different MIMU mountings, multiple repetitions and non-strict observance of the calibration protocol and proved to be robust against these factors. Compared to previous works, the proposed method does not require the wearer to maintain specific arm postures while performing the calibration motions, and therefore it is more robust and better suited for real-world applications.

Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis.

The proposed strap-down integration method exploits the cyclical nature of human gait: during the gait swing phase, the quaternion-based attitude representation is integrated using a gyroscope from initial conditions that are determined during stance by an accelerometer. Positioning requires double time integration of the gravity-compensated accelerometer signals during swing. An interpolation technique applied to attitude quaternions was developed to improve the accuracy of orientation and positioning estimates by accounting for the effect of sensor bias and scale factor drifts. A simulation environment was developed for the analysis and testing of the proposed algorithm on a synthetic movement trajectory. The aim was to define the true attitude and positioning used in the computation of estimation errors. By thermal modelling, the changes of bias and scale factor of the inertial sensors, calibrated at a single reference temperature, were analysed over a range of +/- 10 degrees C, for measurement noise standard deviations up to sigma(g) = 2.5 degrees s(-1) (gyroscope) and sigma(a) = 0.05 m s(-2) (accelerometer). The compensation technique reduced the maximum root mean square errors (RMSEs) to: RMSE(theta) = 14.6 degrees (orientation) and RMSE(d) = 17.7cm (positioning) for an integration interval of one gait cycle (an improvement of 3 degrees and 7 cm); RMSE(theta) = 14.8 degrees and RMSE(d) = 30.0 cm for an integration interval of two gait cycles (an improvement of 11 degrees and 262 cm).

Polymer electret guidance channels enhance peripheral nerve regeneration in mice.

Polytetrafluoroethylene (PTFE) tubes were prepared as electrets displaying a quasi-permanent surface charge due to the presence of trapped monopolar charge carriers. PTFE tubes containing either positive or negative charges and electrically neutral PTFE tubes were used as nerve guidance channels for the repair of a 4 mm nerve gap in the sciatic nerve of mice. After 4 weeks of implantation, positively and negatively charged PTFE electrets contained regenerated nerves with significantly more myelinated axons than nerves regenerated in uncharged PTFE tubes. This observation suggests that peripheral nerve regeneration can be enhanced by electrically charged nerve guidance channels.

A statistical mechanical analysis of postural sway using non-Gaussian FARIMA stochastic models.

In this paper, postural sway is modeled using a fractional autoregressive integrated moving average (FARIMA) family of models: the center-of-pressure (COP) motion is viewed in terms of a self-similar, anti-persistent random-walk process, obtained by fractionally summating non-Gaussian random variables, whose correlation structure for small time lags is shaped by a linear time-invariant low-pass filter. The model parameters are: the strength of the stochastic driving, e.g., the root mean square (rms) value of the time-difference COP motion; the DC gain, damping ratio and natural frequency of the filter; the Hurst exponent, which measures the random-walk antipersistence magnitude. In the proposed modeling procedure, a graphical estimator for determining the Hurst exponent is cascaded to a method for matching autoregressive (AR) models to fractionally difference COP motion via higher order cumulants. The effect of the presence or absence of vision on the model parameter values is discussed with regard to data from experiments on healthy young adults.

Analysis of postural sway using entropy measures of signal complexity.

A stochastic complexity analysis is applied to centre-of-pressure (COP) time series, by using different complexity features, namely the spectral entropy, the approximate entropy, and the singular value decomposition spectrum entropy. A principal component analysis allows an estimate of the overall signal complexity in terms of the ensemble complexity score; the difference in values between open-eyes (OE) and closed-eyes (CE) trials is used for clustering purposes. In experiments on healthy young adults, the complexity of the mediolateral component is shown not to depend on the manipulation of vision. Conversely, the increase of the anteroposterior complexity in OE conditions can be statistically significant, leading to a functional division of the subjects into two groups: the Romberg ratios (RRs), namely the ratios of the CE measure to the OE measure, are: RR = 1.19 +/- 0.15 (group 1 subjects), and RR = 1.05 +/- 0.14 (group 2 subjects). Multivariate statistical techniques are applied to the complexity features and the parameters of a postural sway model recently proposed; the results suggest that the complexity change is the sign of information-generating behaviours of postural fluctuations, in the presence of a control strategy which aims at loosening long-range correlation and decreasing stochastic activity when visual feedback is allowed.

Real-time Kalman filter applied to biomechanical data for state estimation and numerical differentiation.

This study focused on the application of real-time Kalman filters to biomechanical data and, in particular, the simulation environment used to compare the performance of modified and standard two-state Kalman filters when estimating displacement and velocity from noisy displacement data. The modification proposed in this paper was the numerical tachometer, augmented by a median smoother. The numerical tachometer integrated the derivative estimates from finite differences of noisy sampled data into the Kalman filter structure; the median smoother acted before differentiation, to protect from grossly erroneous measurements. The numerical tachometer allowed better fits to the simulated data than can be achieved without it: the root mean square errors decreased by 10% in the displacement domain and by 54% in the velocity domain, for sampling frequencies and signal contamination levels that were typical in human movement sciences. The sensitivity to errors in the modelling of the signal and noise characteristics was less than in the standard filter implementation. The use of the median smoother improved the robustness of the filtering algorithm against additive white Gaussian measurement noise and allowed the cancellation of isolated noise spikes.

