Patrick Kugler

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

Motor impairments are the prerequisite for the diagnosis in Parkinsons 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.

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

Parkinsons 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.

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.

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.

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 Parkinsons 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.

Embedded Classification of the Perceived Fatigue State of Runners: Towards a Body Sensor Network for Assessing the Fatigue State during Running

This paper presents methods for collecting and analyzing biomechanical and physiological data from several body sensors during recreational runs in order to classify an athletes perceived fatigue state. Heart rate, heart rate variability, running speed, stride frequency and biomechanical data were recorded continuously from 431 runners during a free one-hour outdoor run. During the activity the sportsmen answered questions about their perceived fatigue state in 5 min intervals. The data were analyzed using specifically designed features computed for each of the 5 min intervals. The features were used to train different classifiers, which were able to distinguish two levels of the runners fatigue state with an accuracy of 88.3 % across multiple study participants. Feature selection evidenced that a heart rate variability feature and two biomechanical features were best suited for classification of the perceived fatigue level. Therefore, the classification system needs the information from various sensors on the human body. The resulting classifier was implemented on an embedded microcontroller to show that it would be feasible to integrate it directly into a body sensor network. Such a wearable classification system for fatigue can be used to support sportsmen, for example by changing their training plan or by adapting their equipment to the specific needs of a fatigued athlete.

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.

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

Huber, C, Göpfert, B, Kugler, PF-X, and von Tscharner, V. The effect of sprint and endurance training on EMG signal analysis by wavelets. J Strength Cond Res 24(6): 1527-1536, 2010-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.

Shimmer, Cooja and Contiki: A new toolset for the simulation of on-node signal processing algorithms

Wearable sensors are widely used for data collection in many applications. Ssensor nodes have also been applied for real-time applications, e.g. for ECG analysis or activity and fall detection. Processing of the sensor data is either done on an external device or on the node itself. While on-node processing reduces data rate and increases battery life, development and testing can be time-consuming. To allow faster implementation of such algorithms, we propose a simulation framework for the Shimmer platform using the Cooja simulator, MSPSim and the Contiki operating system. We provide the simulator and example applications compatible with the ShimmerConnect protocol, allowing streaming of raw and pre-processed sensor data to MATLAB, LabView and Android. Additionally, a simple activity and fall detection algorithm was implemented on the sensor node and evaluated using both the simulator and real hardware. In the future this will allow rapid development and testing of on-node pre-processing algorithms.

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.