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.

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.

Freezing of gait (FOG) can be assessed by clinical and instrumental methods. Clinical examination has the advantage of being available to most clinicians; however, it requires experience and may not reveal FOG even for cases confirmed by the medical history. Instrumental methods have an advantage in that they may be used for ambulatory monitoring. The aim of the present study was to describe and evaluate a new instrumental method based on a force sensitive resistor and Pearson's correlation coefficient (Pcc) for the assessment of FOG. Nine patients with Parkinson's disease in the "on" state walked through a corridor, passed through a doorway and made a U-turn. We analyzed 24 FOG episodes by computing the Pcc between one "regular/normal" step and the rest of the steps. The Pcc reached ±1 for "normal" locomotion, while correlation diminished due to the lack of periodicity during FOG episodes. Gait was assessed in parallel with video. FOG episodes determined from the video were all detected with the proposed method. The computed duration of the FOG episodes was compared with those estimated from the video. The method was sensitive to various types of freezing; although no differences due to different types of freezing were detected. The study showed that Pcc analysis permitted the computerized detection of FOG in a simple manner analogous to human visual judgment, and its automation may be useful in clinical practice to provide a record of the history of FOG.

In order to improve surface electromyography (sEMG) based control of hand prosthesis, we applied Principal Component Analysis (PCA) for feature extraction. The sEMG data from a group of healthy subjects (downloaded from free NINAPRO database) comprised the following sets: three grasping, eight wrist, and eleven finger movements. We tested the accuracy of a simple quadratic classifier for two sets of features derived from PCA. Preliminary results suggest that the first two principal components do not guarantee successful hand movement classification. The hand movement classification accuracy significantly increased with using three instead of two features, in all three sets of movements and throughout all subjects.

We propose a novel system for measuring finger force profiles for dexterity assessment. The system consists of a software application and encased hardware. The sensing part of the system consists of ten high sensitivity strain gage force sensors, one for each finger. The developed application controls turning ON/OFF of the LEDs (each LED corresponds to one finger) to signalize the user which finger should press the corresponding strain gage force sensor. Linearity, repeatability, and sensitivity to position pressure of the strain gage force sensors were tested to prove the system reliability. The potential usage of the system with the assessment of quantitative parameters was demonstrated on two healthy subjects.