Artificial neural network model of the mapping between electromyographic activation and trajectory patterns in free-arm movements.

Artificial neural networks (ANNs) have been used to identify the relationship between electromyographic (EMG) activity and arm kinematics during the execution of motor tasks. Although considerable work has been devoted to showing that ANNs perform this mapping, there has been little work to explore any relationship with physiological properties of the neuromuscular systems. A back-propagation through time (BPTT) ANN was used to map the EMG of five selected muscles (pectoralis major (PM), anterior deltoid (AD), posterior deltoid (PD), biceps brachii (BB) and triceps brachii (TB)) on arm kinematics in seven normal subjects performing three-dimensional unrestrained grasping movements. To investigate the physiological validity of the BPTT-ANN, inputs were artificially altered, and the predicted outputs were analysed. Results show that the BPTT-ANN performed the mapping correctly (root mean square (RMS) error between target and predicted outputs averaged across subject test sets was 0.092 +/- 0.015). Moreover, it provided insights into the roles of muscles in performing the movement (average indexes measuring the output alteration with respect to the target were 0.070 +/- 0.027, 0.356 +/- 0.172, 0.568 +/- 0.413, 0.510 +/- 0.268, 0.681 +/- 0.430 for PM, AD, PD, BB, TB, respectively, in the movement forward phase, and 0.077 +/- 0.015, 0.179 +/- 0.147, 0.291 +/- 0.247, 0.671 +/- 0.054, 0.232 +/- 0.097 in the return phase).

Adaptive fuzzy control of electrically stimulated muscles for arm movements.

A modified adaptive Takagi-Sugeno (TS) fuzzy logic controller (FLC) is proposed that allows a simulated elbow-like biomechanical system to accurately track sigmoidal and sinusoidal trajectories in the sagittal plane. The work is a first effort towards the implementation of a system to restore elbow movements in quadriplegics using functional neuromuscular stimulation. The single-joint musculo-skeletal system is composed of a co-contractable pair of electrically stimulated muscles; the muscle model accounts for the increase in fatigue during the tracking exercise. In the proposed controller structure, a reinforcement learning scheme is used to accomplish the parameter tuning, and the parameter projection algorithm guarantees the system stability during the adaptation process. The controller performance is evaluated using computer simulation experiments and compared with the performance achievable when a standard proportional-integrative-derivative (PID) controller is employed for the same application. The modified adaptive TSFLC outperforms the PID controller in all tested situations, with a clear-cut advantage in the case of high-frequency sinusoidal trajectories (angular frequencies spanning the interval 8-12 rad s-1). The standard controller suffers from a dramatic increase in root mean square (RMS) tracking error above the value at 8 rad s-1, e.g. ERMS > or = 0.013, whereas the correlation coefficient between the actual and desired trajectory falls almost to zero, starting from the value rho approximately equal to 0.97 at 8 rad s-1. On the other hand, the adaptive TSFLC yields ERMS < or = 0.015, with rho > or = 0.78, over the whole range of tested angular frequencies.

On automatic identification of upper-limb movements using small-sized training sets of EMG signals.

We evaluate the performance of a variety of neural and fuzzy networks for discrimination among three planar arm-pointing movements by means of electromyographic (EMG) signals, when learning is based on small-sized training sets. The aim of this work is to underline the importance that the sparse data problem has in designing pattern classifiers with good generalisation properties. The results indicate that one of the proposed fuzzy networks is more robust than the other classifiers when working with small training sets.

An algorithm for detecting the onset of muscle contraction by EMG signal processing.

The Generalized Likelihood Ratio (GLR) test is proposed for estimating the time instant of muscle contraction onset via electromyographic (EMG) signal processing. In contrast to commonly used threshold-based estimation methods, the proposed algorithm proves to be reasonably accurate even for low levels of EMG activity; the improved behaviour of the GLR test comes with just a modest increase in the computational complexity. A set of computer simulation experiments are presented, where the proposed algorithm and two different threshold-based estimators are studied; also, we discuss the results of analysing the EMG recordings of selected proximal muscles of the upper limb in a hemiparetic subject.

A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques.

In this paper, a hybrid approach is presented for discriminating a few upper limb movements by processing the electromyographic (EMG) signals from selected shoulder muscles. Statistical techniques, such as the Generalized Likelihood Ratio test, the Principal Component Analysis, autoregressive parametric modeling techniques and cepstral analysis techniques, combined with a fuzzy logic based classifier (the Abe-Lan network) are used to construct low-dimensional feature spaces with high classification rates. The experimental results show the ability of the algorithm to correctly classify all the EMG patterns related to the selected planar arm pointing movements. Moreover, the structure presented offers promise for real-time applications because of the low computation costs of the overall algorithm.