Nonlinear state-space modeling of human motion using 2-D marker observations

A novel method for the estimation of human kinematics, based on state-space modeling, is proposed. The state consists of the positions, orientations, velocities, and accelerations of an articulated model. Estimation is performed using the unscented Kalman filter (UKF) algorithm with a fixed-interval smoother. Impulsive acceleration at floor contact of the foot is estimated by implementing a contact constraint in the UKF evolution model. The constraint inserts an acceleration impulse into the model state. The estimation method was applied to marker-based motion analysis in a motion laboratory. Validation measurements were performed with a rigid test device and with human gait. A triaxial accelerometer was used to evaluate acceleration estimates. Comparison between the proposed method and the extended Kalman smoother showed a clear difference in the quality of estimates during impulsive accelerations. The proposed approach enables estimation of human kinematics during both continuous and transient accelerations. The approach provides a novel way of estimating acceleration at foot initial contact, and thus enables more accurate evaluation of loading from the beginning of the floor contact.

Kubios HRV--heart rate variability analysis software

Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis. The software supports several input data formats for electrocardiogram (ECG) data and beat-to-beat RR interval data. It includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection. The software computes all the commonly used time-domain and frequency-domain HRV parameters and several nonlinear parameters. There are several adjustable analysis settings through which the analysis methods can be optimized for different data. The ECG derived respiratory frequency is also computed, which is important for reliable interpretation of the analysis results. The analysis results can be saved as an ASCII text file (easy to import into MS Excel or SPSS), Matlab MAT-file, or as a PDF report. The software is easy to use through its compact graphical user interface. The software is available free of charge for Windows and Linux operating systems at http://kubios.uef.fi.

Asynchronicity of facial blood perfusion in migraine

Asymmetrical changes in blood perfusion and asynchronous blood supply to head tissues likely contribute to migraine pathophysiology. Imaging was widely used in order to understand hemodynamic variations in migraine. However, mapping of blood pulsations in the face of migraineurs has not been performed so far. We used the Blood Pulsation Imaging (BPI) technique, which was recently developed in our group, to establish whether 2D-imaging of blood pulsations parameters can reveal new biomarkers of migraine. BPI characteristics were measured in migraineurs during the attack-free interval and compared to healthy subjects with and without a family history of migraine. We found a novel phenomenon of transverse waves of facial blood perfusion in migraineurs in contrast to healthy subjects who showed synchronous blood delivery to both sides of the face. Moreover, the amplitude of blood pulsations was symmetrically distributed over the face of healthy subjects, but asymmetrically in migraineurs and subjects with a family history of migraine. In the migraine patients we found a remarkable correlation between the side of unilateral headache and the direction of the blood perfusion wave. Our data suggest that migraine is associated with lateralization of blood perfusion and asynchronous blood pulsations in the facial area, which could be due to essential dysfunction of the autonomic vascular control in the face. These findings may further enhance our understanding of migraine pathophysiology and suggest new easily available biomarkers of this pathology.

Non-Linear EMG Parameters for Differential and Early Diagnostics of Parkinson's Disease

The pre-clinical diagnostics is essential for management of Parkinson’s disease (PD). Although PD has been studied intensively in the last decades, the pre-clinical indicators of that motor disorder have yet to be established. Several approaches were proposed but the definitive method is still lacking. Here we report on the non-linear characteristics of surface electromyogram (sEMG) and tremor acceleration as a possible diagnostic tool, and, in prospective, as a predictor for PD. Following this approach we calculated such non-linear parameters of sEMG and accelerometer signal as correlation dimension, entropy, and determinism. We found that the non-linear parameters allowed discriminating some 85% of healthy controls from PD patients. Thus, this approach offers considerable potential for developing sEMG-based method for pre-clinical diagnostics of PD. However, non-linear parameters proved to be more reliable for the shaking form of PD, while diagnostics of the rigid form of PD using EMG remains an open question.

Acral coldness in migraineurs

In search for new biomarkers of vascular disturbances accompanying migraine, we compared the facial and hand skin temperatures in 41 women, including 12 migraine patients during the headache-free period and 29 healthy controls. Compared to the controls, the acral skin temperatures were lower in migraineurs, especially in those with right-sided headache. Our findings suggest that migraine is associated with a peripheral coldness possibly due to abnormal autonomic vascular control. The cold nose and hands may represent easily assessable biomarkers of these disorders.

