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.

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.

A widely accepted functional motor test for measuring basic mobility capabilities is the `Timed Up-and-Go' (TUG) test. Although several basic mobility tasks are included, only the total time is used as outcome parameter. It has been shown that timings of sub-phases can be used as relevant clinical parameters for the assessment of Parkinson's disease patients. A variety of systems and methods have been proposed for instrumenting the TUG test, but only limited information has been published regarding phase classification. In this paper an automated TUG phase classification methodology is proposed and validated in a study with 16 Parkinson's disease patients. Statistical, signal energy, chronological and gait features were extracted from acceleration and orientation signals of shoe mounted inertial measurement units. The phases `sit to walk', `walking', `first turn', `second turn' and `turn to sit' were segmented in a two stage classifier approach. Strides were used for a separation of the walking phase and classifiers like NaiveBayes, k-Nearest-Neighbor, Support Vector Machine (SVM) and Random Forest for the final phase segmentation. SVM performed best with a mean sensitivity of 81.80% over all phases. Additionally, the impact of UPDRS and Hoehn & Yahr ratings on the phase times was assessed. The proposed methodology could be used to analyze gait parameters of sub-phases like stride length, stride time, foot clearance, heel-strike or toe-off angle for an improved assessment of Parkinson's disease patients.

Motion analysis has become an important tool for athletes to improve their performance. However, most motion analysis systems are expensive and can only be used in a laboratory environment. Ambulatory motion analysis systems using inertial sensors would allow more flexible use, e.g. in a real training environment or even during competitions. This paper presents the calculation of the flexion-extension knee angle from segment acceleration and angular rates measured using body-worn inertial sensors. Using a functional calibration procedure, the sensors are first aligned without the need of an external camera system. An extended Kalman filter is used to estimate the relative orientations of thigh and shank, from which the knee angle is calculated. The algorithm was validated by comparing the computed knee angle to the output of a reference camera motion tracking system. In total seven subjects performed five dynamic motions: walking, jogging, running, jumps and squats. The averaged root mean squared error of the estimated knee angle was 8.2 ± 2.4 over all motions, with an average Pearson-correlation of 0.971 ± 0.020. In the future this will allow the analysis of joint angles during dynamic sports movements.

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