The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

Aim of this study was to investigate the feasibility of electrotactile feedback in closed loop training of force control during the routine grasping task. The feedback was provided using an array electrode and a simple six-level spatial coding, and the experiment was conducted in three amputee subjects. The psychometric tests confirmed that the subjects could perceive and interpret the electrotactile feedback with a high success rate. The subjects performed the routine grasping task comprising 4 blocks of 60 grasping trials. In each trial, the subjects employed feedforward control to close the hand and produce the desired grasping force (four levels). First (baseline) and the last (validation) session were performed in open loop, while the second and the third session (training) included electrotactile feedback. The obtained results confirmed that using the feedback improved the accuracy and precision of the force control. In addition, the subjects performed significantly better in the validation vs. baseline session, therefore suggesting that electrotactile feedback can be used for learning and training of myoelectric control.

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.

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.