Nitroglycerin-induced changes in facial skin temperature: 'cold nose' as a predictor of headache?

Nitroglycerin (NTG) often induces headaches when used to treat cardiac diseases. Such property of NTG has been widely used in modelling of migraine-like headaches. However, background reasons, predisposing to the development of NTG-headache, are less studied. The main aim of our study was to find, using NTG model, easily accessible markers of the vascular changes associated with headache. Because changes in the blood flow alter the local skin temperature (Tsk), we studied the relationship between the regional changes in the facial Tsk and NTG-induced headaches. Tsk was measured with infrared thermography in 11 healthy women during 3 h after sublingual NTG administration. NTG caused headache in five women, and four of them were the first-degree relatives of migraine patients. Notably, before NTG administration, subjects in the headache group had lower Tsk values, especially in the nose area, than women in the pain-free group (n = 6). NTG-induced headache was associated with a long-lasting increase of Tsk over the baseline. In sharp contrast, in the pain-free group, the Tsk reduced and returned rapidly to the baseline. Thus, the low baseline level and greater increase of regional Tsk correlated with the incidence of headache that supports a role of greater vascular changes in headache happening on the basis of the dissimilarities in vascular tone. An easily accessible phenomenon of 'cold nose' may indicate background vascular dysfunctions in individuals with predisposition to headache. Facial infrared thermography, coupled with NTG administration, suggests a novel temporally controlled approach for non-invasive investigation of vascular processes accompanying headaches.

Monitoring functional impairment and recovery after traumatic brain injury in rats by FMRI

The present study was designed to test a hypothesis that functional magnetic resonance imaging (fMRI) can be used to monitor functional impairment and recovery after moderate experimental traumatic brain injury (TBI). Moderate TBI was induced by lateral fluid percussion injury in adult rats. The severity of brain damage and functional recovery in the primary somatosensory cortex (S1) was monitored for up to 56 days using fMRI, cerebral blood flow (CBF) by arterial spin labeling, local field potential measurements (LFP), behavioral assessment, and histology. All the rats had reduced blood-oxygen-level-dependent (BOLD) responses during the 1st week after trauma in the ipsilateral S1. Forty percent of these animals showed recovery of the BOLD response during the 56 day follow-up. Unexpectedly, no association was found between the recovery in BOLD response and the volume of the cortical lesion or thalamic neurodegeneration. Instead, the functional recovery occurred in rats with preserved myelinated fibers in layer VI of S1. This is, to our knowledge, the first study demonstrating that fMRI can be used to monitor post-TBI functional impairment and consequent spontaneous recovery. Moreover, the BOLD response was associated with the density of myelinated fibers in the S1, rather than with neurodegeneration. The present findings encourage exploration of the usefulness of fMRI as a noninvasive prognostic biomarker for human post-TBI outcomes and therapy responses.

EMG signal morphology in essential tremor and Parkinson's disease

The aim of this work was to differentiate patients with essential tremor from patients with Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's disease, and 40 healthy controls. The EMG signals were high pass filtered and divided to smaller segments from which histograms were calculated using 200 histogram bins. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.

Differences in heart rate variability of female nurses between and within normal and extended work shifts

The aim of this study was to investigate differences in heart rate variability (HRV) reflecting the function of autonomic nervous system (ANS) and psycho-physiological strain associated with normal and extended work shifts in nursing work. Complete data were available from 51 female nurses with a mean age of 40 yr, and based on two comparable 36-h HRV measurements supplemented with a questionnaire. Time-domain (meanRR, SDNN, RMSSD) and frequency-domain (LF power, HF power) parameters represented the HRV data, and were analyzed by linear mixed models. The differences between the compared work shifts were minor, revealing mainly increased sympathetic activity at the beginning of the normal work shift. The HRV parameters detected significant differences between work and leisure-time during the normal and extended work shifts in female nurses. During work shifts, an increase in sympathetic and a decrease in parasympathetic control of HRV was observed when compared to the leisure-time situation. Older subjects had overall lower HRV than younger subjects indicating increased sympathetic activation of ANS, especially during work. HRV parameters revealed significant differences between work, leisure-time and sleep of female nurses, but there were few differences between normal and extended work shifts in HRV parameters. This lack of differences between work shifts may be a consequence of the adaptation of nurses to the extended shifts or the more flexible organization of work duties possible during extended work shifts.

Hypoglycemia detection based on cardiac repolarization features

Hypoglycemia is known to affect repolarization characteristics of the heart. These changes are shown from ECG by prolonged QT-time and T-wave flattening. In this study we constructed a classifier based on these ECG parameters. By using the classifier we tried to detect hypoglycemic events from measurements of 22 test subjects. Hypoglycemic state was achieved using glucose clamp technique. Used test protocol consisted of three stages: normoglycemic period, transition period (blood glucose concentration decreasing) and hypoglycemic period. Subjects were divided into three groups: 9 healthy controls (Healthy), 6 otherwise healthy type 1 diabetics (T1DM) and 7 type 1 diabetics with disease complications (T1DMc). Detection of hypoglycemic event could be made passably from 15/22 measurements. In addition, we found that detection process is easier for healthy and T1DM groups than T1DMc group diabetics because in T1DMc group subjects' have lower autonomic response to hypoglycemic events. Also we noticed that changes in ECG occurs few minutes after blood glucose is decreased below 3.5 mmol/1.

Kinematic and kinetic changes in obese gait in bariatric surgery-induced weight loss

This study examines the effects of a radical bariatric surgery-induced weight loss on the gait of obese subjects. We performed a three-dimensional motion analysis of lower limbs, and collected force platform data in the gait laboratory to calculate knee and hip joint moments. Subjects (n=13) performed walking trials in the laboratory before and 8.8 months (SD 4.2) after the surgical procedure at two gait speeds (1.2m/s and 1.5m/s). The average weight loss was 26.7kg (SD 9.2kg), corresponding to 21.5% (SD 6.8%) of the initial weight. We observed a decrease in step width at both gait speeds, but no changes in relative double support or swing time or stride length. A significant decrease was noted in the absolute values of peak knee abductor, peak knee flexor and peak hip extensor moments. However, the moment values normalized by the body weight and height remained unchanged in most cases. Thus, we conclude that weight loss reduces hip and knee joint moments in proportion to the amount of weight lost.

Heart rate variability dynamics during low-dose propofol and dexmedetomidine anesthesia

Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

Estimation of arterial baroreflex sensitivity in relation to carotid artery stiffness

Arterial baroreflex has a significant role in regulating blood pressure. It is known that increased stiffness of the carotid sinus affects mecanotransduction of baroreceptors and therefore limits baroreceptors capability to detect changes in blood pressure. By using high resolution ultrasound video signal and continuous measurement of electrocardiogram (ECG) and blood pressure, it is possible to define elastic properties of artery simultaneously with baroreflex sensitivity parameters. In this paper dataset which consist 38 subjects, 11 diabetics and 27 healthy controls was analyzed. Use of diabetic and healthy test subjects gives wide scale of arteries with different elasticity properties, which provide opportunity to validate baroreflex and artery stiffness estimation methods.

Analysis of EMG and acceleration signals for quantifying the effects of deep brain stimulation in Parkinson's disease

Deep brain stimulation (DBS) is effective in reducing motor symptoms in Parkinson's disease (PD). However, objective methods for quantifying its efficacy are lacking. We present a principal component (PC)-based tracking method for quantifying the effects of DBS in PD by using electromyography (EMG) and acceleration measurements. Ten parameters capturing PD characteristic signal features were initially extracted from isometric EMG and acceleration recordings. Using a PC approach, the original parameters were transformed into a smaller number of PCs. Finally, the effects of DBS were quantified by examining the PCs in a low-dimensional feature space. The EMG and acceleration data from 13 PD patients with DBS ON and OFF, and 13 healthy age-matched controls were used for analysis. Clinical evaluation of patients showed that their motor symptoms were effectively reduced with DBS. The analysis results showed that the signal characteristics of 12 patients were more similar to those of the healthy controls with DBS ON than with DBS OFF. These observations indicate that the PC-based tracking method can be used to objectively quantify the effects of DBS on the neuromuscular function of PD patients. Further studies are suggested to estimate the clinical sensitivity of the method to different types of PD.

Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects

Hypoglycemia is known to affect the repolarization characteristics of the heart, but the mechanisms behind these changes are not completely understood. We analyzed repolarization characteristics continuously from 22 subjects during normoglycemic period, transition period (blood glucose concentration decreasing) and hypoglycemic period from nine healthy controls (Healthy), six otherwise healthy type 1 diabetics (T1DM) and seven type 1 diabetics with disease complications (T1DMc). An advanced principal component regression (PCR)-based method was used for estimating ECG parameters beat-by-beat, and thus, continuous comparison between the repolarization characteristics and blood glucose values was made. We observed that hypoglycemia related ECG changes in the T1DMc group were smaller than changes in the Healthy and T1DM groups. We also noticed that when glucose concentration remained at a low level, the heart rate corrected QT interval prolonged progressively. Finally, a few minutes time lag was observed between the start of hypoglycemia and cardiac repolarization changes. One explanation for these observations could be that hypoglycemia related hormonal changes have a significant role behind the repolarization changes. This could explain at least the observed time lag (hormonal changes are slow) and the lower repolarization changes in the T1DMc group (hormonal secretion lowered in long duration diabetics).

Analysis of heart rate variability dynamics during propofol and dexmedetomidine anesthesia

It has been observed that heart rate variability (HRV) diminishes during anesthesia, but the exact mechanisms causing it are not completely understood. The aim of this paper was to study the dynamics of HRV during low dose propofol (N=9) and dexmedetomidine (N=8) anesthesia by using state-of-the-art time-varying methods, and thereby ultimately try to improve the safety of anesthesia. The time-varying spectrum is estimated by using a Kalman smoother approach. The results show that there is an overall increase in HRV and decrease in heart rate prior to loss of consciousness. For dexmedetomidine these changes are more considerable than for propofol. For dexmedetomidine the variability also seems to start decreasing right after loss of consciousness, whereas for propofol HRV continues increasing.

EMG and acceleration signal analysis for quantifying the effects of medication in Parkinson's disease

Parkinson's disease (PD) is characterized by motor disabilities that can be alleviated reasonably with appropriate medication. However, there is a lack of objective methods for quantifying the efficacy of treatment in PD. We applied here an objective method for quantifying the effects of medication in PD using EMG and acceleration measurements and analysis. In the method, four signal features were calculated from the EMG and acceleration recordings of both sides of the body: the kurtosis and recurrence rate of EMG, and the amplitude and sample entropy of acceleration. Principal component approach was used for reducing the number of variables. EMG and acceleration data measured from nine PD patients were used for analysis. The patients were measured in four different medication conditions: with medication off, and two and three and four hours after taking the medication. The results showed that in eight patients the EMG recordings changed into less spiky and the acceleration recordings into more complex after taking the medication. A reverse phenomenon in the signal characteristics was observed in seven patients 3-4 hours after taking the medication. The results indicate that the presented method is potentially useful for quantifying objectively the effects of medication on the neuromuscular function in PD.

Discrimination of EMG and acceleration measurements between patients with Parkinson's disease and healthy persons

In this paper, we examine the potential of electromyographic (EMG) and acceleration measurements in discriminating patients with Parkinson's disease (PD) from healthy persons. Two types of muscle contractions are examined: static contractions of biceps brachii muscles and elbow extension movements. Twelve features are extracted from static and ten features from extension measurements. These features describe signal morphology and nonlinear characteristics, power spreading in EMG wavelet scalograms and spectral coherence. Principal component approach is applied separately for static and extension trial to reduce the number of features before discrimination. The discrimination between subjects is done in a two-dimensional space by applying cluster analysis to the best discriminating principal components. The discrimination power of the used method was estimated with EMG and acceleration data measured from 56 patients with PD and 59 healthy controls. In the cluster analysis, three clusters were formed: one cluster with most (85%) of the healthy persons and two clusters with 80% of patients. Patients were divided into two clusters based on their type of motor disability (problems during movement and/or static contraction). Discrimination results show that EMG and acceleration measurements are potential for discriminating patients with PD from healthy persons. Furthermore, they have potential in the objective clinical assessment of PD.

Method for Testing Motion Analysis Laboratory Measurement Systems

This paper proposes a method for comparing data from accelerometers, optical based 3D motion capture systems, and force platforms (FPs) in the context of spatial and temporal differences. Testing method is based on the motion laboratory accreditation test (MLAT), which can be used to test FP and camera based motion capture components of a motion analysis laboratory. This study extends MLAT to include accelerometer data. Accelerometers were attached to a device similar to the MLAT rod. The elevation of the rod from the plane of the floor is computed and compared with the force platform vector orientation and the rod orientation obtained by optical motion capture system. Orientation of the test device is achieved by forming nonlinear equation group, which describes the components of the measured accelerations. Solution for this equation group is estimated by using the Gauss–Newton method. This expanded MLAT procedure can be used in the laboratory setting were either FP, camera based motion capture, or any other motion capture system is used along with accelerometer measurements.

Postural control and thigh muscle activity in men with knee osteoarthritis

The aim of this study was to examine the standing balance and the function of vastus medialis (VM) and biceps femoris (BF) muscles with surface electromyography (EMG). Fifty-four subjects with uni- or bilateral knee osteoarthritis (OA) (aged 50-69 years) and 53 age-matched randomly selected clinically and radiologically healthy men participated in this study. Postural control was assessed on a force platform with a bipedal stance with eyes open (EO) and closed (EC) and a monopedal stance with EO. The balance parameters, mean sway velocity, velocity along AP and ML axes, elliptical area, standard deviation of center of pressure, average radial displacement, mean frequency and frequency domain balance parameters and different power spectral density frequency bands were determined. Root mean square (RMS) for EMG amplitude, mean EMG frequency (f(EMG,mean)) and median EMG frequency (f(EMG,med)) of motor unit activity were calculated from the normalized EMG data. During bipedal stance with EC and EO, there were no significant differences in balance parameters between groups, but during bipedal stance with EO, the RMS in VM was about 56% higher (p<0.05) in subjects with knee OA than in the control subjects and the values of f(EMG,mean) and f(EMG,med) were about 48% higher (p<0.05) in control subjects than subjects with knee OA. It is concluded that subjects with knee OA do not have any standing balance deficit, but they do exhibit increased muscle activity in VM muscle compared to control subjects.

Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm

A time-varying parametric spectrum estimation method for analyzing dynamics of heart rate variability (HRV) signals is presented. In the method, HRV signal is first modeled with a time-varying autoregressive model and the model parameters are solved recursively with a Kalman smoother algorithm. Time-varying spectrum estimates are then obtained from the estimated model parameters. The obtained spectrum can be further decomposed into separate components, which is especially advantageous in HRV applications where low frequency (LF) and high frequency (HF) components are generally aimed to be distinguished. As case studies, the dynamics of HRV signals recorded during 1) orthostatic test, 2) exercise test and 3) simulated driving task are analyzed.

A principal component regression approach for estimation of ventricular repolarization characteristics

The time interval between Q-wave onset and T-wave offset, i.e., QT interval, in an ECG corresponds to the total ventricular activity, including both depolarization and repolarization times. It has been suggested that abnormal QT variability could be a marker of cardiac diseases such as ventricular arrhythmias, and QT-interval has also been observed to lengthen during hypoglycemia. In this paper, we propose a robust method for estimating ventricular repolarization characteristics such as QT interval and T-wave amplitude. The method is based on principal component regression. In the method, QT epochs are first extracted from ECG in respect of R-waves. Then, correlation matrix of the extracted epochs is formed and its eigenvectors computed. The most significant eigenvectors are then fitted to the data to obtain noise-free estimates of QT epochs. Nonstationarities in QT-epoch characteristics can also be modeled by updating the eigenvectors dynamically. The main benefit of the proposed method is robustness to noise, i.e., it works also when using ECGs that have low SNR, for example, signals measured during normal-life environments. One application of the proposed method could be the detection of the hypoglycemia.