A new method for estimation of angles of leg segments and joints, which uses accelerometer arrays attached to body segments, is described. An array consists of two accelerometers mounted on a rigid rod. The absolute angle of each body segment was determined by band pass filtering of the differences between signals from parallel axes from two accelerometers mounted on the same rod. Joint angles were evaluated by subtracting absolute angles of the neighboring segments. This method eliminates the need for double integration as well as the drift typical for double integration. The efficiency of the algorithm is illustrated by experimental results involving healthy subjects who walked on a treadmill at various speeds, ranging between 0.15 m/s and 2.0 m/s. The validation was performed by comparing the estimated joint angles with the joint angles measured with flexible goniometers. The discrepancies were assessed by the differences between the two sets of data (obtained to be below 6 degrees) and by the Pearson correlation coefficient (greater than 0.97 for the knee angle and greater than 0.85 for the ankle angle).

We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a dominant axis. The method was verified on twelve subjects, who performed various tapping tasks, mimicking impaired patterns. The obtained tapping angles were compared with results of a motion capture camera system, demonstrating excellent accuracy. The root-mean-square (RMS) error between the two sets of data is, on average, below 4°, and the intraclass correlation coefficient is, on average, greater than 0.972. Data obtained by the proposed method may be used together with scores from clinical tests to enable a better diagnostic. Along with hardware simplicity, this makes the proposed method a promising candidate for use in clinical practice. Furthermore, our definition of the tapping angle can be applied to all tapping assessment systems.

Background: Gait disturbances are an integral part of clinical manifestations of Parkinson’s disease (PD), even in the initial stages of the disease. Our goal was to identify the set of spatio-temporal gait parameters that bear the highest relevance for characterizing de novo PD patients. Methods: Forty patients with de novo PD and forty healthy controls were recorded while walking over an electronic walkway in three different conditions: (1) base walking, (2) walking with an additional motor task, (3) walking with an additional mental task. Both groups were well balanced concerning age and gender. To select a smaller number of relevant parameters, affinity propagation clustering was applied on parameter pairwise correlation. The exemplars were then sorted by importance using the random forest algorithm. Classification accuracy of a support vector machine was tested using the selected parameters and compared to the accuracy of the model using a set of parameters derived from literature. Results: Final selection of parameters included: stride length and stride length coefficient of variation (CV), stride time and stride time CV, swing time and swing time CV, step time asymmetry, and heel-to-heel base support CV. Classification performed using these parameters showed higher overall accuracy (85%) than classification using the common parameter set containing: stride time, stride length, swing time and double support time, along with their CVs (78%). Conclusion: In early stages of PD, double support time and its CV appear to be weak indicators of the disease. We instead found step time asymmetry and support base CV to significantly contribute to classification accuracy.

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson’s disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.

Background/Aim. Finger tapping test is commonly used in neurological examinations as a test of motor performance. The new system comprising inertial and force sensors and custom proprietary software was developed for quantitative estimation and assessment of finger and foot tapping tests. The aim of this system was to provide diagnosis support and objective assessment of motor function. Methods. Miniature inertial sensors were placed on fingertips and used for measuring finger movements. A force sensor was placed on the fingertip of one finger, in order to measure the force during tapping. For foot tapping assessment, an inertial sensor was mounted on the subject?s foot, which was placed above a force platform. By using this system, various parameters such as a number of taps, tapping duration, rhythm, open and close speed, the applied force and tapping angle, can be extracted for detailed analysis of a patient?s motor performance. The system was tested on 13 patients with Parkinson?s disease and 14 healthy controls. Results. The system allowed easy measurement of listed parameters, and additional graphical representation showed quantitative differences in these parameters between neurological patient and healthy subjects. Conclusion. The novel system for finger and foot tapping test is compact, simple to use and efficiently collects patient data. Parameters measured in patients can be compared to those measured in healthy subjects, or among groups of patients, or used to monitor progress of the disease, or therapy effects. Created data and scores could be used together with the scores from clinical tests, providing the possibility for better insight into the diagnosis.

Background/Aim. Postural impairments and gait disorders in Parkinson's disease (PD) affect limits of stability, impaire postural adjustment, and evoke poor responses to perturbation. In the later stage of the disease, some patients can suffer from episodic features such as freezing of gait (FOG). Objective gait assessment and monitoring progress of the disease can give clinicians and therapist important information about changes in gait pattern and potential gait deviations, in order to prevent concomitant falls. The aim of this study was to propose a method for identification of freezing episodes and gait disturbances in patients with PD. A wireless inertial sensor system can be used to provide follow-up of the treatment effects or progress of the disease. Methods. The system is simple for mounting a subject, comfortable, simple for installing and recording, reliable and provides high-quality sensor data. A total of 12 patients were recorded and tested. Software calculates various gait parameters that could be estimated. User friendly visual tool provides information about changes in gait characteristics, either in a form of spectrogram or by observing spatiotemporal parameters. Based on these parameters, the algorithm performs classification of strides and identification of FOG types. Results. The described stride classification was merged with an algorithm for stride reconstruction resulting in a useful graphical tool that allows clinicians to inspect and analyze subject?s movements. Conclusion. The described gait assessment system can be used for detection and categorization of gait disturbances by applying rule-based classification based on stride length, stride time, and frequency of the shank segment movements. The method provides an valuable graphical interface which is easy to interpret and provides clinicians and therapists with valuable information regarding the temporal changes in gait.