Heiko Gassner

An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things

Current challenges demand a profound restructuration of the global healthcare system. A more efficient system is required to cope with the growing world population and increased life expectancy, which is associated with a marked prevalence of chronic neurological disorders such as Parkinsons disease (PD). One possible approach to meet this demand is a laterally distributed platform such as the Internet of Things (IoT). Real-time motion metrics in PD could be obtained virtually in any scenario by placing lightweight wearable sensors in the patients clothes and connecting them to a medical database through mobile devices such as cell phones or tablets. Technologies exist to collect huge amounts of patient data not only during regular medical visits but also at home during activities of daily life. These data could be fed into intelligent algorithms to first discriminate relevant threatening conditions, adjust medications based on online obtained physical deficits, and facilitate strategies to modify disease progression. A major impact of this approach lies in its efficiency, by maximizing resources and drastically improving the patient experience. The patient participates actively in disease management via combined objective device- and self-assessment and by sharing information within both medical and peer groups. Here, we review and discuss the existing wearable technologies and the Internet-of-Things concept applied to PD, with an emphasis on how this technological platform may lead to a shift in paradigm in terms of diagnostics and treatment.

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.

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.

MotionLab@Home: Complementary Measurement of Gait Characteristics Using Wearable Technology and Markerless Video Tracking - A Study Protocol

Background: While treatment monitoring of healthcare interventions is mainly conducted at the hospital, most information on a patient’s health status could be obtained from his everyday life activities. Therefore, there is a great interest in methods for long term (home) monitoring applications. However, there is still a lack of quantitative methods allowing everyday life activities monitoring such as gait analysis at home. While video-based systems have been employed given their high accuracy, they remain expensive and obtrusive with the range of motion of a patient limited to the set up measurement volume. Body worn sensors on the other hand present as an excellent opportunity to remotely and unobtrusively monitor gait characteristics not only at home, but during activities of everyday life. Technical limitations as well as changes in the patient’s gait patterns challenge the extraction of specific gait parameters. Methods: We propose a hybrid system comprising of a markerless video-based motion capturing system and a wearable sensor system with foot worn sensors. A study protocol is presented that will be used to validate the systems against each other. Discussion: This study will evaluate whether a markerless motion capturing system is feasible as a complementary tool for everyday gait analysis. Further, we will validate the accuracy of the sensor system using the video-based system as a gold standard. In the future, the combination might allow a recalibration of algorithmic sensor parameters based on deviations from the reference video-based system, and the combination of both modalities may enhance gait analysis in home monitoring.

F65 Mobile sensor-based gait analysis provides objective motor assessments in huntington’s disease

Background Gait disturbance plays an important role for quality of life in patients with Huntington’s disease (HD). Measuring gait parameters in patients with HD is essential for the objective assessment of motor impairments and potential beneficial effects of future treatments. Aims A mobile sensor-based gait analysis system was used to objectively assess specific features of gait in HD patients compared to healthy controls. Moreover, these measures were correlated to the clinical scores UDHRS Total Motor Score (TMS) and Total Functional Capacity (TFC). Methods 50 HD patients at two German sites were included in the study and received standardized clinical assessments during their annual ENROLL-HD visit. In addition, HD patients and a cohort of age- and gender matched healthy controls performed defined gait tests consisting of a 4 × 10 m walk, the 2-Minute-Walk-Test, and the Timed Up and Go Test (TUG). The mobile gait analysis system utilized inertial sensors attached to both shoes. Spatio-temporal gait parameters were calculated by machine learning algorithms. Results Specific gait parameters such as stride length and gait velocity were severely reduced, stride and stance time were significantly increased in patients with HD compared to healthy controls. Parameters describing gait variability were significantly altered in HD subjects and showed strong correlations to TMS and TFC. The objective gait measurements reflected disease stage according to TFC. In contrast, correlations of functional measures (e.g. TUG) were notably weaker. Conclusions Mobile gait analysis objectively supports the identification of specific features of motor impairment in HD for future clinical trials.

Quantifying postural instability in Parkinsonian gait from inertial sensor data during standardised clinical gait tests

Quantifying dynamic postural stability from inertial sensor data is clinically very relevant for treatment and therapy monitoring in neuromuscular diseases, e.g. Parkinsons disease (PD). We extract peak accelerations in movement direction during the loading phase and in vertical direction at ground contact from gravity-free acceleration signals captured at the patients feet as novel markers of dynamic postural stability. The approach is tested on a dataset containing 100 idiopathic PD patients and 50 age- and weight-matched healthy controls. Experiments include group separation of the controls and PD patients with/without postural instability as assessed by the pull test and analysis of correlations to existing parameters from inertial sensor data. Both markers show significant clinical differences, specifically between the two conditions in the PD group. At least one parameter provides complementary information to the existing set of spatio-temporal gait parameters while the other one correlates highly to gait velocity but might be measurable more precisely. In conclusion, the inertial sensor derived markers can detect postural instability but further research in this domain is needed.

Segmentation of gait sequences using inertial sensor data in hereditary spastic paraplegia

Gait analysis is an important tool for diagnosis, monitoring and treatment of neurological diseases. Among these are hereditary spastic paraplegias (HSPs) whose main characteristic is heterogeneous gait disturbance. So far HSP gait has been analysed in a limited number of studies, and within a laboratory set up only. Although the rarity of orphan diseases often limits larger scale studies, the investigation of these diseases is still important, not only to the affect population, but also for other diseases which share gait characteristics.