Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data.

Changes in gait patterns provide important information about individuals' health. To perform sensor based gait analysis, it is crucial to develop methodologies to automatically segment single strides from continuous movement sequences. In this study we developed an algorithm based on time-invariant template matching to isolate strides from inertial sensor signals. Shoe-mounted gyroscopes and accelerometers were used to record gait data from 40 elderly controls, 15 patients with Parkinson's disease and 15 geriatric patients. Each stride was manually labeled from a straight 40 m walk test and from a video monitored free walk sequence. A multi-dimensional subsequence Dynamic Time Warping (msDTW) approach was used to search for patterns matching a pre-defined stride template constructed from 25 elderly controls. F-measure of 98% (recall 98%, precision 98%) for 40 m walk tests and of 97% (recall 97%, precision 97%) for free walk tests were obtained for the three groups. Compared to conventional peak detection methods up to 15% F-measure improvement was shown. The msDTW proved to be robust for segmenting strides from both standardized gait tests and free walks. This approach may serve as a platform for individualized stride segmentation during activities of daily living.

The effect of sprint and endurance training on electromyogram signal analysis by wavelets.

The purpose of this study was to relate the spectral changes of surface electromyograms (EMGs) to training regimes. The EMGs of M. vastus medialis and M. vastus lateralis of 8 female sprint-trained and 7 female endurance-trained athletes (ST and ET athletes) were examined while performing isokinetic knee extension on a dynamometer under 4 different loading conditions (angular velocity and load). The EMG signals were wavelet transformed, and the corresponding spectra were classified using a spherical classification, support vector machines (SVMs) and mean frequencies (MFs). Consistent differences in the EMG spectra between the 2 groups were expected because of the difference in the muscle features resulting from the various training regimes. On average, the ST athletes showed a downshift in the EMG spectra compared with the ET athletes. The spectra of the ST and ET athletes were classifiable by spherical classification and SVM but not by the MF. Thus, the different shapes of the EMG spectra contained the information for the classification. The hypothesis that specific muscle differences caused by various training regimes are consistent and lead to systematic changes in surface EMG spectra was confirmed. With the availability of new apparels, ones with integrated EMG electrodes, a measurement of the EMG will be available to coaches more frequently in the near future. The classification of wavelet transformed EMGs will allow monitoring training-related spectral changes.

C-arm CT imaging with the extended line-ellipse-line trajectory: first implementation on a state-of-the-art robotic angiography system.

Three-dimensional cone-beam imaging has become valuable in interventional radiology. Currently, this tool, referred to as C-arm CT, employs a circular short-scan for data acquisition, which limits the axial volume coverage and yields unavoidable cone-beam artifacts. To improve flexibility in axial coverage and image quality, there is a critical need for novel data acquisition geometries and related image reconstruction algorithms. For this purpose, we previously introduced the extended line-ellipse-line trajectory, which allows complete scanning of arbitrary volume lengths in the axial direction together with adjustable axial beam collimation, from narrow to wide depending on the targeted application. A first implementation of this trajectory on a state-of-the-art robotic angiography system is reported here. More specifically, an assessment of the quality of this first implementation is presented. The assessment is in terms of geometric fidelity and repeatability, complemented with a first visual inspection of how well the implementation enables imaging an anthropomorphic head phantom. The geometric fidelity analysis shows that the ideal trajectory is closely emulated, with only minor deviations that have no impact on data completeness and clinical practicality. Also, mean backprojection errors over short-term repetitions are shown to be below the detector pixel size at field-of-view center for most views, which indicates repeatability is satisfactory for clinical utilization. These repeatability observations are further supported by values of the Structural Similarity Index Metric above 94% for reconstructions of the FORBILD head phantom from computer-simulated data based on repeated data acquisition geometries. Last, the real data experiment with the anthropomorphic head phantom shows that the high contrast features of the phantom are well reconstructed without distortions as well as without breaks or other disturbing transition zones, which was not obvious given the complexity of the data acquisition geometry and the major variations in axial coverage that occur over the scan.

Combined accelerometer and EMG analysis to differentiate essential tremor from Parkinson's disease.

