Jens Barth

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.

Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients

A detailed and quantitative gait analysis can provide evidence of various gait impairments in elderly people. To provide an objective decision-making basis for gait analysis, simple applicable tests analyzing a high number of strides are required. A mobile gait analysis system, which is mounted on shoes, can fulfill these requirements. This paper presents a method for computing clinically relevant temporal and spatial gait parameters. Therefore, an accelerometer and a gyroscope were positioned laterally below each ankle joint. Temporal gait events were detected by searching for characteristic features in the signals. To calculate stride length, the gravity compensated accelerometer signal was double integrated, and sensor drift was modeled using a piece-wise defined linear function. The presented method was validated using GAITRite-based gait parameters from 101 patients (average age 82.1 years). Subjects performed a normal walking test with and without a wheeled walker. The parameters stride length and stride time showed a correlation of 0.93 and 0.95 between both systems. The absolute error of stride length was 6.26 cm on normal walking test. The developed system as well as the GAITRite showed an increased stride length, when using a four-wheeled walker as walking aid. However, the walking aid interfered with the automated analysis of the GAITRite system, but not with the inertial sensor-based approach. In summary, an algorithm for the calculation of clinically relevant gait parameters derived from inertial sensors is applicable in the diagnostic workup and also during long-term monitoring approaches in the elderly population.

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.

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.

Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease

Objective and rater independent analysis of movement impairment is one of the most challenging tasks in medical engineering. Especially assessment of motor symptoms defines the clinical diagnosis in Parkinsons disease (PD). A sensor-based system to measure the movement of the upper and lower extremities would therefore complement the clinical evaluation of PD. In this study two different sensor-based systems were combined to assess movement of 18 PD patients and 17 healthy controls. First, hand motor function was evaluated using a sensor pen with integrated accelerometers and pressure sensors, and second, gait function was assessed using a sports shoe with attached inertial sensors (gyroscopes, accelerometers). Subjects performed standardized tests for both extremities. Features were calculated from sensor signals to differentiate between patients and controls. For the latter, pattern recognition methods were used and the performance of four classifiers was compared. In a first step classification was done for every single system and in a second step for combined features of both systems. Combination of both motor task assessments substantially improved classification rates to 97% using the AdaBoost classifier for the experiment patients vs. controls. The combination of two different analysis systems led to enhanced, more stable, objective, and rater independent recognition of motor impairment. The method can be used as a complementary diagnostic tool for movement disorders.

Wearable sensors objectively measure gait parameters in Parkinson's disease

Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson’s disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson’s disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson’s disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects’ preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson’s disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson’s disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson’s disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson’s disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

Inertial sensor based and shoe size independent gait analysis including heel and toe clearance estimation

Falls are a major cause for morbidity and mortality in the ageing society. Inertial sensor based gait assessment including the analysis of the heel and toe clearance can be an indicator for the risk of falling. This paper presents a method for calculating the continuous heel and toe clearance without the knowledge of the shoe dimensions as well as the foot angle in the sagittal plane. These gait parameters were validated using an optical motion capture system. 20 healthy subjects from 3 different age groups (young, mid age, old) performed gait trials with different stride lengths and stride velocities. We obtained low mean absolute errors, low standard deviations and high Pearson correlations (0.91-0.99) for all gait parameters. In summary, we implemented a viable algorithm for the calculation of the heel and toe clearance without knowing the shoe dimensions as well as the foot angle in sagittal plane. We conclude that the given method is applicable for a mobile and unobtrusive gait assessment for healthy subjects from all age classes.

Mobile Stride Length Estimation With Deep Convolutional Neural Networks

Objective: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of stateof-the-art double integration approaches to gait patterns with a clear zero-velocity phase. Methods: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a 10-fold cross validation and for three different stride definitions. Results: Even though best results are achieved with strides defined from mid-stance to mid-stance with average accuracy and precision of 0.01±5.37 cm, performance does not strongly depend on stride definition. The achieved precision outperforms stateof-the-art methods evaluated on the same benchmark dataset by 3.0 cm (36%). Conclusion: Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. Significance: With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by re-training and applying the proposed method.

Mobile biometrische Ganganalyse

ZusammenfassungHintergrundDas idiopathische Parkinson-Syndrom (IPS) ist durch zunehmende motorische und nichtmotorische Symptome charakterisiert, die das jeweilige Krankheitsstadium definieren und unterschiedliche diagnostische und therapeutische Herausforderungen darstellen.Material und MethodenEs wurde eine mobile, rechnergestützte, biosensorische Ganganalyse an Patienten im frühen und mittleren Krankheitsstadium im Vergleich zu Kontrollen getestet. Standardisierte Ganguntersuchungen wurden mit am Schuh angebrachten Bewegungssensoren durchgeführt. Die erzeugten Beschleunigungs- und Drehsignale wurden mittels Verfahren der Mustererkennung analysiert.ErgebnisseEs konnte eine Unterscheidung zwischen Patienten und Kontrollen erzielt, Patienten in einem frühen Stadium identifiziert und eine krankheitsstadiumspezifische Zuordnung getroffen werden.SchlussfolgerungIm Rahmen dieser Studie zeigte sich, dass mobile, biometrische Ganganalysesysteme in der Lage sind, objektivierbare Aussagen über die Gangstörungen beim IPS zu treffen. Diese automatisierte Ganganalyse kann somit sowohl bei der Früherkennung des IPS als auch in der Verlaufsbeurteilung beim Auftreten von motorischen Fluktuationen in der Alltagsumgebung des Patienten hilfreich eingesetzt werden. Durch mobile Bewegungsanalysesysteme werden Patienten, Angehörige und Therapeuten bei der Beurteilung und Therapie von Gangstörungen optimal unterstützt.SummaryParkinson’s disease (PD) is characterized by progressive motor and non-motor symptoms, leading to distinct diagnostic and therapeutic challenges in all stages of the disease. This study investigated a mobile biosensor-based gait analysis system for patients in early and intermediate stages of PD compared to controls. Subjects wearing a motion sensor-equipped shoe performed a standardized gait exercise. Accelerometer- and gyroscope-based signals were analysed using a complex set of pattern recognition algorithms. The analysis was able (1) to distinguish between PD patients and controls, (2) to identify patients at an early stage of the disease and (3) to distinguish between early and intermediate stage patients. Thus, using this mobile biosensor-based analysis system we were able to obtain objective classifications of gait characteristics in PD. Future studies will show that mobile biosensor-based movement detection technology will support identification of early PD stages and allow objective characterization of motor fluctuations in advanced stages of the disease. This will provide an important and supportive tool for patients, caregivers and therapists.Parkinsons disease (PD) is characterized by progressive motor and non-motor symptoms, leading to distinct diagnostic and therapeutic challenges in all stages of the disease. This study investigated a mobile biosensor-based gait analysis system for patients in early and intermediate stages of PD compared to controls. Subjects wearing a motion sensor-equipped shoe performed a standardized gait exercise. Accelerometer- and gyroscope-based signals were analysed using a complex set of pattern recognition algorithms. The analysis was able (1) to distinguish between PD patients and controls, (2) to identify patients at an early stage of the disease and (3) to distinguish between early and intermediate stage patients. Thus, using this mobile biosensor-based analysis system we were able to obtain objective classifications of gait characteristics in PD. Future studies will show that mobile biosensor-based movement detection technology will support identification of early PD stages and allow objective characterization of motor fluctuations in advanced stages of the disease. This will provide an important and supportive tool for patients, caregivers and therapists.