In this study, we intended to differentiate patients with essential tremor (ET) from tremor dominant Parkinson disease (PD). Accelerometer and electromyographic signals of hand movement from standardized upper extremity movement tests (resting, holding, carrying weight) were extracted from 13 PD and 11 ET patients. The signals were filtered to remove noise and non-tremor high frequency components. A set of statistical features was then extracted from the discrete wavelet transformation of the signals. Principal component analysis was utilized to reduce dimensionality of the feature space. Classification was performed using support vector machines. We evaluated the proposed method using leave one out cross validation and we report overall accuracy of the classification. With this method, it was possible to discriminate 12/13 PD patients from 8/11 patients with ET with an overall accuracy of 83%. In order to individualize this finding for clinical application we generated a posterior probability for the test result of each patient and compared the misclassified patients, or low probability scores to available clinical follow up information for individual cases. This non-standardized post hoc analysis revealed that not only the technical accuracy but also the clinical accuracy limited the overall classification rate. We show that, in addition to the successful isolation of diagnostic features, longitudinal and larger sized validation is needed in order to prove clinical applicability.

Absolute and relative intrasession reliability of surface EMG variables for voluntary precise forearm movements.

The reliability of surface electromyography (EMG) derived parameters is of high importance, but there is distinct lack of studies concerning the reliability during dynamic contractions. Especially Amplitude, Fourier and Wavelet parameter in conjunction have not been tested so far. The interpretation of the EMG variables might be difficult because the movement itself introduces additional factors that affect its characteristics. The aim of this study was to determine the relative and absolute intrasession reliability of electromyographic (EMG) variables of selected arm muscles during concurrent precise elbow extension/flexion movements at different force levels and movement speed. Participants (all-male: n = 17, range 20-32 years) were asked to adapt to a gross-motor visuomotor tracking task (elbow extension/flexion movement) using a custom-built lever arm apparatus. After sufficient adaptation surface electromyography was used to record the electrical activity of mm. biceps brachii, brachioradialis and triceps brachii, and the signal amplitude (RMS [µV]) and the mean frequency of the power spectrum (MNF [Hz]) were computed. Additionally Wavelet analysis was used. Relative reproducibility (intraclass correlation) for signal amplitude, mean frequency of the power spectrum and Wavelet intensity during dynamic contractions was fair to good, independent of force level and movement speed (ICC = 0.71-0.98). The amount of absolute intrasession reliability (coefficient of variation) of EMG variables depends on muscle and force level.

Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices.

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.

Marker-based classification of young-elderly gait pattern differences via direct PCA feature extraction and SVMs.

The classification of gait patterns has great potential as a diagnostic tool, for example, for the diagnosis of injury or to identify at-risk gait in the elderly. The purpose of the paper is to present a method for classifying group differences in gait pattern by using the complete spatial and temporal information of the segment motion quantified by the markers. The classification rates that are obtained are compared with previous studies using conventional classification features. For our analysis, 37 three-dimensional marker trajectories were collected from each of our 24 young and 24 elderly female subjects while they were walking on a treadmill. Principal component analysis was carried out on these trajectories to retain the spatial and temporal information in the markers. Using a Support Vector Machine with a linear kernel, a classification rate of 95.8% was obtained. This classification approach also allowed visualisation of the contribution of individual markers to group differentiation in position and time. The approach made no specific assumptions and did not require prior knowledge of specific time points in the gait cycle. It is therefore directly applicable for group classification tasks in any study involving marker measurements.

Automatic recognition of Parkinson's disease using surface electromyography during standardized gait tests.

Diagnosis and severity staging of Parkinsons disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls. EMG signals were recorded from 5 patients with PD and 5 younger healthy controls, while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier. Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for PD.

Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities.

The segmentation of gait signals into single steps is an important basis for objective gait analysis. Only a precise detection of step beginning and end enables the computation of step parameters like step height, variability and duration. A special challenge for the application is the accurateness of such an algorithm when based on signals from daily live activities. In this study, gyroscopes were attached laterally to sport shoes to collect gait data. For the automated step segmentation, subsequence Dynamic Time Warping was used. 35 healthy controls and ten patients with Parkinson's disease performed a four times ten meter walk. Furthermore 4 subjects were recorded during different daily life activities. The algorithm enabled counting steps, detecting precisely step beginning and end and rejecting other movements. Results showed a recognition rate of steps during ten meter walk exercises of 97.7% and in daily life activities of 86.7%. The segmentation procedure can be used for gait analysis from daily life activities and can constitute the basis for computation of precise step parameters. The algorithm is applicable for long-term gait monitoring as well as for analyzing gait abnormalities.

Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease.

Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.

Somnography using unobtrusive motion sensors and Android-based mobile phones.

Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50% of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. Ten healthy male subjects were recorded during two consecutive nights. Sensors from the Shimmer platform were placed in the bed to record accelerometer data, while reference hypnograms were collected using a SOMNOwatch system. A series of filters were used to extract a motion feature in 30 second epochs from the accelerometer signals. The feature was used together with the ground truth information to train a Naive Bayes classifiers that distinguished wakefulness, REM and non-REM sleep. Additionally the algorithm was implemented on an Android mobile phone. Averaged over all subjects, the classifier had a mean accuracy of 79.0 % (SD 9.2%) for the three classes. The mobile phone implementation was able to run in realtime during all experiments. In future this will lead to a method for simple and unobtrusive somnography using mobile phones.

Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices.

We developed an application for Android™-based mobile devices that allows real-time electrocardiogram (ECG) monitoring and automated arrhythmia detection by analyzing ECG parameters. ECG data provided by pre-recorded files or acquired live by accessing a Shimmer™ sensor node via Bluetooth™ can be processed and evaluated. The application is based on the Pan-Tompkins algorithm for QRS-detection and contains further algorithm blocks to detect abnormal heartbeats. The algorithm was validated using the MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia databases. More than 99% of all QRS complexes were detected correctly by the algorithm. Overall sensitivity for abnormal beat detection was 89.5% with a specificity of 80.6%. The application is available for download and may be used for real-time ECG-monitoring on mobile devices.

A wireless trigger for synchronization of wearable sensors to external systems during recording of human gait.

Mobile gait analysis focuses on the automatic extraction of gait parameters from wearable sensor data. However, development of algorithms for this task requires kinematic data with accurate and highly synchronous ground truth. In this paper we present a wireless trigger system which allows reliable synchronization of wearable sensors to external systems providing ground truth. To demonstrate the applicability of the system for mobile gait analysis, a Shimmer wireless sensor node with inertial sensors was mounted at the heel of a running shoe and synchronized with an external VICON motion capturing system using the wireless trigger system. Inertial sensor data were recorded during walking and running with the shoe, while kinematic and kinetic ground truth was acquired from the synchronized VICON system. Evaluation of delay and jitter of the system showed a mean delay of 2 ms and low jitter of 20 us. Recording was highly synchronous and the collected kinematics had a correlation of up to 0.99. In the future the proposed system will allow the creation of a database of inertial data from human gait with accurate ground truth synchronization.

Classification of muscle activity based on effort level during constant pace running.

During running, psychologic and physiologic changes are manifested in the perception of effort, muscle properties and movement strategies. The latter two aspects are expressed as changes in electromyographic (EMG) activity. This paper tests the hypothesis that the EMG signals change in a systematic way during a run and that these changes are related to the effort level of the runner. Fifteen female recreational runners performed 1-h treadmill runs at a constant speed (95% of speed at ventilatory threshold). EMG signals were recorded from four muscles (tibialis anterior, gastrocnemius medialis, vastus lateralis, and semitendinosus). The wavelet transformed EMG data were used to discriminate between different effort phases of running using a support vector machine (SVM) approach. The effect of the penalty parameter, C, and cross validation folds, n, used were evaluated and found to have little influence on the outcome. Recognition rates of >80% were achieved for all C and n values across all muscles. Average recognition rates were: TA - 89.2, GM - 88.3%, VL - 84.6% and ST - 94.0%. These results suggest that selected lower extremity EMG signals using wavelet-based methods contained highly systematic differences that could be used by the SVM to discriminate between the low- and high-effort stages of prolonged running.

Piper rhythm in the activation of the gastrocnemius medialis during running.

The presence of temporal rhythmicity in electromyographic (EMG) signals at frequencies of 35-60 Hz was initially noted by Piper (1907). This modulation and synchronization of motor unit activity is generally accepted to represent a centrally generated coding of motor commands. The purpose of this study was to resolve and quantify the Piper rhythm in the gastrocnemius medialis (GM) muscle during running. EMG was recorded from the GM of 14 female runners during 1-h treadmill runs. The average wavelet transform was computed for EMG from series of steps taken at 2 min intervals throughout the run. The total intensity across three wavelets (center frequencies: 170, 218 and 271 Hz) was computed and a histogram indicating the incidence peaks in this signal was generated for each subject. In order to rule out effects of the analysis process, the process was repeated using simulated EMG data. Autocorrelations of the histograms were used to extract the frequency of the peaks resulting in rhythmicity at 25-55 Hz. The ability to measure superimposed rhythmicity in EMG signals during dynamic tasks allows investigation of the role of aspects of central drive during movement. In particular, the changes in central control during dynamic activities can be examined with this approach.

Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease.

Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists.